By GManNickG

2012-06-27 13:51:36 8 Comments

Here is a piece of C++ code that shows some very peculiar behavior. For some strange reason, sorting the data miraculously makes the code almost six times faster:

#include <algorithm>
#include <ctime>
#include <iostream>

int main()
    // Generate data
    const unsigned arraySize = 32768;
    int data[arraySize];

    for (unsigned c = 0; c < arraySize; ++c)
        data[c] = std::rand() % 256;

    // !!! With this, the next loop runs faster.
    std::sort(data, data + arraySize);

    // Test
    clock_t start = clock();
    long long sum = 0;

    for (unsigned i = 0; i < 100000; ++i)
        // Primary loop
        for (unsigned c = 0; c < arraySize; ++c)
            if (data[c] >= 128)
                sum += data[c];

    double elapsedTime = static_cast<double>(clock() - start) / CLOCKS_PER_SEC;

    std::cout << elapsedTime << std::endl;
    std::cout << "sum = " << sum << std::endl;
  • Without std::sort(data, data + arraySize);, the code runs in 11.54 seconds.
  • With the sorted data, the code runs in 1.93 seconds.

Initially, I thought this might be just a language or compiler anomaly, so I tried Java:

import java.util.Arrays;
import java.util.Random;

public class Main
    public static void main(String[] args)
        // Generate data
        int arraySize = 32768;
        int data[] = new int[arraySize];

        Random rnd = new Random(0);
        for (int c = 0; c < arraySize; ++c)
            data[c] = rnd.nextInt() % 256;

        // !!! With this, the next loop runs faster

        // Test
        long start = System.nanoTime();
        long sum = 0;

        for (int i = 0; i < 100000; ++i)
            // Primary loop
            for (int c = 0; c < arraySize; ++c)
                if (data[c] >= 128)
                    sum += data[c];

        System.out.println((System.nanoTime() - start) / 1000000000.0);
        System.out.println("sum = " + sum);

With a similar but less extreme result.

My first thought was that sorting brings the data into the cache, but then I thought how silly that was because the array was just generated.

  • What is going on?
  • Why is processing a sorted array faster than processing an unsorted array?

The code is summing up some independent terms, so the order should not matter.


@Mysticial 2012-06-27 13:56:42

You are a victim of branch prediction fail.

What is Branch Prediction?

Consider a railroad junction:

Image showing a railroad junction Image by Mecanismo, via Wikimedia Commons. Used under the CC-By-SA 3.0 license.

Now for the sake of argument, suppose this is back in the 1800s - before long distance or radio communication.

You are the operator of a junction and you hear a train coming. You have no idea which way it is supposed to go. You stop the train to ask the driver which direction they want. And then you set the switch appropriately.

Trains are heavy and have a lot of inertia. So they take forever to start up and slow down.

Is there a better way? You guess which direction the train will go!

  • If you guessed right, it continues on.
  • If you guessed wrong, the captain will stop, back up, and yell at you to flip the switch. Then it can restart down the other path.

If you guess right every time, the train will never have to stop.
If you guess wrong too often, the train will spend a lot of time stopping, backing up, and restarting.

Consider an if-statement: At the processor level, it is a branch instruction:

Screenshot of compiled code containing an if statement

You are a processor and you see a branch. You have no idea which way it will go. What do you do? You halt execution and wait until the previous instructions are complete. Then you continue down the correct path.

Modern processors are complicated and have long pipelines. So they take forever to "warm up" and "slow down".

Is there a better way? You guess which direction the branch will go!

  • If you guessed right, you continue executing.
  • If you guessed wrong, you need to flush the pipeline and roll back to the branch. Then you can restart down the other path.

If you guess right every time, the execution will never have to stop.
If you guess wrong too often, you spend a lot of time stalling, rolling back, and restarting.

This is branch prediction. I admit it's not the best analogy since the train could just signal the direction with a flag. But in computers, the processor doesn't know which direction a branch will go until the last moment.

So how would you strategically guess to minimize the number of times that the train must back up and go down the other path? You look at the past history! If the train goes left 99% of the time, then you guess left. If it alternates, then you alternate your guesses. If it goes one way every three times, you guess the same...

In other words, you try to identify a pattern and follow it. This is more or less how branch predictors work.

Most applications have well-behaved branches. So modern branch predictors will typically achieve >90% hit rates. But when faced with unpredictable branches with no recognizable patterns, branch predictors are virtually useless.

Further reading: "Branch predictor" article on Wikipedia.

As hinted from above, the culprit is this if-statement:

if (data[c] >= 128)
    sum += data[c];

Notice that the data is evenly distributed between 0 and 255. When the data is sorted, roughly the first half of the iterations will not enter the if-statement. After that, they will all enter the if-statement.

This is very friendly to the branch predictor since the branch consecutively goes the same direction many times. Even a simple saturating counter will correctly predict the branch except for the few iterations after it switches direction.

Quick visualization:

T = branch taken
N = branch not taken

data[] = 0, 1, 2, 3, 4, ... 126, 127, 128, 129, 130, ... 250, 251, 252, ...
branch = N  N  N  N  N  ...   N    N    T    T    T  ...   T    T    T  ...

       = NNNNNNNNNNNN ... NNNNNNNTTTTTTTTT ... TTTTTTTTTT  (easy to predict)

However, when the data is completely random, the branch predictor is rendered useless, because it can't predict random data. Thus there will probably be around 50% misprediction (no better than random guessing).

data[] = 226, 185, 125, 158, 198, 144, 217, 79, 202, 118,  14, 150, 177, 182, 133, ...
branch =   T,   T,   N,   T,   T,   T,   T,  N,   T,   N,   N,   T,   T,   T,   N  ...

       = TTNTTTTNTNNTTTN ...   (completely random - hard to predict)

So what can be done?

If the compiler isn't able to optimize the branch into a conditional move, you can try some hacks if you are willing to sacrifice readability for performance.


if (data[c] >= 128)
    sum += data[c];


int t = (data[c] - 128) >> 31;
sum += ~t & data[c];

This eliminates the branch and replaces it with some bitwise operations.

(Note that this hack is not strictly equivalent to the original if-statement. But in this case, it's valid for all the input values of data[].)

Benchmarks: Core i7 920 @ 3.5 GHz

C++ - Visual Studio 2010 - x64 Release

//  Branch - Random
seconds = 11.777

//  Branch - Sorted
seconds = 2.352

//  Branchless - Random
seconds = 2.564

//  Branchless - Sorted
seconds = 2.587

Java - NetBeans 7.1.1 JDK 7 - x64

//  Branch - Random
seconds = 10.93293813

//  Branch - Sorted
seconds = 5.643797077

//  Branchless - Random
seconds = 3.113581453

//  Branchless - Sorted
seconds = 3.186068823


  • With the Branch: There is a huge difference between the sorted and unsorted data.
  • With the Hack: There is no difference between sorted and unsorted data.
  • In the C++ case, the hack is actually a tad slower than with the branch when the data is sorted.

A general rule of thumb is to avoid data-dependent branching in critical loops (such as in this example).


  • GCC 4.6.1 with -O3 or -ftree-vectorize on x64 is able to generate a conditional move. So there is no difference between the sorted and unsorted data - both are fast.

    (Or somewhat fast: for the already-sorted case, cmov can be slower especially if GCC puts it on the critical path instead of just add, especially on Intel before Broadwell where cmov has 2 cycle latency: gcc optimization flag -O3 makes code slower than -O2)

  • VC++ 2010 is unable to generate conditional moves for this branch even under /Ox.

  • Intel C++ Compiler (ICC) 11 does something miraculous. It interchanges the two loops, thereby hoisting the unpredictable branch to the outer loop. So not only is it immune to the mispredictions, it is also twice as fast as whatever VC++ and GCC can generate! In other words, ICC took advantage of the test-loop to defeat the benchmark...

  • If you give the Intel compiler the branchless code, it just out-right vectorizes it... and is just as fast as with the branch (with the loop interchange).

This goes to show that even mature modern compilers can vary wildly in their ability to optimize code...

@Mysticial 2012-07-10 17:08:39

@phonetagger Take a look at this followup question:… The Intel Compiler came pretty close to completely getting rid of the outer loop.

@onmyway133 2013-08-06 09:07:38

@Mysticial How does the train/compiler know that it has enter the wrong path ?

@Phil Miller 2013-08-18 20:57:43

Under the modern C and C++ standards, the shifting hack is actually not implementation-defined behavior, but undefined behavior! Shifting a 1 in or out of the sign bit of a signed integer is no longer permissible.

@Mysticial 2013-08-18 21:04:38

@Novelocrat Only half of that is correct. Shifting a 1 into the sign-bit when it is zero is indeed UB. That's because it's signed integer overflow. But shifting a 1 out of the sign-bit is IB. Right-shifting a negative signed integer is IB. You can go into the argument that that C/C++ doesn't require that the top bit be the sign indicator. But implementation details are IB.

@René Link 2014-01-24 10:15:57

@Mystical readability of the 'hack' can be increased by introducing a method. E.g. in java private int sumIfGreaterThan128(int curSum, int value). The JIT compiler will inline it at runtime anyway. I guess in other languages there are equal optimizations available.

@Mysticial 2014-03-08 19:53:33

@Unheilig Using bitwise operations for anything other than legitimate bit manipulation or multiplying/dividing by a variable power-of-two is not something I usually recommend since it's often obfuscating. Nevertheless, here's a good reference for bit twiddling hacks:

@Unheilig 2014-03-08 20:05:22

@Mysticial Thanks so much for the link. It looks promising. I will go though it. One last request. Sorry, but please don't mind, could you tell me how you could do this int t = (data[c] - 128) >> 31; sum += ~t & data[c]; to replace the original if-condition above?

@Andon M. Coleman 2014-04-19 12:17:56

@obe: Given hierarchical memory structures, it's impossible to say what the expense of a cache miss will be. It might miss in L1 and be resolved in slower L2, or miss in L3 and be resolved in system memory. However, unless for some bizarre reason this cache miss causes memory in a non-resident page to be loaded from disk, you have a good point... memory hasn't had access time in the range of milliseconds in about 25-30 years ;)

@woojoo666 2014-06-19 23:33:38

Would the conditional be faster than the hack if there was no branch prediction? The conditional would be (check) (jump) (add), whereas the hack uses 4 consecutive arithmetic operations

@Mysticial 2014-06-19 23:45:57

@woojoo666 That would depend on how expensive the 4 operations are with respect to the branch handling logic. So it'll probably vary on a case-by-case basis.

@naveejr 2014-07-08 05:33:58

Isn't it possible to execute both branches in parallel and later stop execution of one wrong branch, Instead of predicting one branch?

@matec 2014-09-27 19:23:09

@Tuğrul I would assume that for a given particle the chance that it collides with another particle is less than 1%. Then the branch prediction could just always predict no collision and would be >99% correct, whether sorted or not. In the end for fast collision check you'll want to use a tree structure anyways.

@Code Geek 2014-11-07 17:52:11

I wonder why is the branch prediction concept in place, what is the use of the (Branch Prediction)concept ? I mean without it we would get definite result with the sorted and unsorted array.

@Filip Bartuzi 2014-11-09 13:37:58

When does branch prediction takes place? When does language will know that array is sorted? I'm thinking of situation of array that looks like: [1,2,3,4,5,...998,999,1000, 3, 10001, 10002] ? will this obscure 3 increase running time? Will it be as long as unsorted array?

@Mysticial 2014-11-09 17:49:30

@FilipBartuzi Branch prediction happens in the processor while it is running the code. The language doesn't know anything. In your example, it will still be fast because you're only adding 1 or 2 mispredictions on and around the 3.

@Tomas Andrle 2014-11-25 15:32:24

@BJHomer I tried it with Clang 3.5: debug sorted 9.3s, debug unsorted 24.6s. O2 sorted 5.0s, O2 unsorted also 5.0s. So it seems like Clang is able to optimize the loop well. The branchless version from the post took 13s for debug, 4.1s for O2, with almost no difference between sorted/unsorted.

@Slipp D. Thompson 2015-01-17 22:54:28

This may be out of scope of this Q/A, but are there modern processors that will continue down both paths a short ways when the branch prediction reports similar odds for each branch? If not, why not? It would seem that spare cycles gone into ensuring the branch is ready in time is better than guessing incorrectly or just waiting around.

@Mysticial 2015-01-18 01:19:02

@SlippD.Thompson Probably not for the reasons I mentioned in an earlier comment..

@Lutz Prechelt 2015-02-01 15:25:30

Rule of thumb for writing code that is efficient on a modern processor: Everything that makes the execution of your program more regular (less uneven) will tend to make it more efficient. The sort in this example has this effect because of branch prediction. Access locality (rather than far-and-wide random accesses) has this effect because of caches.

@jdero 2015-06-27 11:35:58

The grammar in me wants me to think this should read "... victim of branch prediction failure" rather than just "... victim of branch prediction fail."

@TheGuyWithTheFace 2015-06-29 14:42:07

So in Java's case, for example, does the branch prediction take place on the processor level or in the java runtime?

@Ismael Luceno 2015-07-11 09:19:53

GCC has a lot of optimizations that aren't enabled by default, among the things it can do:

@Ismael Luceno 2015-07-11 09:27:15

it can split loops (with -ftree-loop-distribution and -ftree-loop-distribute-patterns), move invariant sections (by default), move invariant conditions out of the loops (with -funswitch-loops, but causes duplication), convert conditional jumps to conditional stores or remove them (-ftree-loop-if-convert and -ftree-loop-if-convert-stores). Sadly a lot of these options have unsafe side effects, and only do any good improvement to very naively written code.

@Renato Oliveira 2015-10-13 11:08:30

There is another fact here. The data and temporal locality. When you access the same position many times subsequently, its value still in the registers, so this is why the loop interchange doubles the performance over the original sorted version.

@Sandeep 2015-12-06 11:43:13

@Mysticial, Is this answer still valid given the processor changes now in 2015/6?

@Mysticial 2015-12-06 16:31:18

@Sandeep Yes. Processors still have branch prediction. If anything has changed, it's the compilers. Nowadays, I bet they're more likely to do what ICC and GCC (under -O3) did here - that is, remove the branch. Given how high profile this question is, it's very possible that compilers have been updated to specifically handle the case in this question. The definitely pay attention to SO. And it happened on this question where GCC was updated within 3 weeks. I don't see why it wouldn't happen here as well.

@Marian Spanik 2015-12-09 17:46:57

I just ran the code in VS 2015 and the sorting doesn't improve performance anymore. I measured about 1.1s for 32768 elements and about 11s for 327680 elements (moved to a global variable to prevent a stack overflow) with my Intel Core I5 with 3.6GHz thanks to TurboBoost. I looked to a disassembly and I didn't find any branch except one for terminating the loop - it actually uses instructions like cdq and movlpd which are normally used for floating point operations.

@doug65536 2016-04-22 12:33:50

@One-One The prediction is done at the beginning of the pipeline, the operation flows through the pipeline and reaches a waiting area for its operands. Once its operands are available, the instruction executes and the instruction becomes eligible for retirement (commit to actual state). The retirement of the branch checks whether the guess was correct. If not, flush pipeline and restart instruction fetcher at correct instruction. If prediction was correct, just keep going. By this time, many instructions after the predicted branch have started but have not been committed to state.

@chaws 2016-05-13 20:18:51

Complementing... you can also use something like 'sum += (data[i] > 128) * data[i];', which is still branchless but same accuracy as the one with the branch.

@Nicholas Hamilton 2016-05-19 23:39:34

What about the ternary operator? sum += data[i] > 128 ? data[i] : 0

@Mysticial 2016-05-19 23:44:31

@NicholasHamilton The ternary operator is a branch. Though some compilers (namely MSVC) seem to be better at optimizing them than plain-old if-statements.

@Malus Jan 2016-06-05 21:38:38

Really good explication. If you add this code it become much more faster: for (register unsigned i = 0; i < 100000; ++i) { // Primary loop for (register unsigned c = 0; c < arraySize; ++c) { register int t=(data[c]<<25)>>31; sum += ~t & data[c]; } }

@Atul 2016-07-14 14:48:40

@Mysticial: Thanks for such a nice insight. After going through this came to my mind couple of questions: identify a pattern and follow it this sounds more of artificial intelligence. So is it safe to say modern compilers are equipped with AI algorithms for branch prediction? halt execution and wait until the previous instructions are complete that sounds to me of multiple threads. Does CPU internally breaks code blocks into threads? (One your comment also mentions processor is executing many instructions at the same time). If yes, what role does compiler play?

@Mysticial 2016-07-14 14:55:21

@Atul The Wikipedia article on branch prediction has examples of some branch prediction algorithms. Whether or not you want to call them "AIs" is up to you. For your other question about multiple instructions at the same time, it's called Superscalar Execution.

@Atul 2016-07-15 02:35:58

@Mysticial: Thanks! I took some time to go through article(s). What I gather is, branch prediction is more about processor architecture, no matter what compiler we use (unlike what I was under impression)

@Black 2016-07-21 06:31:32

I still fail to understand why the compiler has to even predict?! Why does it not just compare the two values with each other and decides then?!

@Mysticial 2016-07-21 15:09:50

@EdwardBlack It's not the compiler that does the prediction. It's the processor. Secondly, the processor can't "just decide" because it's doing many things at the same time. To oversimplify things a bit, while the processor is executing the current instruction, it's already reading ahead by 20+ instructions and preparing them for execution. If you have a branch, the processor needs to decide which side to do this "read ahead". When there's a misprediction, everything that has been "read and prepared ahead of time" needs to be thrown away and restarted on the other side.

@Mysticial 2016-07-21 15:14:47

@EdwardBlack An appropriate analogy for this "read ahead" thing would be airline flight scheduling. Flight schedules are made months ahead of time. But when something unexpected happens (like a storm shutdowns down a major hub), flights get canceled and that schedule goes up in smoke. The result? Massive delays that propagate through to many flights that don't even touch that airport. Of course for a processor, the "pre-planning" window is on the order of nano-seconds and up to a few hundred instructions rather than thousands of flights that cover months.

@TimK 2016-08-09 19:25:41

This is a good answer but it should be made clear that it is processor branch prediction that is causing this behaviour. Compilers predict branches too.

@Riley 2016-09-20 15:02:10

As a side note, there has been some research done where branch predictors were able to "figure out the pattern of rand()". They had a branch that was basically if( rand.nextInt(100) < 50 ) and correctly predicted something like 99% of the time.

@Riley 2016-09-20 15:08:31

There were a few questions as to why the processor doesn't just execute both branches. One reason is that processors can have as many as 20 predictions in-flight (guessed but not resolved yet). To handle that many you'd need to be executing 2^20 (1048576) different paths at the same time.

@Mysticial 2016-09-20 15:56:11

@Riley I actually did answer the comments about executing both branches. But it seems that my comment got deleted for some reason.

@Riley 2016-09-20 15:57:33

@Mysticial Was your answer basically the same or is there another reason?

@Mysticial 2016-09-20 16:02:41

@Riley Same. Exponential resource growth and the fact that prediction is often very accurate.

@Mysticial 2016-10-29 23:42:43

@Adjit There's no simple answer to that. For the particular case here, GCC and ICC can do conditional moves, but VS can't. But that will change depending on the code. The example in this question is optimizeable to a conditional move. But not always. Only really simple branches can be optimized this way.

@Mysticial 2016-10-31 15:11:25

@Adjit No, that's way too specific. What to take away is that anything that causes the flow of execution to conditionally change is subject to a performance penalty due to branch misprediction. This includes if-statements, loop-conditions, switches, ternary operators, short-circuiting boolean logic, calls to function pointers, calls to lambdas, calls to virtual/polymorphic methods, etc... (The last 3 of these aren't related to branch-prediction per se, but the same concept applies in that the processor doesn't "know where to go" next.)

@Zze 2016-11-15 02:20:13

@Mysticial is there a particular reason you opted for your implementation (declaring an int) over sum += ~((data[c] - 128) >> 31) & data[c]; ?

@Mysticial 2016-11-15 05:57:48

@Zze No. That was just the way it turned out when I derived it. The variable being the mask that either enables the value, or sets it to zero.

@elfan 2016-11-16 22:36:35

@mysticial Does it mean that it is possible that the result of the program is incorrect because of the prediction? How does it know/validate whether the guessing is correct? Wouldn't that still need to actually compute everything to know the correct result for validation?

@Mysticial 2016-11-16 23:37:10

@elfan No. Branch prediction does not affect correctness. When the processor runs a set of instructions, it must behave "as if" it were running them line-by-line. It can play tricks underneath to make things faster (such as branch prediction), but in the end, it still must respect the program as it is written. For your other question, the processor will know when a prediction is correct once the branch instruction executes and determines which way it's supposed to go.

@elfan 2016-11-17 03:02:43

@Mysticial, does it mean in this case the code will complete in around 2 seconds (instead of 12) if there is no branch prediction, and that the extra 10 seconds was because of the overhead of wrong predictions (the train going back)?

@Mysticial 2016-11-17 03:59:34

@elfan No. As a hypothetical example: If there was no prediction, it would always take 10 seconds. With good prediction, it would be 2 seconds. With consistently bad prediction, it would be 12 seconds. The extra 2 seconds being the overhead of going back. In most cases, it will be closer to the 2 seconds, so it's a net win.

@elfan 2016-11-17 06:37:32

I see, so the 8 seconds was the overhead to stop the train and ask the driver every time. If there was no branch at all, and no branch prediction, then the code will always take 2 seconds (i.e. similar to the case that the prediction is always correct in a system with branch prediction). Thanks for your great explanation @Mysticial

@Mohit Thakur 2016-11-29 06:00:28

More simplify way for: int t = (data[c] - 128) >> 31; sum += ~t & data[c]; is char t = data[c] >> 7 ; /* Truncating the 7 bits (equivalent to data[c] >=128 ) */ sum += -t & data[c]; /* -t will be equivalent to -1 if data[c] >= 128*/.

@njzk2 2016-12-02 19:43:03

How does this influence (if at all) the evaluation of the complexity of an algorithm?

@Snaipe 2016-12-07 20:20:25

@njzk2 it doesn't. Complexity is the asymptotical behaviour of the time (or size) of a function when its inputs tends to infinity. Branch prediction failures doesn't change how the curve behaves at infinity, it only adds some constant computation time to the branch operation.

@AMDG 2017-02-06 00:22:40

I question the efficacy of human minds when it comes to unnecessary overhead such as branch prediction. I just read up on the Wikipedia article—what smart person invented this so I can smack him upside? Just do two concurrent speculative executions for both branches and then discard the "wrong" branch's speculative execution.

@Mysticial 2017-02-06 19:24:41

@LinkTheProgrammer I'm not so sure the solution is that simple. Because if it was, they'd probably be doing that already. The main problem I see is that modern CPUs will be predicting multiple branches ahead. And that leads to an exponential state explosion if you want to follow them all. The other thing is that running down multiple paths means you'll be wasting a lot execution resources on stuff that will be thrown away. And that probably has implications for power consumption as well.

@AMDG 2017-02-06 21:43:12

@Mysticial perhaps, but I'm thinking pure latency here. We can worry about power consumption when my $200 laptop stops lagging and people start writing actually efficient code.

@k3a 2017-03-10 20:10:12

There is also __builtin_expect in GCC to help the compiler. See…

@TheLoneJoker 2017-09-18 01:46:52

sum += ~t & data[c]; The above will be wrong since a & operation will change the bits. What you want is as below a multiplication operation: sum += (~t * data[c]);

@DDD 2017-10-02 16:55:59

I would like to know what patterns can i use to achieve this? Is there anything i could do without properly planning all the time on these small array operations so i can just write code in a fashion that suits for the computer? Is it better to sort the array all the time before an operation? I know its sort of pointless at this level but many things add up very quickly. Also does the same thing applies in other programming languages? Or is it c++ specific?

@Mysticial 2017-10-02 17:16:05

@Azarus I think you're among a sizable # of people who are getting confused by the sorting aspect. Sorting has nothing to do with branch prediction itself. It just happens to expose the effect of branch prediction in the specific example of this question. So don't go around blindly sorting things thinking it will magically solve everything. Not all illnesses are treated with antibiotics. You need to understand the problem first before you can apply the solution. Otherwise you may end up doing more harm than good. And before you even get to that, the rules of premature optimization still apply.

@DDD 2017-10-02 17:20:14

@mystical i am aware of that would be indeed really stupid to sort arrays all the time. The computer would run into branch prediction all the time. But my question was more about what can we use in practice to avoid or reduce its performance issues if theres any in the first place? Is there anything we can do about it? Or its not worth the effort and situations where it really matters are rare?

@Mysticial 2017-10-02 17:53:38

@Azarus If you're looking for a "best practice", it's in the answer already - "A general rule of thumb is to avoid data-dependent branching in critical loops." But I can't speak for whether it's worth the effort since it depends on the requirements of the application and the available resources to do it.

@M.kazem Akhgary 2017-10-11 14:10:10

ok, I see your benchmarks, but whole branch prediction thing doesn't provide much performance. it introduces more problems than solving it. then why cpu manufactures even bothered integrating such thing in it?

@Mysticial 2017-10-11 15:27:22

@M.kazemAkhgary Because branch prediction helps in >95% of the cases in real life. The example in this question happens to be that remaining 5%. And even in that 5%, the penalty is minimal compared to not predicting at all and always stalling.

@chqrlie 2017-10-19 11:16:34

I'm afraid your proposed optimisation is flawed: int t = (data[c] - 128) >> 31; has implementation defined behavior: data[c] has int type, so data[c] - 128 will be negative for values less than 128. Right shifting a negative value has implementation defined behavior. You can fix this for 2's complement architectures with a simple expression: sum += -(data[c] >= 128) & data[c]; for which many compilers will produce code without jumps. Alternatively, given the range of data[c]: sum += -(data[c] >> 7) & data[c];

@Mysticial 2017-10-19 15:50:30

@chqrlie When you get to this level of optimization, implementation-defined behavior is often an acceptable trade-off. Especially since sign-fill right-shift is essentially universal for 2's complement architectures. The real offender here is assuming a 32-bit int. It's been a while, but I probably didn't use a solution with a comparison because I had experiences with compilers generating branches for bool -> int conversions. You also can't do that in Java. Anyway, that was 5 years ago. I rarely do these hacks anymore since I prefer the SIMD intrinsic route.

@Mysticial 2017-10-19 17:56:13

@M.kazemAkhgary Short answer is that each core has multiple pipelines. Long answer is that it's a loaded question. Try searching for "superscalar processor". Modern processors can execute around 4 instructions/cycle.

@Luke Hutchison 2017-12-22 12:37:02

On ARM, a (short) conditional is a zero-cost operation, because every instruction has a 4-bit conditional field (allowing for 16 different types of conditions on any instruction), so branching can be completely avoided in many cases. (In this case, just do the comparison, then conditionally accumulate). So on ARM, the runtime will be longer for sorted than for unsorted, because sorting requires extra work.

@Timothé Malahieude 2018-01-05 10:42:05

Note that this optimization is precisely the cause of Spectre and Meltdown big security vulnerabilities. In short, some operations like caching are not actually rollbacked (for performance reasons), which cause some potentially sensitive data to become readable by other processes.

@Evusas 2018-02-28 08:35:16

Let me clarify one thing about your analogy with trains: train suffers by making a bad choice, because it needs to get back to the branching point on failure (I consider that it's going to be the wrong choice as you see). Does the branch prediction suffer from a bad choice compared to not making any choice and rather waiting? Or is the branch prediction in CPUs beneficial in both cases (making train analogy imprecise)?

@Mysticial 2018-02-28 19:12:04

@Evusas I'm not a hardware designer, so I don't know the answer for sure. But the rollback logic certainly isn't free. Even if the CPU designers managed to completely hide the performance impact of a misprediction roll-back, there are still costs in terms of power consumption of the wasted computation. Today's chips are very power optimized and will vary their clock speeds to stay under a power limit. So it's certainly within the realm of possibility that excessive waste of power from mispredictions can indirectly hurt performance.

@Sergey.quixoticaxis.Ivanov 2018-04-10 18:32:19

@TimothéMalahieude or maybe overly optimistic guarantees of OS vendors on process isolation are the cause. Some OSes that do not use the concept of process don't care at all

@naviram 2018-09-16 00:12:12

@Mystical seems to me that the brach pridiction decreases the elapse time that the execution takes but not less CPU time/work is actually done cause the condition need to be checked either way, just there are less CPU idle times. Then one can say that if you make milion different jobs, the total time to do all of the will be similar with and without branch pridiction. so it optimizes perfomance but not throughput. is this right?

@Mysticial 2018-09-16 00:44:43

@naviram No it isn't. A CPU's resources can't be redistributed like that. Most of it is use it or lose it. If the CPU is stuck waiting for a branch to unwind (among many other things), its computing resources remain idle. Features like hyperthreading will allow some amount of sharing. But not to the level that you describe where everything is always utilized no matter what happens. Also, a branch misprediction involves wasted resources. So in the case you describe where throughput matters and everything can be prefectly redistributed, branch prediction would be actively harmful.

@lesurp 2019-05-16 13:27:37

I haven't read every comment but while the answer's explanation is good, a simpler (and faster!) version for this specific task is sum += data[c] * (data[c] >= 128);. It is better than the posted answer even in unoptimized builds (in optimized builds, even the naive version is faster than the posted answer actually, a good reminder that micro-optimizing is bad). A comparison doesn't mean a branch. Compiler is clang 9.0.

@user541686 2019-07-24 02:38:58

@Mysticial: You might want to qualify the "avoid data-dependent branching in critical loops" a bit, given it's really only valid when the conditional block performs very little work? A little worried someone will take it literally and make some expensive statement unconditional...

@tony _008 2019-08-14 17:43:52

this is one of the better answers I've seen on stack overflow ever.

@Kyle Delaney 2019-10-14 18:44:55

Can the train track analogy apply to junctions in the processor's actual electrical circuits?

@Saint-Martin 2020-01-10 21:47:44

Is there a definition of a "predict friendly" code available ? Are the following statements "predict equivalent" ? Or is first statement more "predict friendly" than second ? why ? if (a && b)... if (a) if (b)...

@hatirlatici 2019-10-23 21:35:40

This question is rooted in Branch Prediction Models on CPUs. I'd recommend reading this paper:

Increasing the Instruction Fetch Rate via Multiple Branch Prediction and a Branch Address Cache

When you have sorted elements, IR could not be bothered to fetch all CPU instructions, again and again, It fetches them from cache.

@Peter Cordes 2019-12-13 14:29:22

The instructions stay hot in the CPU's L1 instruction cache regardless of mispredicts. The problem is fetching them into the pipeline in the right order, before the immediately-previous instructions have decoded and finished executing.

@Selcuk 2019-10-14 23:34:40

Bjarne Stroustrup's Answer to this question:

That sounds like an interview question. Is it true? How would you know? It is a bad idea to answer questions about efficiency without first doing some measurements, so it is important to know how to measure.

So, I tried with a vector of a million integers and got:

Already sorted    32995 milliseconds
Shuffled          125944 milliseconds

Already sorted    18610 milliseconds
Shuffled          133304 milliseconds

Already sorted    17942 milliseconds
Shuffled          107858 milliseconds

I ran that a few times to be sure. Yes, the phenomenon is real. My key code was:

void run(vector<int>& v, const string& label)
    auto t0 = system_clock::now();
    sort(v.begin(), v.end());
    auto t1 = system_clock::now();
    cout << label 
         << duration_cast<microseconds>(t1 — t0).count() 
         << " milliseconds\n";

void tst()
    vector<int> v(1'000'000);
    iota(v.begin(), v.end(), 0);
    run(v, "already sorted ");
    std::shuffle(v.begin(), v.end(), std::mt19937{ std::random_device{}() });
    run(v, "shuffled    ");

At least the phenomenon is real with this compiler, standard library, and optimizer settings. Different implementations can and do give different answers. In fact, someone did do a more systematic study (a quick web search will find it) and most implementations show that effect.

One reason is branch prediction: the key operation in the sort algorithm is “if(v[i] < pivot]) …” or equivalent. For a sorted sequence that test is always true whereas, for a random sequence, the branch chosen varies randomly.

Another reason is that when the vector is already sorted, we never need to move elements to their correct position. The effect of these little details is the factor of five or six that we saw.

Quicksort (and sorting in general) is a complex study that has attracted some of the greatest minds of computer science. A good sort function is a result of both choosing a good algorithm and paying attention to hardware performance in its implementation.

If you want to write efficient code, you need to know a bit about machine architecture.

@user2297550 2018-12-09 06:18:17

The assumption by other answers that one needs to sort the data is not correct.

The following code does not sort the entire array, but only 200-element segments of it, and thereby runs the fastest.

Sorting only k-element sections completes the pre-processing in linear time, O(n), rather than the O(n.log(n)) time needed to sort the entire array.

#include <algorithm>
#include <ctime>
#include <iostream>

int main() {
    int data[32768]; const int l = sizeof data / sizeof data[0];

    for (unsigned c = 0; c < l; ++c)
        data[c] = std::rand() % 256;

    // sort 200-element segments, not the whole array
    for (unsigned c = 0; c + 200 <= l; c += 200)
        std::sort(&data[c], &data[c + 200]);

    clock_t start = clock();
    long long sum = 0;

    for (unsigned i = 0; i < 100000; ++i) {
        for (unsigned c = 0; c < sizeof data / sizeof(int); ++c) {
            if (data[c] >= 128)
                sum += data[c];

    std::cout << static_cast<double>(clock() - start) / CLOCKS_PER_SEC << std::endl;
    std::cout << "sum = " << sum << std::endl;

This also "proves" that it has nothing to do with any algorithmic issue such as sort order, and it is indeed branch prediction.

@Luke Hutchison 2019-02-28 12:18:29

I don't really see how this proves anything? The only thing you have shown is that "not doing all the work of sorting the whole array takes less time than sorting the whole array". Your claim that this "also runs fastest" is very architecture-dependent. See my answer about how this works on ARM. PS you could make your code faster on non-ARM architectures by putting the summation inside the 200-element block loop, sorting in reverse, and then using Yochai Timmer's suggestion of breaking once you get an out-of range value. That way each 200-element block summation can be terminated early.

@user2297550 2019-09-05 13:11:52

Please ignore the petty comment above. It has as little substance as it is longwinded. The answer remains the fastest and also with the most explanatory power. And whoever is deleting comments, stop it or if you must, then delete the comment above, not this one.

@Peter Cordes 2019-12-13 14:44:53

If you just want to implement the algorithm efficiently over unsorted data, you would do that operation branchlessly (and with SIMD, e.g. with x86 pcmpgtb to find elements with their high bit set, then AND to zero smaller elements). Spending any time actually sorting chunks would be slower. A branchless version would have data-independent performance, also proving that the cost came from branch misprediction. Or just use performance counters to observe that directly, like Skylake int_misc.clear_resteer_cycles or int_misc.recovery_cycles to count front-end idle cycles from mispredicts

@Eugene 2017-11-06 16:15:16

As what has already been mentioned by others, what behind the mystery is Branch Predictor.

I'm not trying to add something but explaining the concept in another way. There is a concise introduction on the wiki which contains text and diagram. I do like the explanation below which uses a diagram to elaborate the Branch Predictor intuitively.

In computer architecture, a branch predictor is a digital circuit that tries to guess which way a branch (e.g. an if-then-else structure) will go before this is known for sure. The purpose of the branch predictor is to improve the flow in the instruction pipeline. Branch predictors play a critical role in achieving high effective performance in many modern pipelined microprocessor architectures such as x86.

Two-way branching is usually implemented with a conditional jump instruction. A conditional jump can either be "not taken" and continue execution with the first branch of code which follows immediately after the conditional jump, or it can be "taken" and jump to a different place in program memory where the second branch of code is stored. It is not known for certain whether a conditional jump will be taken or not taken until the condition has been calculated and the conditional jump has passed the execution stage in the instruction pipeline (see fig. 1).

figure 1

Based on the described scenario, I have written an animation demo to show how instructions are executed in a pipeline in different situations.

  1. Without the Branch Predictor.

Without branch prediction, the processor would have to wait until the conditional jump instruction has passed the execute stage before the next instruction can enter the fetch stage in the pipeline.

The example contains three instructions and the first one is a conditional jump instruction. The latter two instructions can go into the pipeline until the conditional jump instruction is executed.

without branch predictor

It will take 9 clock cycles for 3 instructions to be completed.

  1. Use Branch Predictor and don't take a conditional jump. Let's assume that the predict is not taking the conditional jump.

enter image description here

It will take 7 clock cycles for 3 instructions to be completed.

  1. Use Branch Predictor and take a conditional jump. Let's assume that the predict is not taking the conditional jump.

enter image description here

It will take 9 clock cycles for 3 instructions to be completed.

The time that is wasted in case of a branch misprediction is equal to the number of stages in the pipeline from the fetch stage to the execute stage. Modern microprocessors tend to have quite long pipelines so that the misprediction delay is between 10 and 20 clock cycles. As a result, making a pipeline longer increases the need for a more advanced branch predictor.

As you can see, it seems we don't have a reason not to use Branch Predictor.

It's quite a simple demo that clarifies the very basic part of Branch Predictor. If those gifs are annoying, please feel free to remove them from the answer and visitors can also get the demo from BranchPredictorDemo

@mckenzm 2019-07-29 05:14:16

Almost as good as the Intel marketing animations, and they were obsessed not just with branch prediction but out of order execution, both strategies being "speculative". Reading ahead in memory and storage (sequential pre-fetch to buffer) is also speculative. It all adds up.

@Maciej 2015-10-10 00:30:42

Frequently used Boolean operations in C++ produce many branches in the compiled program. If these branches are inside loops and are hard to predict they can slow down execution significantly. Boolean variables are stored as 8-bit integers with the value 0 for false and 1 for true.

Boolean variables are overdetermined in the sense that all operators that have Boolean variables as input check if the inputs have any other value than 0 or 1, but operators that have Booleans as output can produce no other value than 0 or 1. This makes operations with Boolean variables as input less efficient than necessary. Consider example:

bool a, b, c, d;
c = a && b;
d = a || b;

This is typically implemented by the compiler in the following way:

bool a, b, c, d;
if (a != 0) {
    if (b != 0) {
        c = 1;
    else {
        goto CFALSE;
else {
    c = 0;
if (a == 0) {
    if (b == 0) {
        d = 0;
    else {
        goto DTRUE;
else {
    d = 1;

This code is far from optimal. The branches may take a long time in case of mispredictions. The Boolean operations can be made much more efficient if it is known with certainty that the operands have no other values than 0 and 1. The reason why the compiler does not make such an assumption is that the variables might have other values if they are uninitialized or come from unknown sources. The above code can be optimized if a and b has been initialized to valid values or if they come from operators that produce Boolean output. The optimized code looks like this:

char a = 0, b = 1, c, d;
c = a & b;
d = a | b;

char is used instead of bool in order to make it possible to use the bitwise operators (& and |) instead of the Boolean operators (&& and ||). The bitwise operators are single instructions that take only one clock cycle. The OR operator (|) works even if a and b have other values than 0 or 1. The AND operator (&) and the EXCLUSIVE OR operator (^) may give inconsistent results if the operands have other values than 0 and 1.

~ can not be used for NOT. Instead, you can make a Boolean NOT on a variable which is known to be 0 or 1 by XOR'ing it with 1:

bool a, b;
b = !a;

can be optimized to:

char a = 0, b;
b = a ^ 1;

a && b cannot be replaced with a & b if b is an expression that should not be evaluated if a is false ( && will not evaluate b, & will). Likewise, a || b can not be replaced with a | b if b is an expression that should not be evaluated if a is true.

Using bitwise operators is more advantageous if the operands are variables than if the operands are comparisons:

bool a; double x, y, z;
a = x > y && z < 5.0;

is optimal in most cases (unless you expect the && expression to generate many branch mispredictions).

@steveha 2013-07-22 08:29:30

One way to avoid branch prediction errors is to build a lookup table, and index it using the data. Stefan de Bruijn discussed that in his answer.

But in this case, we know values are in the range [0, 255] and we only care about values >= 128. That means we can easily extract a single bit that will tell us whether we want a value or not: by shifting the data to the right 7 bits, we are left with a 0 bit or a 1 bit, and we only want to add the value when we have a 1 bit. Let's call this bit the "decision bit".

By using the 0/1 value of the decision bit as an index into an array, we can make code that will be equally fast whether the data is sorted or not sorted. Our code will always add a value, but when the decision bit is 0, we will add the value somewhere we don't care about. Here's the code:

// Test
clock_t start = clock();
long long a[] = {0, 0};
long long sum;

for (unsigned i = 0; i < 100000; ++i)
    // Primary loop
    for (unsigned c = 0; c < arraySize; ++c)
        int j = (data[c] >> 7);
        a[j] += data[c];

double elapsedTime = static_cast<double>(clock() - start) / CLOCKS_PER_SEC;
sum = a[1];

This code wastes half of the adds but never has a branch prediction failure. It's tremendously faster on random data than the version with an actual if statement.

But in my testing, an explicit lookup table was slightly faster than this, probably because indexing into a lookup table was slightly faster than bit shifting. This shows how my code sets up and uses the lookup table (unimaginatively called lut for "LookUp Table" in the code). Here's the C++ code:

// Declare and then fill in the lookup table
int lut[256];
for (unsigned c = 0; c < 256; ++c)
    lut[c] = (c >= 128) ? c : 0;

// Use the lookup table after it is built
for (unsigned i = 0; i < 100000; ++i)
    // Primary loop
    for (unsigned c = 0; c < arraySize; ++c)
        sum += lut[data[c]];

In this case, the lookup table was only 256 bytes, so it fits nicely in a cache and all was fast. This technique wouldn't work well if the data was 24-bit values and we only wanted half of them... the lookup table would be far too big to be practical. On the other hand, we can combine the two techniques shown above: first shift the bits over, then index a lookup table. For a 24-bit value that we only want the top half value, we could potentially shift the data right by 12 bits, and be left with a 12-bit value for a table index. A 12-bit table index implies a table of 4096 values, which might be practical.

The technique of indexing into an array, instead of using an if statement, can be used for deciding which pointer to use. I saw a library that implemented binary trees, and instead of having two named pointers (pLeft and pRight or whatever) had a length-2 array of pointers and used the "decision bit" technique to decide which one to follow. For example, instead of:

if (x < node->value)
    node = node->pLeft;
    node = node->pRight;

this library would do something like:

i = (x < node->value);
node = node->link[i];

Here's a link to this code: Red Black Trees, Eternally Confuzzled

@atlaste 2013-07-29 12:05:24

Right, you can also just use the bit directly and multiply (data[c]>>7 - which is discussed somewhere here as well); I intentionally left this solution out, but of course you are correct. Just a small note: The rule of thumb for lookup tables is that if it fits in 4KB (because of caching), it'll work - preferably make the table as small as possible. For managed languages I'd push that to 64KB, for low-level languages like C++ and C, I'd probably reconsider (that's just my experience). Since typeof(int) = 4, I'd try to stick to max 10 bits.

@steveha 2013-07-29 22:02:13

I think indexing with the 0/1 value will probably be faster than an integer multiply, but I guess if performance is really critical you should profile it. I agree that small lookup tables are essential to avoid cache pressure, but clearly if you have a bigger cache you can get away with a bigger lookup table, so 4KB is more a rule of thumb than a hard rule. I think you meant sizeof(int) == 4? That would be true for 32-bit. My two-year-old cell phone has a 32KB L1 cache, so even a 4K lookup table might work, especially if the lookup values were a byte instead of an int.

@Richard Tingle 2014-03-04 15:38:24

Possibly I'm missing something but in your j equals 0 or 1 method why don't you just multiply your value by j before adding it rather than using the array indexing (possibly should be multiplied by 1-j rather than j)

@atlaste 2014-03-18 08:45:05

@steveha Multiplication should be faster, I tried looking it up in the Intel books, but couldn't find it... either way, benchmarking also gives me that result here.

@atlaste 2014-03-18 08:52:11

@steveha P.S.: another possible answer would be int c = data[j]; sum += c & -(c >> 7); which requires no multiplications at all.

@Falco 2014-04-02 15:15:02

Good answer - since a lookup-table can also handle complex cases, where we cannot easily cheat with bit-manipulation

@Petter 2014-07-04 14:56:03

Zain is correct. The “if” is just hidden in the lookup table. The code is faster because the lookup table is hidden OUTSIDE the 100000 iterations. There is nothign to gain from using a lookup table for this problem.

@Logan Pickup 2014-10-27 21:01:42

i = (x < node->value); node = node->link[i]; doesn't have an explicit branch, but it still contains a comparison; it depends a lot on the target architecture as to whether this can be resolved without a branch or not. Since it can be done on x86 (using either CMOV or LAHF) and ARM (conditional add or move), which are the only architectures I use, this is possibly not important!

@steveha 2014-10-27 23:05:57

On what architecture would an expression like (x < node->value) require a branch to evaluate? All the architectures with which I am familiar have a "flags" register, and it is simple to extract the desired flag value. I guess on the Pentium 4 the flag bit extraction might be slow as IIRC that chip doesn't have dedicated shifting hardware for addresses, but borrows the ALU to shift bits. But I don't know where an actual branch would be needed. Hmm, your examples were conditional... the idea is that once you extract the bit from flags, you can just use indexing with no branch.

@Luke Hutchison 2019-02-28 12:37:24

Bit shifting is a zero-cost operation in ARM, so you may find the bit-shifted version is faster on ARM.

@Peter Mortensen 2019-05-27 13:07:05

What about cache sizes, L1, L2, etc.? Having to go to main memory for a table lookup would be killer. Can you address that in your answer?

@vulcan raven 2012-07-03 02:25:30

If you are curious about even more optimizations that can be done to this code, consider this:

Starting with the original loop:

for (unsigned i = 0; i < 100000; ++i)
    for (unsigned j = 0; j < arraySize; ++j)
        if (data[j] >= 128)
            sum += data[j];

With loop interchange, we can safely change this loop to:

for (unsigned j = 0; j < arraySize; ++j)
    for (unsigned i = 0; i < 100000; ++i)
        if (data[j] >= 128)
            sum += data[j];

Then, you can see that the if conditional is constant throughout the execution of the i loop, so you can hoist the if out:

for (unsigned j = 0; j < arraySize; ++j)
    if (data[j] >= 128)
        for (unsigned i = 0; i < 100000; ++i)
            sum += data[j];

Then, you see that the inner loop can be collapsed into one single expression, assuming the floating point model allows it (/fp:fast is thrown, for example)

for (unsigned j = 0; j < arraySize; ++j)
    if (data[j] >= 128)
        sum += data[j] * 100000;

That one is 100,000 times faster than before.

@Jyaif 2012-10-11 01:48:01

If you want to cheat, you might as well take the multiplication outside the loop and do sum*=100000 after the loop.

@Yair Altman 2013-03-04 12:59:11

@Michael - I believe that this example is actually an example of loop-invariant hoisting (LIH) optimization, and NOT loop swap. In this case, the entire inner loop is independent of the outer loop and can therefore be hoisted out of the outer loop, whereupon the result is simply multiplied by a sum over i of one unit =1e5. It makes no difference to the end result, but I just wanted to set the record straight since this is such a frequented page.

@Alex North-Keys 2013-05-15 11:57:16

Although not in the simple spirit of swapping loops, the inner if at this point could be converted to: sum += (data[j] >= 128) ? data[j] * 100000 : 0; which the compiler may be able to reduce to cmovge or equivalent.

@saurabheights 2016-06-22 15:45:19

The outer loop is to make the time taken by inner loop large enough to profile. So why would you loop swap. At the end, that loop will be removed anyways.

@Matthieu M. 2016-12-29 13:58:53

@saurabheights: Wrong question: why would the compiler NOT loop swap. Microbenchmarks is hard ;)

@saurabheights 2016-12-29 14:53:48

@MatthieuM.: I agree with you but to the extent that compiler may optimize it. Not sure if it will, but I am no expert at Compiler Optimizations. But if you are right, wouldn't this invalidate the remark "That one is 100,000x faster than before" by vulcan.

@CygnusX1 2018-11-10 12:24:36

If you make 'i' volatile, the compiler will have hard time optimizing anything that involves the outer loop. The inner contents will still be optimized though.

@WiSaGaN 2012-06-28 02:14:03

The reason why performance improves drastically when the data is sorted is that the branch prediction penalty is removed, as explained beautifully in Mysticial's answer.

Now, if we look at the code

if (data[c] >= 128)
    sum += data[c];

we can find that the meaning of this particular if... else... branch is to add something when a condition is satisfied. This type of branch can be easily transformed into a conditional move statement, which would be compiled into a conditional move instruction: cmovl, in an x86 system. The branch and thus the potential branch prediction penalty is removed.

In C, thus C++, the statement, which would compile directly (without any optimization) into the conditional move instruction in x86, is the ternary operator ... ? ... : .... So we rewrite the above statement into an equivalent one:

sum += data[c] >=128 ? data[c] : 0;

While maintaining readability, we can check the speedup factor.

On an Intel Core i7-2600K @ 3.4 GHz and Visual Studio 2010 Release Mode, the benchmark is (format copied from Mysticial):


//  Branch - Random
seconds = 8.885

//  Branch - Sorted
seconds = 1.528

//  Branchless - Random
seconds = 3.716

//  Branchless - Sorted
seconds = 3.71


//  Branch - Random
seconds = 11.302

//  Branch - Sorted
 seconds = 1.830

//  Branchless - Random
seconds = 2.736

//  Branchless - Sorted
seconds = 2.737

The result is robust in multiple tests. We get a great speedup when the branch result is unpredictable, but we suffer a little bit when it is predictable. In fact, when using a conditional move, the performance is the same regardless of the data pattern.

Now let's look more closely by investigating the x86 assembly they generate. For simplicity, we use two functions max1 and max2.

max1 uses the conditional branch if... else ...:

int max1(int a, int b) {
    if (a > b)
        return a;
        return b;

max2 uses the ternary operator ... ? ... : ...:

int max2(int a, int b) {
    return a > b ? a : b;

On a x86-64 machine, GCC -S generates the assembly below.

    movl    %edi, -4(%rbp)
    movl    %esi, -8(%rbp)
    movl    -4(%rbp), %eax
    cmpl    -8(%rbp), %eax
    jle     .L2
    movl    -4(%rbp), %eax
    movl    %eax, -12(%rbp)
    jmp     .L4
    movl    -8(%rbp), %eax
    movl    %eax, -12(%rbp)
    movl    -12(%rbp), %eax

    movl    %edi, -4(%rbp)
    movl    %esi, -8(%rbp)
    movl    -4(%rbp), %eax
    cmpl    %eax, -8(%rbp)
    cmovge  -8(%rbp), %eax

max2 uses much less code due to the usage of instruction cmovge. But the real gain is that max2 does not involve branch jumps, jmp, which would have a significant performance penalty if the predicted result is not right.

So why does a conditional move perform better?

In a typical x86 processor, the execution of an instruction is divided into several stages. Roughly, we have different hardware to deal with different stages. So we do not have to wait for one instruction to finish to start a new one. This is called pipelining.

In a branch case, the following instruction is determined by the preceding one, so we cannot do pipelining. We have to either wait or predict.

In a conditional move case, the execution conditional move instruction is divided into several stages, but the earlier stages like Fetch and Decode does not depend on the result of the previous instruction; only latter stages need the result. Thus, we wait a fraction of one instruction's execution time. This is why the conditional move version is slower than the branch when prediction is easy.

The book Computer Systems: A Programmer's Perspective, second edition explains this in detail. You can check Section 3.6.6 for Conditional Move Instructions, entire Chapter 4 for Processor Architecture, and Section 5.11.2 for a special treatment for Branch Prediction and Misprediction Penalties.

Sometimes, some modern compilers can optimize our code to assembly with better performance, sometimes some compilers can't (the code in question is using Visual Studio's native compiler). Knowing the performance difference between branch and conditional move when unpredictable can help us write code with better performance when the scenario gets so complex that the compiler can not optimize them automatically.

@WiSaGaN 2012-06-30 16:08:27

@BlueRaja-DannyPflughoeft This is the un-optimized version. The compiler did NOT optimize the ternary-operator, it just TRANSLATE it. GCC can optimize if-then if given sufficient optimization level, nevertheless, this one shows the power of conditional move, and manual optimization makes a difference.

@Justin L. 2012-10-11 03:12:02

@WiSaGaN The code demonstrates nothing, because your two pieces of code compile to the same machine code. It's critically important that people don't get the idea that somehow the if statement in your example is different from the terenary in your example. It's true that you own up to the similarity in your last paragraph, but that doesn't erase the fact that the rest of the example is harmful.

@Justin L. 2012-10-11 03:18:22

For example, I have no idea if the benchmarks at the top of your post are compiled with optimizations or not. Obviously this is important. If there's a difference in speed when compiled with optimizations, that would be interesting, especially if you could explain the difference by pointing to differences in the generated code.

@WiSaGaN 2012-10-11 03:23:37

@JustinL. OP's code can not be optimized by VS2010 compiler, the test was done in release mode.

@Justin L. 2012-10-11 04:13:03

@WiSaGaN My downvote would definitely turn into an upvote if you modified your answer to remove the misleading -O0 example and to show the difference in optimized asm on your two testcases.

@UpAndAdam 2013-09-13 20:12:16

@WiSaGaN What do you mean it can't be optimized it was done in release mode? You can specify the optimization to be used in your solution/proejct configuration regardless of what "mode" you build unless they magically removed that from VS2010...

@WiSaGaN 2013-09-14 15:18:02

@UpAndAdam At the moment of the test, VS2010 can't optimize the original branch into a conditional move even when specifying high optimization level, while gcc can.

@UpAndAdam 2013-09-16 14:43:48

@WiSaGaN gotcha, thanks didn't realize it was just a short coming of the optimizations VS can make as a whole. From your comment it sounded like it had to do with the compilation mode. As you can guess I'm not a very heavy windows and VS user anymore

@Kin Cheung 2015-09-01 05:51:20

This ternary operator trick works beautifully for Java. After reading Mystical's answer, I was wondering what could be done for Java to avoid false branch prediction since Java doesn't have anything equivalent to -O3. ternary operator: 2.1943s and original: 6.0303s.

@Omkaar.K 2017-12-07 17:28:29

Sorted arrays are processed faster than an unsorted array, due to a phenomena called branch prediction.

The branch predictor is a digital circuit (in computer architecture) trying to predict which way a branch will go, improving the flow in the instruction pipeline. The circuit/computer predicts the next step and executes it.

Making a wrong prediction leads to going back to the previous step, and executing with another prediction. Assuming the prediction is correct, the code will continue to the next step. A wrong prediction results in repeating the same step, until a correct prediction occurs.

The answer to your question is very simple.

In an unsorted array, the computer makes multiple predictions, leading to an increased chance of errors. Whereas, in a sorted array, the computer makes fewer predictions, reducing the chance of errors. Making more predictions requires more time.

Sorted Array: Straight Road ____________________________________________________________________________________ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT

Unsorted Array: Curved Road

______   ________
|     |__|

Branch prediction: Guessing/predicting which road is straight and following it without checking

___________________________________________ Straight road
 |_________________________________________|Longer road

Although both the roads reach the same destination, the straight road is shorter, and the other is longer. If then you choose the other by mistake, there is no turning back, and so you will waste some extra time if you choose the longer road. This is similar to what happens in the computer, and I hope this helped you understand better.

Also I want to cite @Simon_Weaver from the comments:

It doesn’t make fewer predictions - it makes fewer incorrect predictions. It still has to predict for each time through the loop...

@Sinatr 2018-07-04 13:54:21

"In simple words" - I find your explanation less simple than the other with trains and far less accurate than any of other answer, though I am not a beginner. I am very curious why there are so many upvotes, perhaps one of future upvoters can tell me?

@xoxel 2018-07-09 12:45:50

@Sinatr it's probably really opinion based, i myself found that good enough to upvote it, it's ofc not as accurate as other examples, that's the whole point: giving away the answer (as we can all agree branch-prediction is involved here) without having readers to go trought technical explanations as others did (very well). And i think he did it well enough.

@Simon_Weaver 2018-07-16 01:28:03

It doesn’t make fewer predictions - it makes fewer incorrect predictions. It still has to predict for each time through the loop.

@Omkaar.K 2018-07-16 05:52:47

Oh your correct, my bad, thank you @Simon_Weaver, I will correct it in some time, or please can some of your edit it and then I’ll approve it, thanks in advance...

@Yves Daoust 2013-07-24 07:57:39

In the sorted case, you can do better than relying on successful branch prediction or any branchless comparison trick: completely remove the branch.

Indeed, the array is partitioned in a contiguous zone with data < 128 and another with data >= 128. So you should find the partition point with a dichotomic search (using Lg(arraySize) = 15 comparisons), then do a straight accumulation from that point.

Something like (unchecked)

int i= 0, j, k= arraySize;
while (i < k)
  j= (i + k) >> 1;
  if (data[j] >= 128)
    k= j;
    i= j;
sum= 0;
for (; i < arraySize; i++)
  sum+= data[i];

or, slightly more obfuscated

int i, k, j= (i + k) >> 1;
for (i= 0, k= arraySize; i < k; (data[j] >= 128 ? k : i)= j)
  j= (i + k) >> 1;
for (sum= 0; i < arraySize; i++)
  sum+= data[i];

A yet faster approach, that gives an approximate solution for both sorted or unsorted is: sum= 3137536; (assuming a truly uniform distribution, 16384 samples with expected value 191.5) :-)

@sehe 2013-07-24 16:31:30

sum= 3137536 - clever. That's kinda obviously not the point of the question. The question is clearly about explaining surprising performance characteristics. I'm inclined to say that the addition of doing std::partition instead of std::sort is valuable. Though the actual question extends to more than just the synthetic benchmark given.

@Yves Daoust 2013-07-24 20:37:31

@DeadMG: this is indeed not the standard dichotomic search for a given key, but a search for the partitioning index; it requires a single compare per iteration. But don't rely on this code, I have not checked it. If you are interested in a guaranteed correct implementation, let me know.

@Yochai Timmer 2017-11-23 14:28:29

Besides the fact that the branch prediction may slow you down, a sorted array has another advantage:

You can have a stop condition instead of just checking the value, this way you only loop over the relevant data, and ignore the rest.
The branch prediction will miss only once.

 // sort backwards (higher values first), may be in some other part of the code
 std::sort(data, data + arraySize, std::greater<int>());

 for (unsigned c = 0; c < arraySize; ++c) {
       if (data[c] < 128) {
       sum += data[c];               

@Luke Hutchison 2018-11-06 12:28:29

Right, but the setup cost of sorting the array is O(N log N), so breaking early doesn't help you if the only reason you are sorting the array is to be able to break early. If, however, you have other reasons to pre-sort the array, then yes, this is valuable.

@Yochai Timmer 2019-02-27 12:23:22

Depends how many times you sort the data compared to how many times you loop on it. The sort in this example is just an example, it doesn't have to be just before the loop

@Luke Hutchison 2019-02-28 12:28:14

Yes, that's exactly the point I made in my first comment :-) You say "The branch prediction will miss only once." But you are not counting the O(N log N) branch prediction misses inside the sort algorithm, which is actually greater than the O(N) branch prediction misses in the unsorted case. So you would need to use the entirety of the sorted data O(log N) times to break even (probably actually closer to O(10 log N), depending on the sort algorithm, e.g. for quicksort, due to cache misses -- mergesort is more cache-coherent, so you would need closer to O(2 log N) usages to break even.)

@Luke Hutchison 2019-02-28 12:34:48

One significant optimization though would be to do only "half a quicksort", sorting only items less than the target pivot value of 127 (assuming everything less than or equal to the pivot is sorted after the pivot). Once you reach the pivot, sum the elements before the pivot. This would run in O(N) startup time rather than O(N log N), although there will still be a lot of branch prediction misses, probably of the order of O(5 N) based on the numbers I gave before, since it's half a quicksort.

@Saqlain 2013-02-15 07:24:16

As data is distributed between 0 and 255 when the array is sorted, around the first half of the iterations will not enter the if-statement (the if statement is shared below).

if (data[c] >= 128)
    sum += data[c];

The question is: What makes the above statement not execute in certain cases as in case of sorted data? Here comes the "branch predictor". A branch predictor is a digital circuit that tries to guess which way a branch (e.g. an if-then-else structure) will go before this is known for sure. The purpose of the branch predictor is to improve the flow in the instruction pipeline. Branch predictors play a critical role in achieving high effective performance!

Let's do some bench marking to understand it better

The performance of an if-statement depends on whether its condition has a predictable pattern. If the condition is always true or always false, the branch prediction logic in the processor will pick up the pattern. On the other hand, if the pattern is unpredictable, the if-statement will be much more expensive.

Let’s measure the performance of this loop with different conditions:

for (int i = 0; i < max; i++)
    if (condition)

Here are the timings of the loop with different true-false patterns:

Condition                Pattern             Time (ms)
(i & 0×80000000) == 0    T repeated          322

(i & 0xffffffff) == 0    F repeated          276

(i & 1) == 0             TF alternating      760

(i & 3) == 0             TFFFTFFF…           513

(i & 2) == 0             TTFFTTFF…           1675

(i & 4) == 0             TTTTFFFFTTTTFFFF…   1275

(i & 8) == 0             8T 8F 8T 8F …       752

(i & 16) == 0            16T 16F 16T 16F …   490

A “bad” true-false pattern can make an if-statement up to six times slower than a “good” pattern! Of course, which pattern is good and which is bad depends on the exact instructions generated by the compiler and on the specific processor.

So there is no doubt about the impact of branch prediction on performance!

@cst1992 2016-03-28 12:58:51

@MooingDuck 'Cause it won't make a difference - that value can be anything, but it still will be in the bounds of these thresholds. So why show a random value when you already know the limits? Although I agree that you could show one for the sake of completeness, and 'just for the heck of it'.

@Mooing Duck 2016-03-28 18:27:16

@cst1992: Right now his slowest timing is TTFFTTFFTTFF, which seems, to my human eye, quite predictable. Random is inherently unpredictable, so it's entirely possible it would be slower still, and thus outside the limits shown here. OTOH, it could be that TTFFTTFF perfectly hits the pathological case. Can't tell, since he didn't show the timings for random.

@steveha 2016-07-20 21:07:37

@MooingDuck To a human eye, "TTFFTTFFTTFF" is a predictable sequence, but what we are talking about here is the behavior of the branch predictor built into a CPU. The branch predictor is not AI-level pattern recognition; it's very simple. When you just alternate branches it doesn't predict well. In most code, branches go the same way almost all the time; consider a loop that executes a thousand times. The branch at the end of the loop goes back to the start of the loop 999 times, and then the thousandth time does something different. A very simple branch predictor works well, usually.

@Mooing Duck 2016-07-20 21:10:18

@steveha: I think you're making assumptions about how the CPU branch predictor works, and I disagree with that methodology. I don't know how advanced that branch predictor is, but I seem to think it's far more advanced than you do. You're probably right, but measurements would definitely be good.

@steveha 2016-07-26 03:49:15

@MooingDuck It is true that I am not an expert in processor design. But I invite you to read the Wikipedia page about branch predictors. Not one of the discussed designs could lock on to the pattern TTFFTTFF... and predict correctly. (Except maybe for the neural net one, with a sufficiently advanced neural net, and I'll bet you cash money that you don't own a computing device that has such a branch predictor in its processor.)

@Mooing Duck 2016-07-26 04:33:55

@steveha: The Two-level adaptive predictor could lock onto the TTFFTTFF pattern with no issue whatsoever. "Variants of this prediction method are used in most modern microprocessors". Local branch prediction and Global branch prediction are based on a two level adaptive predictor, they can as well. "Global branch prediction is used in AMD processors, and in Intel Pentium M, Core, Core 2, and Silvermont-based Atom processors" Also add Agree predictor, Hybrid predictor, Prediction of indirect jumps, to that list. Loop predictor wont lock on, but hits 75%. That leaves only 2 that can't lock on

@Warren K 2016-12-23 22:32:07

@MooingDuck : The diagram in Surt's answer below I think explains why TTFFTTFF is in fact the "pathological case" in Saqlain's example.

@atlaste 2013-04-24 06:26:28

I just read up on this question and its answers, and I feel an answer is missing.

A common way to eliminate branch prediction that I've found to work particularly good in managed languages is a table lookup instead of using a branch (although I haven't tested it in this case).

This approach works in general if:

  1. it's a small table and is likely to be cached in the processor, and
  2. you are running things in a quite tight loop and/or the processor can preload the data.

Background and why

From a processor perspective, your memory is slow. To compensate for the difference in speed, a couple of caches are built into your processor (L1/L2 cache). So imagine that you're doing your nice calculations and figure out that you need a piece of memory. The processor will get its 'load' operation and loads the piece of memory into cache -- and then uses the cache to do the rest of the calculations. Because memory is relatively slow, this 'load' will slow down your program.

Like branch prediction, this was optimized in the Pentium processors: the processor predicts that it needs to load a piece of data and attempts to load that into the cache before the operation actually hits the cache. As we've already seen, branch prediction sometimes goes horribly wrong -- in the worst case scenario you need to go back and actually wait for a memory load, which will take forever (in other words: failing branch prediction is bad, a memory load after a branch prediction fail is just horrible!).

Fortunately for us, if the memory access pattern is predictable, the processor will load it in its fast cache and all is well.

The first thing we need to know is what is small? While smaller is generally better, a rule of thumb is to stick to lookup tables that are <= 4096 bytes in size. As an upper limit: if your lookup table is larger than 64K it's probably worth reconsidering.

Constructing a table

So we've figured out that we can create a small table. Next thing to do is get a lookup function in place. Lookup functions are usually small functions that use a couple of basic integer operations (and, or, xor, shift, add, remove and perhaps multiply). You want to have your input translated by the lookup function to some kind of 'unique key' in your table, which then simply gives you the answer of all the work you wanted it to do.

In this case: >= 128 means we can keep the value, < 128 means we get rid of it. The easiest way to do that is by using an 'AND': if we keep it, we AND it with 7FFFFFFF; if we want to get rid of it, we AND it with 0. Notice also that 128 is a power of 2 -- so we can go ahead and make a table of 32768/128 integers and fill it with one zero and a lot of 7FFFFFFFF's.

Managed languages

You might wonder why this works well in managed languages. After all, managed languages check the boundaries of the arrays with a branch to ensure you don't mess up...

Well, not exactly... :-)

There has been quite some work on eliminating this branch for managed languages. For example:

for (int i = 0; i < array.Length; ++i)
   // Use array[i]

In this case, it's obvious to the compiler that the boundary condition will never be hit. At least the Microsoft JIT compiler (but I expect Java does similar things) will notice this and remove the check altogether. WOW, that means no branch. Similarly, it will deal with other obvious cases.

If you run into trouble with lookups in managed languages -- the key is to add a & 0x[something]FFF to your lookup function to make the boundary check predictable -- and watch it going faster.

The result of this case

// Generate data
int arraySize = 32768;
int[] data = new int[arraySize];

Random random = new Random(0);
for (int c = 0; c < arraySize; ++c)
    data[c] = random.Next(256);

/*To keep the spirit of the code intact, I'll make a separate lookup table
(I assume we cannot modify 'data' or the number of loops)*/

int[] lookup = new int[256];

for (int c = 0; c < 256; ++c)
    lookup[c] = (c >= 128) ? c : 0;

// Test
DateTime startTime = System.DateTime.Now;
long sum = 0;

for (int i = 0; i < 100000; ++i)
    // Primary loop
    for (int j = 0; j < arraySize; ++j)
        /* Here you basically want to use simple operations - so no
        random branches, but things like &, |, *, -, +, etc. are fine. */
        sum += lookup[data[j]];

DateTime endTime = System.DateTime.Now;
Console.WriteLine(endTime - startTime);
Console.WriteLine("sum = " + sum);

@Dustin Oprea 2013-04-24 17:50:33

You want to bypass the branch-predictor, why? It's an optimization.

@atlaste 2013-04-24 21:57:13

Because no branch is better than a branch :-) In a lot of situations this is simply a lot faster... if you're optimizing, it's definitely worth a try. They also use it quite a bit in f.ex.

@Zain Rizvi 2013-12-19 21:45:03

In general lookup tables can be fast, but have you ran the tests for this particular condition? You'll still have a branch condition in your code, only now it's moved to the look up table generation part. You still wouldn't get your perf boost

@atlaste 2013-12-20 18:57:29

@Zain if you really want to know... Yes: 15 seconds with the branch and 10 with my version. Regardless, it's a useful technique to know either way.

@Kris Vandermotten 2014-03-12 12:17:49

Why not sum += lookup[data[j]] where lookup is an array with 256 entries, the first ones being zero and the last ones being equal to the index?

@atlaste 2014-03-17 10:24:45

@KrisVandermotten +1 for that - I made a bug implementing it by checking the index of j; it should have been lookup[data[j]] as you suggest instead.

@Kris Vandermotten 2014-03-17 17:17:02

You say "There has been quite some work on eliminating this branch for managed languages." Sure, but that won't help with sum += lookup[data[j]];. However, what will help is that the lookup will never go out of bounds in a bug free program, so the branch predictor can predict the branch perfectly. And that means speed!

@atlaste 2014-03-18 08:33:51

@KrisVandermotten Yes, that's correct. What would also help in this case is use the correct data type (e.g. byte[] data); that would eliminate the branch completely, which means more speed. :-) Basically I always assume that if (1) you're in a tight loop with a limited amount of code (such as here) and (2) if you could predict the branches with 'static code analysis', then the processor / JIT'ter will do their work properly.

@A. Binzxxxxxx 2015-10-26 16:06:42

array access means pointer operation like *(ptr + x) - right? tannery operator should be faster here - right?

@Chinaxing 2016-05-13 16:15:48

but the lookup method will need the jump instruction to jump to the target codes. this place have the unconditional jump, the cpu will always clean its pipeline in this case ? which will be bad than if prediction when the prediction hit. is this right?

@Konard 2019-05-11 10:56:03

Looks like for (int c = 0; c < 256; ++c) lookup[c] = (c >= 128) ? c : 0; can be replaced with for (int c = 128; c < 256; ++c) lookup[c] = c; because array already initialized to zeros in managed language on initialization.

@fzyzcjy 2019-08-02 02:18:38

@Zain Rizvi Generating lookup table takes very little time compared with branching every time, so it does have time boost

@fzyzcjy 2019-08-02 02:22:37

@Chinaxing For unconditional jumps, the CPU will always load its pipeline, so its performance is similar to always-right prediction.

@Luke Hutchison 2017-12-22 13:13:05

On ARM, there is no branch needed, because every instruction has a 4-bit condition field, which is tested at zero cost. This eliminates the need for short branches, and there would be no branch prediction hit. Therefore, the sorted version would run slower than the unsorted version on ARM, because of the extra overhead of sorting. The inner loop would look something like the following:

MOV R0, #0     // R0 = sum = 0
MOV R1, #0     // R1 = c = 0
ADR R2, data   // R2 = addr of data array (put this instruction outside outer loop)
.inner_loop    // Inner loop branch label
    LDRB R3, [R2, R1]     // R3 = data[c]
    CMP R3, #128          // compare R3 to 128
    ADDGE R0, R0, R3      // if R3 >= 128, then sum += data[c] -- no branch needed!
    ADD R1, R1, #1        // c++
    CMP R1, #arraySize    // compare c to arraySize
    BLT inner_loop        // Branch to inner_loop if c < arraySize

@jpaugh 2018-05-14 14:04:03

Are you saying that every instruction can be conditional? So, multiple instructions with the GE suffix could be performed sequentially, without changing the value of R3 in between?

@Luke Hutchison 2018-05-15 17:06:42

Yes, correct, every instruction can be conditional on ARM, at least in the 32 and 64 bit instruction sets. There's a devoted 4-bit condition field. You can have several instructions in a row with the same condition, but at some point, if the chance of the condition being false is non-negligible, then it is more efficient to add a branch.

@Luke Hutchison 2018-05-15 17:06:54

The other innovation in ARM is the addition of the S instruction suffix, also optional on (almost) all instructions, which if absent, prevents instructions from changing status bits (with the exception of the CMP instruction, whose job is to set status bits, so it doesn't need the S suffix). This allows you to avoid CMP instructions in many cases, as long as the comparison is with zero or similar (eg. SUBS R0, R0, #1 will set the Z (Zero) bit when R0 reaches zero). Conditionals and the S suffix incur zero overhead. It's quite a beautiful ISA.

@Luke Hutchison 2018-05-15 17:08:22

Not adding the S suffix allows you to have several conditional instructions in a row without worrying that one of them might change the status bits, which might otherwise have the side effect of skipping the rest of the conditional instructions.

@Alireza 2017-06-18 11:40:43

That's for sure!...

Branch prediction makes the logic run slower, because of the switching which happens in your code! It's like you are going a straight street or a street with a lot of turnings, for sure the straight one is going to be done quicker!...

If the array is sorted, your condition is false at the first step: data[c] >= 128, then becomes a true value for the whole way to the end of the street. That's how you get to the end of the logic faster. On the other hand, using an unsorted array, you need a lot of turning and processing which make your code run slower for sure...

Look at the image I created for you below. Which street is going to be finished faster?

Branch Prediction

So programmatically, branch prediction causes the process to be slower...

Also at the end, it's good to know we have two kinds of branch predictions that each is going to affect your code differently:

1. Static

2. Dynamic

Branch Prediction

Static branch prediction is used by the microprocessor the first time a conditional branch is encountered, and dynamic branch prediction is used for succeeding executions of the conditional branch code.

In order to effectively write your code to take advantage of these rules, when writing if-else or switch statements, check the most common cases first and work progressively down to the least common. Loops do not necessarily require any special ordering of code for static branch prediction, as only the condition of the loop iterator is normally used.

@Harsh Sharma 2015-07-03 15:35:52

The above behavior is happening because of Branch prediction.

To understand branch prediction one must first understand Instruction Pipeline:

Any instruction is broken into a sequence of steps so that different steps can be executed concurrently in parallel. This technique is known as instruction pipeline and this is used to increase throughput in modern processors. To understand this better please see this example on Wikipedia.

Generally, modern processors have quite long pipelines, but for ease let's consider these 4 steps only.

  1. IF -- Fetch the instruction from memory
  2. ID -- Decode the instruction
  3. EX -- Execute the instruction
  4. WB -- Write back to CPU register

4-stage pipeline in general for 2 instructions. 4-stage pipeline in general

Moving back to the above question let's consider the following instructions:

                        A) if (data[c] >= 128)
                               /  \
                              /    \
                        true /      \ false
                            /        \
                           /          \
                          /            \
                         /              \
              B) sum += data[c];          C) for loop or print().

Without branch prediction, the following would occur:

To execute instruction B or instruction C the processor will have to wait till the instruction A doesn't reach till EX stage in the pipeline, as the decision to go to instruction B or instruction C depends on the result of instruction A. So the pipeline will look like this.

when if condition returns true: enter image description here

When if condition returns false: enter image description here

As a result of waiting for the result of instruction A, the total CPU cycles spent in the above case (without branch prediction; for both true and false) is 7.

So what is branch prediction?

Branch predictor will try to guess which way a branch (an if-then-else structure) will go before this is known for sure. It will not wait for the instruction A to reach the EX stage of the pipeline, but it will guess the decision and go to that instruction (B or C in case of our example).

In case of a correct guess, the pipeline looks something like this: enter image description here

If it is later detected that the guess was wrong then the partially executed instructions are discarded and the pipeline starts over with the correct branch, incurring a delay. The time that is wasted in case of a branch misprediction is equal to the number of stages in the pipeline from the fetch stage to the execute stage. Modern microprocessors tend to have quite long pipelines so that the misprediction delay is between 10 and 20 clock cycles. The longer the pipeline the greater the need for a good branch predictor.

In the OP's code, the first time when the conditional, the branch predictor does not have any information to base up prediction, so the first time it will randomly choose the next instruction. Later in the for loop, it can base the prediction on the history. For an array sorted in ascending order, there are three possibilities:

  1. All the elements are less than 128
  2. All the elements are greater than 128
  3. Some starting new elements are less than 128 and later it become greater than 128

Let us assume that the predictor will always assume the true branch on the first run.

So in the first case, it will always take the true branch since historically all its predictions are correct. In the 2nd case, initially it will predict wrong, but after a few iterations, it will predict correctly. In the 3rd case, it will initially predict correctly till the elements are less than 128. After which it will fail for some time and the correct itself when it sees branch prediction failure in history.

In all these cases the failure will be too less in number and as a result, only a few times it will need to discard the partially executed instructions and start over with the correct branch, resulting in fewer CPU cycles.

But in case of a random unsorted array, the prediction will need to discard the partially executed instructions and start over with the correct branch most of the time and result in more CPU cycles compared to the sorted array.

@M.kazem Akhgary 2017-10-11 14:49:42

how are two instructions executed together? is this done with separate cpu cores or is pipeline instruction is integrated in single cpu core?

@Sergey.quixoticaxis.Ivanov 2017-11-03 07:45:12

@M.kazemAkhgary It's all inside one logical core. If you're interested, this is nicely described for example in Intel Software Developer Manual

@Shan 2012-12-30 16:16:46

I tried the same code with MATLAB 2011b with my MacBook Pro (Intel i7, 64 bit, 2.4 GHz) for the following MATLAB code:

% Processing time with Sorted data vs unsorted data
% Generate data
arraySize = 32768
sum = 0;
% Generate random integer data from range 0 to 255
data = randi(256, arraySize, 1);

%Sort the data
data1= sort(data); % data1= data  when no sorting done

%Start a stopwatch timer to measure the execution time

for i=1:100000

    for j=1:arraySize

        if data1(j)>=128
            sum=sum + data1(j);


ExeTimeWithSorting = toc - tic;

The results for the above MATLAB code are as follows:

  a: Elapsed time (without sorting) = 3479.880861 seconds.
  b: Elapsed time (with sorting ) = 2377.873098 seconds.

The results of the C code as in @GManNickG I get:

  a: Elapsed time (without sorting) = 19.8761 sec.
  b: Elapsed time (with sorting ) = 7.37778 sec.

Based on this, it looks MATLAB is almost 175 times slower than the C implementation without sorting and 350 times slower with sorting. In other words, the effect (of branch prediction) is 1.46x for MATLAB implementation and 2.7x for the C implementation.

@ysap 2013-05-17 19:53:00

Just for the sake of completeness, this is probably not how you'd implement that in Matlab. I bet it'd be much faster if done after vectorizing the problem.

@Shan 2013-05-17 23:50:24

Matlab does automatic parallelization / vectorization in many situations but the issue here is to check the effect of branch prediction. Matlab is not immune in anyway!

@Thorbjørn Ravn Andersen 2013-08-24 16:34:32

Does matlab use native numbers or a mat lab specific implementation (infinite amount of digits or so?)

@Tony Tannous 2017-08-04 10:07:12

Branch-prediction gain!

It is important to understand that branch misprediction doesn't slow down programs. The cost of a missed prediction is just as if branch prediction didn't exist and you waited for the evaluation of the expression to decide what code to run (further explanation in the next paragraph).

if (expression)
    // Run 1
} else {
    // Run 2

Whenever there's an if-else \ switch statement, the expression has to be evaluated to determine which block should be executed. In the assembly code generated by the compiler, conditional branch instructions are inserted.

A branch instruction can cause a computer to begin executing a different instruction sequence and thus deviate from its default behavior of executing instructions in order (i.e. if the expression is false, the program skips the code of the if block) depending on some condition, which is the expression evaluation in our case.

That being said, the compiler tries to predict the outcome prior to it being actually evaluated. It will fetch instructions from the if block, and if the expression turns out to be true, then wonderful! We gained the time it took to evaluate it and made progress in the code; if not then we are running the wrong code, the pipeline is flushed, and the correct block is run.


Let's say you need to pick route 1 or route 2. Waiting for your partner to check the map, you have stopped at ## and waited, or you could just pick route1 and if you were lucky (route 1 is the correct route), then great you didn't have to wait for your partner to check the map (you saved the time it would have taken him to check the map), otherwise you will just turn back.

While flushing pipelines is super fast, nowadays taking this gamble is worth it. Predicting sorted data or a data that changes slowly is always easier and better than predicting fast changes.

 O      Route 1  /-------------------------------
/|\             /
 |  ---------##/
/ \            \
        Route 2  \--------------------------------

@Farhad 2017-10-03 09:47:54

It's about branch prediction. What is it?

  • A branch predictor is one of the ancient performance improving techniques which still finds relevance into modern architectures. While the simple prediction techniques provide fast lookup and power efficiency they suffer from a high misprediction rate.

  • On the other hand, complex branch predictions –either neural based or variants of two-level branch prediction –provide better prediction accuracy, but they consume more power and complexity increases exponentially.

  • In addition to this, in complex prediction techniques the time taken to predict the branches is itself very high –ranging from 2 to 5 cycles –which is comparable to the execution time of actual branches.

  • Branch prediction is essentially an optimization (minimization) problem where the emphasis is on to achieve lowest possible miss rate, low power consumption, and low complexity with minimum resources.

There really are three different kinds of branches:

Forward conditional branches - based on a run-time condition, the PC (program counter) is changed to point to an address forward in the instruction stream.

Backward conditional branches - the PC is changed to point backward in the instruction stream. The branch is based on some condition, such as branching backwards to the beginning of a program loop when a test at the end of the loop states the loop should be executed again.

Unconditional branches - this includes jumps, procedure calls and returns that have no specific condition. For example, an unconditional jump instruction might be coded in assembly language as simply "jmp", and the instruction stream must immediately be directed to the target location pointed to by the jump instruction, whereas a conditional jump that might be coded as "jmpne" would redirect the instruction stream only if the result of a comparison of two values in a previous "compare" instructions shows the values to not be equal. (The segmented addressing scheme used by the x86 architecture adds extra complexity, since jumps can be either "near" (within a segment) or "far" (outside the segment). Each type has different effects on branch prediction algorithms.)

Static/dynamic Branch Prediction: Static branch prediction is used by the microprocessor the first time a conditional branch is encountered, and dynamic branch prediction is used for succeeding executions of the conditional branch code.


@Surt 2015-10-11 21:05:18

An official answer would be from

  1. Intel - Avoiding the Cost of Branch Misprediction
  2. Intel - Branch and Loop Reorganization to Prevent Mispredicts
  3. Scientific papers - branch prediction computer architecture
  4. Books: J.L. Hennessy, D.A. Patterson: Computer architecture: a quantitative approach
  5. Articles in scientific publications: T.Y. Yeh, Y.N. Patt made a lot of these on branch predictions.

You can also see from this lovely diagram why the branch predictor gets confused.

2-bit state diagram

Each element in the original code is a random value

data[c] = std::rand() % 256;

so the predictor will change sides as the std::rand() blow.

On the other hand, once it's sorted, the predictor will first move into a state of strongly not taken and when the values change to the high value the predictor will in three runs through change all the way from strongly not taken to strongly taken.

@ForeverLearning 2017-01-12 01:50:28

This question has already been answered excellently many times over. Still I'd like to draw the group's attention to yet another interesting analysis.

Recently this example (modified very slightly) was also used as a way to demonstrate how a piece of code can be profiled within the program itself on Windows. Along the way, the author also shows how to use the results to determine where the code is spending most of its time in both the sorted & unsorted case. Finally the piece also shows how to use a little known feature of the HAL (Hardware Abstraction Layer) to determine just how much branch misprediction is happening in the unsorted case.

The link is here:

@Peter Mortensen 2018-03-16 12:47:19

That is a very interesting article (in fact, I have just read all of it), but how does it answer the question?

@ForeverLearning 2018-03-16 15:37:16

@PeterMortensen I am a bit flummoxed by your question. For example here is one relevant line from that piece: When the input is unsorted, all the rest of the loop takes substantial time. But with sorted input, the processor is somehow able to spend not just less time in the body of the loop, meaning the buckets at offsets 0x18 and 0x1C, but vanishingly little time on the mechanism of looping. Author is trying to discuss profiling in the context of code posted here and in the process trying to explain why the sorted case is so much more faster.

@rkachach 2015-09-23 14:57:47

In the same line (I think this was not highlighted by any answer) it's good to mention that sometimes (specially in software where the performance matters—like in the Linux kernel) you can find some if statements like the following:

if (likely( everything_is_ok ))
    /* Do something */

or similarly:

if (unlikely(very_improbable_condition))
    /* Do something */    

Both likely() and unlikely() are in fact macros that are defined by using something like the GCC's __builtin_expect to help the compiler insert prediction code to favour the condition taking into account the information provided by the user. GCC supports other builtins that could change the behavior of the running program or emit low level instructions like clearing the cache, etc. See this documentation that goes through the available GCC's builtins.

Normally this kind of optimizations are mainly found in hard-real time applications or embedded systems where execution time matters and it's critical. For example, if you are checking for some error condition that only happens 1/10000000 times, then why not inform the compiler about this? This way, by default, the branch prediction would assume that the condition is false.

@Daniel Fischer 2012-06-27 13:54:45

Branch prediction.

With a sorted array, the condition data[c] >= 128 is first false for a streak of values, then becomes true for all later values. That's easy to predict. With an unsorted array, you pay for the branching cost.

@Adam Freeman 2014-09-23 18:58:12

Does branch prediction work better on sorted arrays vs. arrays with different patterns? For example, for the array --> { 10, 5, 20, 10, 40, 20, ... } the next element in the array from the pattern is 80. Would this kind of array be sped up by branch prediction in which the next element is 80 here if the pattern is followed? Or does it usually only help with sorted arrays?

@Agrim Pathak 2014-10-30 07:51:58

So basically everything I conventionally learned about big-O is out of the window? Better to incur a sorting cost than a branching cost?

@Daniel Fischer 2014-10-30 10:14:12

@AgrimPathak That depends. For not too large input, an algorithm with higher complexity is faster than an algorithm with lower complexity when the constants are smaller for the algorithm with higher complexity. Where the break-even point is can be hard to predict. Also, compare this, locality is important. Big-O is important, but it is not the sole criterion for performance.

@Filip Bartuzi 2014-11-09 13:37:18

When does branch prediction takes place? When does language will know that array is sorted? I'm thinking of situation of array that looks like: [1,2,3,4,5,...998,999,1000, 3, 10001, 10002] ? will this obscure 3 increase running time? Will it be as long as unsorted array?

@Daniel Fischer 2014-11-09 13:49:37

@FilipBartuzi Branch prediction takes place in the processor, below the language level (but the language may offer ways to tell the compiler what's likely, so the compiler can emit code suited to that). In your example, the out-of-order 3 will lead to a branch-misprediction (for appropriate conditions, where 3 gives a different result than 1000), and thus processing that array will likely take a couple dozen or hundred nanoseconds longer than a sorted array would, hardly ever noticeable. What costs time is i high rate of mispredictions, one misprediction per 1000 isn't much.

@Peter Wone 2015-05-01 04:02:56

@AdamFreeman - Sorting is relevant here only inasmuch as in this code it increases branch prediction to 100% success.

@Dr t 2017-05-25 17:51:33

I would recommend a look at:‌​ode/… which provides a good discussion with examples of this topic including some that are not mentioned in any comments that i have seen regarding this question.

@आनंद 2017-09-15 14:16:54

@DanielFischer does compiler knows which array is sorted and which one is not?

@knickum 2017-10-13 21:43:01

@AnandTyagi As Peter Wone noted, it's not that the compiler knows which array is sorted or not. Imagine an extremely simple branch predictor that takes the same path as the previous iteration, e.g. a train taking a left if it took a left last time, and vice versa. For a sorted array of 256 ints, (ignoring the undefined first iteration), the prediction would be correct from 2-128, wrong at 129, and then correct from 130-256. Now, that's a terrible branch predictor that would only work in this specific situation, but a really good predictor should still handle this capably.

@caf 2012-10-12 05:53:33

No doubt some of us would be interested in ways of identifying code that is problematic for the CPU's branch-predictor. The Valgrind tool cachegrind has a branch-predictor simulator, enabled by using the --branch-sim=yes flag. Running it over the examples in this question, with the number of outer loops reduced to 10000 and compiled with g++, gives these results:


==32551== Branches:        656,645,130  (  656,609,208 cond +    35,922 ind)
==32551== Mispredicts:         169,556  (      169,095 cond +       461 ind)
==32551== Mispred rate:            0.0% (          0.0%     +       1.2%   )


==32555== Branches:        655,996,082  (  655,960,160 cond +  35,922 ind)
==32555== Mispredicts:     164,073,152  (  164,072,692 cond +     460 ind)
==32555== Mispred rate:           25.0% (         25.0%     +     1.2%   )

Drilling down into the line-by-line output produced by cg_annotate we see for the loop in question:


          Bc    Bcm Bi Bim
      10,001      4  0   0      for (unsigned i = 0; i < 10000; ++i)
           .      .  .   .      {
           .      .  .   .          // primary loop
 327,690,000 10,016  0   0          for (unsigned c = 0; c < arraySize; ++c)
           .      .  .   .          {
 327,680,000 10,006  0   0              if (data[c] >= 128)
           0      0  0   0                  sum += data[c];
           .      .  .   .          }
           .      .  .   .      }


          Bc         Bcm Bi Bim
      10,001           4  0   0      for (unsigned i = 0; i < 10000; ++i)
           .           .  .   .      {
           .           .  .   .          // primary loop
 327,690,000      10,038  0   0          for (unsigned c = 0; c < arraySize; ++c)
           .           .  .   .          {
 327,680,000 164,050,007  0   0              if (data[c] >= 128)
           0           0  0   0                  sum += data[c];
           .           .  .   .          }
           .           .  .   .      }

This lets you easily identify the problematic line - in the unsorted version the if (data[c] >= 128) line is causing 164,050,007 mispredicted conditional branches (Bcm) under cachegrind's branch-predictor model, whereas it's only causing 10,006 in the sorted version.

Alternatively, on Linux you can use the performance counters subsystem to accomplish the same task, but with native performance using CPU counters.

perf stat ./sumtest_sorted


 Performance counter stats for './sumtest_sorted':

  11808.095776 task-clock                #    0.998 CPUs utilized          
         1,062 context-switches          #    0.090 K/sec                  
            14 CPU-migrations            #    0.001 K/sec                  
           337 page-faults               #    0.029 K/sec                  
26,487,882,764 cycles                    #    2.243 GHz                    
41,025,654,322 instructions              #    1.55  insns per cycle        
 6,558,871,379 branches                  #  555.455 M/sec                  
       567,204 branch-misses             #    0.01% of all branches        

  11.827228330 seconds time elapsed


 Performance counter stats for './sumtest_unsorted':

  28877.954344 task-clock                #    0.998 CPUs utilized          
         2,584 context-switches          #    0.089 K/sec                  
            18 CPU-migrations            #    0.001 K/sec                  
           335 page-faults               #    0.012 K/sec                  
65,076,127,595 cycles                    #    2.253 GHz                    
41,032,528,741 instructions              #    0.63  insns per cycle        
 6,560,579,013 branches                  #  227.183 M/sec                  
 1,646,394,749 branch-misses             #   25.10% of all branches        

  28.935500947 seconds time elapsed

It can also do source code annotation with dissassembly.

perf record -e branch-misses ./sumtest_unsorted
perf annotate -d sumtest_unsorted
 Percent |      Source code & Disassembly of sumtest_unsorted
         :                      sum += data[c];
    0.00 :        400a1a:       mov    -0x14(%rbp),%eax
   39.97 :        400a1d:       mov    %eax,%eax
    5.31 :        400a1f:       mov    -0x20040(%rbp,%rax,4),%eax
    4.60 :        400a26:       cltq   
    0.00 :        400a28:       add    %rax,-0x30(%rbp)

See the performance tutorial for more details.

@TallBrianL 2013-12-09 04:00:09

This is scary, in the unsorted list, there should be 50% chance of hitting the add. Somehow the branch prediction only has a 25% miss rate, how can it do better than 50% miss?

@caf 2013-12-09 04:29:25

@tall.b.lo: The 25% is of all branches - there are two branches in the loop, one for data[c] >= 128 (which has a 50% miss rate as you suggest) and one for the loop condition c < arraySize which has ~0% miss rate.

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