#### [SOLVED] Fastest way to do horizontal SSE vector sum (or other reduction)

Given a vector of three (or four) floats. What is the fastest way to sum them?

Is SSE (movaps, shuffle, add, movd) always faster than x87? Are the horizontal-add instructions in SSE3 worth it?

What's the cost to moving to the FPU, then faddp, faddp? What's the fastest specific instruction sequence?

"Try to arrange things so you can sum four vectors at a time" will not be accepted as an answer. :-) e.g. for summing an array, you can use multiple vector accumulators for vertical sums (to hide addps latency), and reduce down to one after the loop, but then you need to horizontally sum that last vector.

#### @Peter Cordes 2016-02-08 12:46:06

In general for any kind of vector horizontal reduction, extract / shuffle high half to low, then vertical add (or min/max/or/and/xor/multiply/whatever); repeat until a single element is left. If you start with vectors wider than 128-bit, narrow in half until you get to 128 (then you can use one of the functions in this answer on that vector). Unless you need the result broadcast to all elements at the end, then you can consider doing full-width shuffles all the way.

Related Q&As for wider vectors, and integers: FP

Integer

### Main answer to this question: mostly float and `__m128`

Here are some versions tuned based on Agner Fog's microarch guide's microarch guide and instruction tables. See also the tag wiki. They should be efficient on any CPU, with no major bottlenecks. (e.g. I avoided things that would help one uarch a bit but be slow on another uarch). Code-size is also minimized.

The common SSE3 / SSSE3 2x `hadd` idiom is only good for code-size, not speed on any existing CPUs. There are use-cases for it (like transpose and add, see below), but a single vector isn't one of them.

I've also included an AVX version. Any kind of horizontal reduction with AVX / AVX2 should start with a `vextractf128` and a "vertical" operation to reduce down to one XMM (`__m128`) vector. In general for wide vectors, your best bet is to narrow in half repeatedly until you're down to a 128-bit vector, regardless of element type. (Except for 8-bit integer, then `vpsadbw` as a first step if you want to hsum without overflow to wider elements.)

See the asm output from all this code on the Godbolt Compiler Explorer. See also my improvements to Agner Fog's C++ Vector Class Library `horizontal_add` functions. (message board thread, and code on github). I used CPP macros to select optimal shuffles for code-size for SSE2, SSE4, and AVX, and for avoiding `movdqa` when AVX isn't available.

• code size: smaller is better for L1 I-cache reasons, and for code fetch from disk (smaller binaries). Total binary size mostly matters for compiler decisions made repeatedly all over a program. If you're bothering to hand-code something with intrinsics, it's worth spending a few code bytes if it gives any speedup for the whole program (be careful of microbenchmarks that make unrolling look good).
• uop-cache size: Often more precious than L1 I\$. 4 single-uop instructions can take less space than 2 `haddps`, so this is highly relevant here.
• latency: Sometimes relevant
• throughput (back-end ports): usually irrelevant, horizontal sums shouldn't be the only thing in an innermost loop. Port pressure matters only as part of the whole loop that contains this.
• throughput (total front-end fused-domain uops): If surrounding code doesn't bottleneck on the same port that the hsum uses, this is a proxy for the impact of the hsum on the throughput of the whole thing.

When a horizontal add is infrequent:

CPUs with no uop-cache might favour 2x `haddps` if it's very rarely used: It's slowish when it does run, but that's not often. Being only 2 instructions minimizes the impact on the surrounding code (I\$ size).

CPUs with a uop-cache will probably favour something that takes fewer uops, even if it's more instructions / more x86 code-size. Total uops cache-lines used is what we want to minimize, which isn't as simple as minimizing total uops (taken branches and 32B boundaries always start a new uop cache line).

Anyway, with that said, horizontal sums come up a lot, so here's my attempt at carefully crafting some versions that compile nicely. Not benchmarked on any real hardware, or even carefully tested. There might be bugs in the shuffle constants or something.

If you're making a fallback / baseline version of your code, remember that only old CPUs will run it; newer CPUs will run your AVX version, or SSE4.1 or whatever.

Old CPUs like K8, and Core2(merom) and earlier only have 64bit shuffle units. Core2 has 128bit execution units for most instructions, but not for shuffles. (Pentium M and K8 handle all 128b vector instructions as two 64bit halves).

Shuffles like `movhlps` that move data in 64-bit chunks (no shuffling within 64-bit halves) are fast, too.

Related: shuffles on new CPUs, and tricks for avoiding 1/clock shuffle throughput bottleneck on Haswell and later: Do 128bit cross lane operations in AVX512 give better performance?

On old CPUs with slow shuffles:

• `movhlps` (Merom: 1uop) is significantly faster than `shufps` (Merom: 3uops). On Pentium-M, cheaper than `movaps`. Also, it runs in the FP domain on Core2, avoiding the bypass delays from other shuffles.
• `unpcklpd` is faster than `unpcklps`.
• `pshufd` is slow, `pshuflw`/`pshufhw` are fast (because they only shuffle a 64bit half)
• `pshufb mm0` (MMX) is fast, `pshufb xmm0` is slow.
• `haddps` is very slow (6uops on Merom and Pentium M)
• `movshdup` (Merom: 1uop) is interesting: It's the only 1uop insn that shuffles within 64b elements.

`shufps` on Core2(including Penryn) brings data into the integer domain, causing a bypass delay to get it back to the FP execution units for `addps`, but `movhlps` is entirely in the FP domain. `shufpd` also runs in the float domain.

`movshdup` runs in the integer domain, but is only one uop.

AMD K10, Intel Core2(Penryn/Wolfdale), and all later CPUs, run all xmm shuffles as a single uop. (But note the bypass delay with `shufps` on Penryn, avoided with `movhlps`)

Without AVX, avoiding wasted `movaps`/`movdqa` instructions requires careful choice of shuffles. Only a few shuffles work as a copy-and-shuffle, rather than modifying the destination. Shuffles that combine data from two inputs (like `unpck*` or `movhlps`) can be used with a tmp variable that's no longer needed instead of `_mm_movehl_ps(same,same)`.

Some of these can be made faster (save a MOVAPS) but uglier / less "clean" by taking a dummy arg for use as a destination for an initial shuffle. For example:

``````// Use dummy = a recently-dead variable that vec depends on,
//  so it doesn't introduce a false dependency,
//  and the compiler probably still has it in a register
__m128d highhalf_pd(__m128d dummy, __m128d vec) {
#ifdef __AVX__
// With 3-operand AVX instructions, don't create an extra dependency on something we don't need anymore.
(void)dummy;
return _mm_unpackhi_pd(vec, vec);
#else
// Without AVX, we can save a MOVAPS with MOVHLPS into a dead register
__m128 tmp = _mm_castpd_ps(dummy);
__m128d high = _mm_castps_pd(_mm_movehl_ps(tmp, _mm_castpd_ps(vec)));
return high;
#endif
}
``````

## SSE1 (aka SSE):

``````float hsum_ps_sse1(__m128 v) {                                  // v = [ D C | B A ]
__m128 shuf   = _mm_shuffle_ps(v, v, _MM_SHUFFLE(2, 3, 0, 1));  // [ C D | A B ]
__m128 sums   = _mm_add_ps(v, shuf);      // sums = [ D+C C+D | B+A A+B ]
shuf          = _mm_movehl_ps(shuf, sums);      //  [   C   D | D+C C+D ]  // let the compiler avoid a mov by reusing shuf
return    _mm_cvtss_f32(sums);
}
# gcc 5.3 -O3:  looks optimal
movaps  xmm1, xmm0     # I think one movaps is unavoidable, unless we have a 2nd register with known-safe floats in the upper 2 elements
shufps  xmm1, xmm0, 177
movhlps xmm1, xmm0     # note the reuse of shuf, avoiding a movaps

# clang 3.7.1 -O3:
movaps  xmm1, xmm0
shufps  xmm1, xmm1, 177
movaps  xmm0, xmm1
shufpd  xmm0, xmm0, 1
``````

I reported a clang bug about pessimizing the shuffles. It has its own internal representation for shuffling, and turns that back into shuffles. gcc more often uses the instructions that directly match the intrinsic you used.

Often clang does better than gcc, in code where the instruction choice isn't hand-tuned, or constant-propagation can simplify things even when the intrinsics are optimal for the non-constant case. Overall it's a good thing that compilers work like a proper compiler for intrinsics, not just an assembler. Compilers can often generate good asm from scalar C that doesn't even try to work the way good asm would. Eventually compilers will treat intrinsics as just another C operator as input for the optimizer.

## SSE3

``````float hsum_ps_sse3(__m128 v) {
__m128 shuf = _mm_movehdup_ps(v);        // broadcast elements 3,1 to 2,0
shuf        = _mm_movehl_ps(shuf, sums); // high half -> low half
return        _mm_cvtss_f32(sums);
}

# gcc 5.3 -O3: perfectly optimal code
movshdup    xmm1, xmm0
movhlps     xmm1, xmm0
``````

• doesn't require any `movaps` copies to work around destructive shuffles (without AVX): `movshdup xmm1, xmm2`'s destination is write-only, so it creates `tmp` out of a dead register for us. This is also why I used `movehl_ps(tmp, sums)` instead of `movehl_ps(sums, sums)`.

• small code-size. The shuffling instructions are small: `movhlps` is 3 bytes, `movshdup` is 4 bytes (same as `shufps`). No immediate byte is required, so with AVX, `vshufps` is 5 bytes but `vmovhlps` and `vmovshdup` are both 4.

I could save another byte with `addps` instead of `addss`. Since this won't be used inside inner loops, the extra energy to switch the extra transistors is probably negligible. FP exceptions from the upper 3 elements aren't a risk, because all elements hold valid FP data. However, clang/LLVM actually "understands" vector shuffles, and emits better code if it knows that only the low element matters.

Like the SSE1 version, adding the odd elements to themselves may cause FP exceptions (like overflow) that wouldn't happen otherwise, but this shouldn't be a problem. Denormals are slow, but IIRC producing a +Inf result isn't on most uarches.

## SSE3 optimizing for code-size

If code-size is your major concern, two `haddps` (`_mm_hadd_ps`) instructions will do the trick (Paul R's answer). This is also the easiest to type and remember. It is not fast, though. Even Intel Skylake still decodes each `haddps` to 3 uops, with 6 cycle latency. So even though it saves machine-code bytes (L1 I-cache), it takes up more space in the more-valuable uop-cache. Real use-cases for `haddps`: a transpose-and-sum problem, or doing some scaling at an intermediate step in this SSE `atoi()` implementation.

## AVX:

This version saves a code byte vs. Marat's answer to the AVX question.

``````#ifdef __AVX__
float hsum256_ps_avx(__m256 v) {
__m128 vlow  = _mm256_castps256_ps128(v);
__m128 vhigh = _mm256_extractf128_ps(v, 1); // high 128
return hsum_ps_sse3(vlow);         // and inline the sse3 version, which is optimal for AVX
// (no wasted instructions, and all of them are the 4B minimum)
}
#endif

vmovaps xmm1,xmm0               # huh, what the heck gcc?  Just extract to xmm1
vextractf128 xmm0,ymm0,0x1
vmovshdup xmm1,xmm0
vmovhlps xmm1,xmm1,xmm0
vzeroupper
ret
``````

## Double-precision:

``````double hsum_pd_sse2(__m128d vd) {                      // v = [ B | A ]
__m128 undef  = _mm_undefined_ps();                       // don't worry, we only use addSD, never touching the garbage bits with an FP add
__m128 shuftmp= _mm_movehl_ps(undef, _mm_castpd_ps(vd));  // there is no movhlpd
__m128d shuf  = _mm_castps_pd(shuftmp);
}

# gcc 5.3.0 -O3
pxor    xmm1, xmm1          # hopefully when inlined, gcc could pick a register it knew wouldn't cause a false dep problem, and avoid the zeroing
movhlps xmm1, xmm0

# clang 3.7.1 -O3 again doesn't use movhlps:
xorpd   xmm2, xmm2          # with  #define _mm_undefined_ps _mm_setzero_ps
movapd  xmm1, xmm0
unpckhpd        xmm1, xmm2
movapd  xmm0, xmm1    # another clang bug: wrong choice of operand order

// This doesn't compile the way it's written
double hsum_pd_scalar_sse2(__m128d vd) {
double tmp;
_mm_storeh_pd(&tmp, vd);       // store the high half
double lo = _mm_cvtsd_f64(vd); // cast the low half
return lo+tmp;
}

# gcc 5.3 -O3
haddpd  xmm0, xmm0   # Lower latency but less throughput than storing to memory

# ICC13
movhpd    QWORD PTR [-8+rsp], xmm0    # only needs the store port, not the shuffle unit
``````

Storing to memory and back avoids an ALU uop. That's good if shuffle port pressure, or ALU uops in general, are a bottleneck. (Note that it doesn't need to `sub rsp, 8` or anything because the x86-64 SysV ABI provides a red-zone that signal handlers won't step on.)

Some people store to an array and sum all the elements, but compilers usually don't realize that the low element of the array is still there in a register from before the store.

## Integer:

`pshufd` is a convenient copy-and-shuffle. Bit and byte shifts are unfortunately in-place, and `punpckhqdq` puts the high half of the destination in the low half of the result, opposite of the way `movhlps` can extract the high half into a different register.

Using `movhlps` for the first step might be good on some CPUs, but only if we have a scratch reg. `pshufd` is a safe choice, and fast on everything after Merom.

``````int hsum_epi32_sse2(__m128i x) {
#ifdef __AVX__
__m128i hi64  = _mm_unpackhi_epi64(x, x);           // 3-operand non-destructive AVX lets us save a byte without needing a mov
#else
__m128i hi64  = _mm_shuffle_epi32(x, _MM_SHUFFLE(1, 0, 3, 2));
#endif
__m128i hi32  = _mm_shufflelo_epi16(sum64, _MM_SHUFFLE(1, 0, 3, 2));    // Swap the low two elements
return _mm_cvtsi128_si32(sum32);       // SSE2 movd
//return _mm_extract_epi32(hl, 0);     // SSE4, even though it compiles to movd instead of a literal pextrd r32,xmm,0
}

# gcc 5.3 -O3
pshufd xmm1,xmm0,0x4e
pshuflw xmm1,xmm0,0x4e
movd   eax,xmm0

int hsum_epi32_ssse3_slow_smallcode(__m128i x){
return _mm_cvtsi128_si32(x);
}
``````

On some CPUs, it's safe to use FP shuffles on integer data. I didn't do this, since on modern CPUs that will at most save 1 or 2 code bytes, with no speed gains (other than code size/alignment effects).

#### @plasmacel 2016-11-29 04:32:26

With SSE2 the remaining `movaps` before the `shufps` also can be eliminated if you use `pshufd` by changing `_mm_shuffle_ps(v, v, _MM_SHUFFLE(2, 3, 0, 1));` to `_mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(v), _MM_SHUFFLE(2, 3, 0, 1)));`. However that maybe adds some latency. godbolt.org/g/0trqRY

#### @Peter Cordes 2016-11-29 04:40:37

@plasmacel: on many CPUs, including Intel SnB-family, there's extra bypass-delay latency to forward the result of an FP instruction to an integer shuffle, and from PSHUFD to ADDPS. It's great if you care about throughput and uop count but not latency. (SHUFPS between integer instructions has no penalty on SnB-family (unlike Nehalem), but the reverse is not true.)

#### @Peter Cordes 2016-11-29 04:46:37

@plasmacel: keep in mind that these functions really need to inline to be useful. And yes, clang pessimizes the shuffles sometimes. That's especially bad for first-gen Core2 and other slow-shuffle CPUs where SHUFPS is far worse than MOVHLPS :( If you enable sse3 (godbolt.org/g/1qbNXw), though, it can use MOVSHDUP for Kornel's first shuffle, which is excellent. Anyway, if you are using clang, use whatever happens to coax clang into making nice asm after inlining. You could even write a version which takes a dummy arg to use as a target for movhlps (when AVX isn't available).

#### @Peter Cordes 2016-11-29 04:50:34

If you have a specific microarchitecture and compiler in mind, you can and should make a version that's more optimal for that. This answer tries to be optimal (latency, throughput and code-size) for modern CPUs like Haswell, while sucking at little as possible on old CPUs. i.e. my SSE1 / SSE2 versions don't do anything that's worse on Haswell just to run faster on an old SlowShuffle CPU like Merom. For Merom, PSHUFD might be a win because it and SHUFPS both run in flt->int domain.

#### @plasmacel 2016-11-29 05:29:14

Is it a win to use `vpermilps` instead of `movsldup`, `movshdup`, `movhlps` and `movlhps` when AVX is available? It is a win over `shufps` and looks like clang also tries to emit it instead of the mentioned ones.

#### @Peter Cordes 2016-11-29 05:30:26

@plasmacel: no, unless your vector was in memory to start with, since VPERMILPS can load+shuffle. You get smaller code-size from using the AVX versions of older instructions, because you don't need an immediate, and they only need the 2-byte VEX prefix (`C5 ..` instead of `C4 .. ..`). Two-source shuffles like VSHUFPS and VMOVHLPS aren't any slower than one-source shuffles like VPSHUFD or VPERMILPS. If there's a difference in energy consumption, it's probably negligible.

#### @Peter Cordes 2016-11-29 05:33:26

@plasmacel: As my answer points out, my SSE3 version compiles optimally with AVX, but clang pessimises it to VPERMILPD: godbolt.org/g/ZH88wH. gcc's version is four 4B instructions (not counting the RET). clang's version is 2 bytes longer, and the same speed. What makes you think VPERMILPS is a win over SHUFPS? AFAIK, clang is wrong to favour it for immediate shuffles where the source is already in a register. Agner Fog's tables show no difference. It's useful for load+shuffle, and for variable-shuffles, and maybe easier for compilers since it's a 1-input instruction, but not faster

#### @Peter Cordes 2016-11-30 16:00:10

@plasmacel: fun fact: on Knight's Landing (Xeon Phi = modified silvermont + AVX512), VPERMILPS (3c lat, 1c rtput) is more efficient than VSHUFPS (4c lat, 2c rtput), which does outweight the instruction-length difference for that architecture. I assume that's from being a 1-input shuffle vs 2-input. Agner Fog updated his stuff for KNL. :)

#### @plasmacel 2016-12-01 03:22:42

Thanks for all the provided info here. It's time to detect the `__AVX512F__ ` macro. :)

#### @arrowd 2016-12-05 13:21:13

@PeterCordes Thank you for great answer. Don't you have a typo in `SSE1 (aka SSE)` section at line `_mm_shuffle_ps(v, v, _MM_SHUFFLE(2, 3, 0, 1)); // [ C D | B A ]`? I guess, you meant `[ C D | A B ]`?

#### @Royi 2017-02-06 19:56:15

@PeterCordes, How does your SSE3 solution compares to @PaulR solution - `v = _mm_hadd_ps(v, v); v = _mm_hadd_ps(v, v);`? Thank You.

#### @Peter Cordes 2017-03-24 15:20:46

@Royi: There are already a couple sections in my answer that discuss the fact that _mm_hadd_ps is slow.

#### @jww 2017-12-04 21:41:02

@PeterCordes - Out of curiosity, have you written any books on x86, assembly and intrinsics. I've been looking for a good book (with recipes) for several years now. The intrinsics are important because they are cross-platform. They work on Clang, GCC, MSVC, SunCC, etc. We can write them once and they run everywhere (unlike ASM for GNU's GAS).

#### @Peter Cordes 2017-12-04 22:54:53

@jww: No. I wouldn't want to set anything in stone that I couldn't come back and edit if/when I realize my advice wasn't optimal after all. I did get an email once asking me if I wanted to be part of writing an asm book, but I never got back to them >.< Anyway, collecting up links to the more useful SO answers with "recipes" that I and others have written would be a good project, if I ever got around to it.

## SSE2

### All four:

``````const __m128 t = _mm_add_ps(v, _mm_movehl_ps(v, v));
const __m128 sum = _mm_add_ss(t, _mm_shuffle_ps(t, t, 1));
``````

### r1+r2+r3:

``````const __m128 t1 = _mm_movehl_ps(v, v);
const __m128 t2 = _mm_add_ps(v, t1);
const __m128 sum = _mm_add_ss(t1, _mm_shuffle_ps(t2, t2, 1));
``````

I've found these to be about same speed as double `HADDPS` (but I haven't measured too closely).

#### @Crowley9 2011-08-10 01:41:56

I would definitely give SSE 4.2 a try. If you are doing this multiple times (I assume you are if performance is an issue), you can pre-load a register with (1,1,1,1), and then do several dot4(my_vec(s), one_vec) on it. Yes, it does a superfluous multiply, but those are fairly cheap these days and such an op is likely to be dominated by the horizontal dependencies, which may be more optimized in the new SSE dot product function. You should test to see if it outperforms the double horizontal add Paul R posted.

I also suggest comparing it to straight scalar (or scalar SSE) code - strangely enough it is often faster (usually because internally it is serialized but tightly pipelined using register bypass, where special horizontal instructions may not be fast pathed (yet)) unless you are running SIMT-like code, which it sounds like you are not (otherwise you would do four dot products).

#### @Peter Cordes 2016-02-07 17:20:26

Even in Skylake, one `dpps` is 4 uops, 13c latency. (But one per 1.5c throughput). `haddps` is 3uops, 6c latency. (one per 2c throughput). Store and scalar is not too bad because it doesn't cost many uops, but it's pretty bad for latency compared to Kornel's answer. Scalar ops have the same latency as vector ops, though. Your "tightly pipelined using register bypass" speculation isn't correct. Everything except div is fully pipelined, but you're right that horizontal instructions aren't fast-pathed. They're decoded to internal shuffle uops.

#### @Paul R 2011-08-09 13:35:18

You can do it in two `HADDPS` instructions in SSE3:

``````v = _mm_hadd_ps(v, v);
``````

This puts the sum in all elements.

#### @Jens Björnhager 2011-08-09 14:02:19

Doesn't the sum end up in all elements?

#### @Paul R 2011-08-09 14:05:00

@Jens: yes, thanks - I think you're right - I'll update my answer.

#### @FeepingCreature 2011-08-09 16:19:22

For a 3-vector sum, I'd need to set the fourth component to zero first. What's the fastest way to do that? I'm tending towards "load mask, andps" - is there a fast way to mask out an element?

#### @Paul R 2011-08-09 16:35:29

I don't see any faster way than `ANDPS`, which is one instruction (the mask being constant of course).

#### @awdz9nld 2013-12-20 20:53:19

@FeepingCreature `__m128 vector3 = _mm_castps_si128(_mm_castsi128_ps(_mm_srli_si128(vector4, 4)));` - this may be faster than masking depending on whether your mask is already loaded from memory

#### @Royi 2017-02-06 19:55:27

Hi, How does it compare to @Peter Cordes SSE3 Solution? Thank You.

### [SOLVED] Fastest way to horizontally sum SSE unsigned byte vector

• 2016-05-03 07:50:11
• Chase R Lewis
• 950 View
• 5 Score