Micro Optimization of a 4-bucket histogram of a large array or list
Solution 1:
This should be possible at about 8 elements (1 AVX2 vector) per 2.5 clock cycles or so (per core) on a modern x86-64 like Skylake or Zen 2, using AVX2. Or per 2 clocks with unrolling. Or on your Piledriver CPU, maybe 1x 16-byte vector of indexes per 3 clocks with AVX1 _mm_cmpeq_epi32.
The general strategy works with 2 to 8 buckets. And for byte, 16-bit, or 32-bit elements. (So byte elements gives you 32 elements histogrammed per 2 clock cycles best case, with a bit of outer-loop overhead to collect byte counters before they overflow.)
Update: or mapping an int to 1UL << (array[i]*8) to increment one of 4 bytes of a counter with SIMD / SWAR addition, we can go close to 1 clock per vector of 8 int on SKL, or per 2 clocks on Zen2. (This is even more specific to 4 or fewer buckets, and int input, and doesn't scale down to SSE2. It needs variable-shifts or at least AVX1 variable-shuffles.) Using byte elements with the first strategy is probably still better in terms of elements per cycle.
As @JonasH points out, you could have different cores working on different parts of the input array. A single core can come close to saturating memory bandwidth on typical desktops, but many-core Xeons have lower per-core memory bandwidth and higher aggregate, and need more cores to saturate L3 or DRAM bandwidth. Why is Skylake so much better than Broadwell-E for single-threaded memory throughput?
A loop that runs for days at a time.
On a single input list that's very very slow to iterate so it still doesn't overflow int counters? Or repeated calls with different large Lists (like your ~900k test array)?
I believe I want to avoid increasing an index for a list or array as it seem to consume a lot of time?
That's probably because you were benchmarking with optimization disabled. Don't do that, it's not meaningful at all; different code is slowed down different amounts by disabling optimization. More explicit steps and tmp vars can often make slower debug-mode code because there are more things that have to be there to look at with a debugger. But they can just optimize into a normal pointer-increment loop when you compile with normal optimization.
Iterating through an array can compile efficiently into asm.
The slow part is the dependency chain through memory for incrementing a variable index of the array. For example on a Skylake CPU, memory-destination add with the same address repeatedly bottlenecks at about one increment per 6 clock cycles because the next add has to wait to load the value stored by the previous one. (Store-forwarding from the store buffer means it doesn't have to wait for it to commit to cache first, but it's still much slower than add into a register.) See also Agner Fog's optimization guides: https://agner.org/optimize/
With counts only distributed across 4 buckets, you'll have a lot of cases where instructions are waiting to reload the data stored by another recent instruction, so you can't even achieve the nearly 1 element per clock cycle you might if counts were well distributed over more counters that still were all hot in L1d cache.
One good solution to this problem is unrolling the loop with multiple arrays of counters. Methods to vectorise histogram in SIMD?. Like instead of int[] indexes = { 0, 0, 0, 0 }; you can make it a 2D array of four counters each. You'd have to manually unroll the loop in the source to iterate over the input array, and handle the last 0..3 left over elements after the unrolled part.
This is a good technique for small to medium arrays of counts, but becomes bad if replicating the counters starts to lead to cache misses.
Use narrow integers to save cache footprint / mem bandwidth.
Another thing you can/should do is use as narrow a type as possible for your arrays of 0..3 values: each number can fit in a byte so using 8-bit integers would save you a factor of 4 cache footprint / memory bandwidth.
x86 can efficiently load/store bytes into to/from full registers. With SSE4.1 you also have SIMD pmovzxbd to make it more efficient to auto-vectorize when you have a byte_array[i] used with an int_array[i] in a loop.
(When I say x86 I mean including x86-64, as opposed to ARM or PowerPC. Of course you don't actually want to compile 32-bit code, what Microsoft calls "x86")
With very small numbers of buckets, like 4
This looks like a job for SIMD compares. With x86 SSE2 the number of int elements per 16-byte vector of data is equal to your number of histogram bins.
You already had a SIMD sort of idea with trying to treat a number as four separate byte elements. See https://en.wikipedia.org/wiki/SIMD#Software
But 00_01_10_11 is just source-level syntax for human-readable separators in numbers, and double is a floating-point type whose internal representation isn't the same as for integers. And you definitely don't want to use strings; SIMD lets you do stuff like operating on 4 elements of an integer array at once.
The best way I can see to approach this is to separately count matches for each of the 4 values, rather than map elements to counters. We want to process multiple elements in parallel but mapping them to counters can have collisions when there are repeated values in one vector of elements. You'd need to increment that counter twice.
The scalar equivalent of this is:
int counts[4] = {0,0,0,0};
for () {
counts[0] += (arr[i] == 0);
counts[1] += (arr[i] == 1);
counts[2] += (arr[i] == 2); // count matches
//counts[3] += (arr[i] == 3); // we assume any that aren't 0..2 are this
}
counts[3] = size - counts[0] - counts[1] - counts[2];
// calculate count 3 from other counts
which (in C++) GCC -O3 will actually auto-vectorize exactly the way I did manually below: https://godbolt.org/z/UJfzuH. Clang even unrolls it when auto-vectorizing, so it should be better than my hand-vectorized version for int inputs. Still not as good as the alternate vpermilps strategy for that case, though.
(And you do still need to manually vectorize if you want byte elements with efficient narrow sums, only widening in an outer loop.)
With byte elements, see How to count character occurrences using SIMD. The element size is too narrow for a counter; it would overflow after 256 counts. So you have to widen either in the inner loop, or use nested loops to do some accumulating before widening.
I don't know C#, so I could write the code in x86 assembly or in C++ with intrinsics. Perhaps C++ intrinsics is more useful for you. C# has some kind of vector extensions that should make it possible to port this.
This is C++ for x86-64, using AVX2 SIMD intrinsics. See https://stackoverflow.com/tags/sse/info for some info.
// Manually vectorized for AVX2, for int element size
// Going nearly 4x as fast should be possible for byte element size
#include <immintrin.h>
void count_elements_avx2(const std::vector<int> &input, unsigned output_counts[4])
{
__m256i counts[4] = { _mm256_setzero_si256() }; // 4 vectors of zeroed counters
// each vector holds counts for one bucket, to be hsummed at the end
size_t size = input.size();
for(size_t i = 0 ; i<size ; i+=8) { // 8x 32-bit elements per vector
__m256i v = _mm256_loadu_si256((const __m256i*)&input[i]); // unaligned load of 8 ints
for (int val = 0 ; val < 3; val++) {
// C++ compilers will unroll this with 3 vector constants and no memory access
__m256i match = _mm256_cmpeq_epi32(v, _mm256_set1_epi32(val)); // 0 or all-ones aka -1
counts[val] = _mm256_sub_epi32(counts[val], match); // x -= -1 or 0 conditional increment
}
}
// transpose and sum 4 vectors of 8 elements down to 1 vector of 4 elements
__m128i summed_counts = hsum_xpose(counts); // helper function defined in Godbolt link
_mm_storeu_si128((__m128i*)output_counts, summed_counts);
output_counts[3] = size - output_counts[0]
- output_counts[1] - output_counts[2];
// TODO: handle the last size%8 input elements; scalar would be easy
}
This compiles nicely with clang (on the Godbolt compiler explorer). Presumably you can write C# that compiles to similar machine code. If not, consider calling native code from a C++ compiler (or hand-written in asm if you can't get truly optimal code from the compiler). If your real use-case runs as many iterations as your benchmark, that could amortize the extra overhead if the input array doesn't have to get copied.
# from an earlier version of the C++, doing all 4 compares in the inner loop
# clang -O3 -march=skylake
.LBB0_2: # do {
vmovdqu ymm7, ymmword ptr [rcx + 4*rdx] # v = load arr[i + 0..7]
vpcmpeqd ymm8, ymm7, ymm3 # compare v == 0
vpsubd ymm4, ymm4, ymm8 # total0 -= cmp_result
vpcmpeqd ymm8, ymm7, ymm5
vpsubd ymm2, ymm2, ymm8
vpcmpeqd ymm7, ymm7, ymm6 # compare v == 2
vpsubd ymm1, ymm1, ymm7 # total2 -= cmp_result
add rdx, 8 # i += 8
cmp rdx, rax
jb .LBB0_2 # }while(i < size)
Estimated best-case Skylake performance: ~2.5 cycles per vector (8 int or 32 int8_t)
Or 2 with unrolling.
Without AVX2, using only SSE2, you'd have some extra movdqa instructions and only be doing 4 elements per vector. This would still be a win vs. scalar histogram in memory, though. Even 1 element / clock is nice, and should be doable with SSE2 that can run on any x86-64 CPU.
Assuming no cache misses of course, with hardware prefetch into L1d staying ahead of the loop. This might only happen with the data already hot in L2 cache at least. I'm also assuming no stalls from memory alignment; ideally your data is aligned by 32 bytes. If it usually isn't, possibly worth processing the first unaligned part and then using aligned loads, if the array is large enough.
For byte elements, the inner-most loop will look similar (with vpcmpeqb and vpsubb but run only at most 255 (not 256) iterations before hsumming to 64-bit counters, to avoid overflow. So throughput per vector will be the same, but with 4x as many elements per vector.
See https://agner.org/optimize/ and https://uops.info/ for performance analysis details. e.g. vpcmpeqd on uops.info
The inner loop is only 9 fused-domain uops for Haswell/Skylake, so best case front-end bottleneck of about 1 iteration per 2.25 cycles (the pipeline is 4 uops wide). Small-loop effects get in the way somewhat: Is performance reduced when executing loops whose uop count is not a multiple of processor width? - Skylake has its loop buffer disabled by a microcode update for an erratum, but even before that a 9 uop loop ended up issuing slightly worse than one iter per 2.25 cycles on average, let's say 2.5 cycles.
Skylake runs vpsubd on ports 0,1, or 5, and runs vpcmpeqd on ports 0 or 1. So the back-end bottleneck on ports 0,1,5 is 6 vector ALU uops for 3 ports, or 1 iteration per 2 cycles. So the front-end bottleneck dominates. (Ice Lake's wider front-end may let it bottleneck on the back-end even without unrolling; same back-end throughputs there unless you use AVX512...)
If clang had indexed from the end of the array and counted the index up towards zero (since it chose to use an indexed addressing mode anyway) it could have saved a uop for a total of 8 uops = one iter per 2 cycles in the front-end, matching the back-end bottleneck. (Either way, scalar add and macro-fused cmp/jcc, or add/jcc loop branch can run on port 6, and the load doesn't compete for ALU ports.) Uop replays of ALU uops dependent on the load shouldn't be a problem even on cache misses, if ALU uops are the bottleneck there will normally be plenty of older uops just waiting for an execution unit to be ready, not waiting for load data.
Unrolling by 2 would have the same benefit: amortizing that 2 uops of loop overhead. So 16 uops for 2 input vectors. That's a nice multiple of the pipeline width on SKL and IceLake, and the single-uop pipeline width on Zen. Unrolling even more could let the front-end can stay ahead of execution, but with them even any back-end delays will let the front end build up a cushion of uops in the scheduler. This will let it execute loads early enough.
Zen2 has a wider front-end (6 uops or 5 instructions wide, IIUC). None of these instructions are multi-uop because Zen2 widened the vector ALUs to 256-bit, so that's 5 single-uop instructions. vpcmpeq* runs on FP 0,1, or 3, same as vpsubd, so the back-end bottleneck is the same as on Skylake: 1 vector per 2 cycles. But the wider front-end removes that bottleneck, leaving the critical path being the back-end even without unrolling.
Zen1 takes 2 uops per 256-bit vector operation (or more for lane-crossing, but these are simple 2 uop). So presumably 12/3 = 4 cycles per vector of 8 or 32 elements, assuming it can get those uops through the front-end efficiently.
I'm assuming that the 1-cycle latency dependency chains through the count vectors are scheduled well by the back-ends and don't result in many wasted cycles. Probably not a big deal, especially if you have any memory bottlenecks in real life. (On Piledriver, SIMD-integer operations have 2 cycle latency, but 6 ALU uops for 2 vector ALU ports that can run them is 1 vector (128-bit) per 3 cycles so even without unrolling there's enough work to hide that latency.)
I didn't analyze the horizontal-sum part of this. It's outside the loop so it only has to run once per call. You did tag this micro-optimization, but we probably don't need to worry about that part.
Other numbers of buckets
The base case of this strategy is 2 buckets: count matches for one thing, count_other = size - count.
We know that every element is one of these 4 possibilities, so we can assume that any x that isn't 0, 1, or 2 is a 3 without checking. This means we don't have to count matches for 3 at all, and can get the count for that bucket from size - sum(counts[0..2]).
(See the edit history for the above perf analysis before doing this optimizations. I changed the numbers after doing this optimization and updating the Godbolt link, hopefully I didn't miss anything.)
AVX512 on Skylake-Xeon
For 64-byte vectors there is no vpcmpeqd to make a vector of all-zero (0) or all-one (-1) elements. Instead you'd compare into a mask register and use that to do a merge-masked add of set1(1). Like c = _mm512_mask_add_epi32(c, _mm512_set1_epi32(1)).
Unfortunately it's not efficient to do a scalar popcount of the compare-result bitmasks.
Random code review: in your first benchmark:
int[] valueLIST = indexers.ToArray();
This seems pointless; According to MS's docs (https://docs.microsoft.com/en-us/dotnet/standard/collections/), a List is efficiently indexable. I think it's equivalent to C++ std::vector<T>. You can just iterate it without copying to an array.
Alt strategy - map 0..3 to a set a bit in one byte of an int
Good if you can't narrow your elements to bytes for the input to save mem bandwidth.
But speaking of which, maybe worth it to use 2x _mm256_packs_epi32 (vpackssdw) and _mm256_packs_epi16 (vpacksswb) to narrow down to 8-bit integers before counting with 3x pcmpeqb / psubb. That costs 3 uops per 4 input vectors to pack down to 1 with byte elements.
But if your input has int elements to start with, this may be best instead of packing and then comparing 3 ways.
You have 4 buckets, and an int has 4 bytes. If we can transform each int element to a 1 at the bottom of the appropriate byte, that would let us add with _mm256_add_epi8 for up to 255 inner-loop iterations before widening to 64-bit counters. (With the standard _mm256_sad_epu8 against zero trick to hsum unsigned bytes without overflow.)
There are 2 ways to do this. The first: use a shuffle as a lookup table. AVX2 vpermd works (_mm256_permutexvar_epi32) using the data as the index vector and a constant _mm256_set_epi32(0,0,0,0, 1UL<<24, 1UL<<16, 1UL<<8, 1UL<<0) as the data being shuffled. Or type-pun the vector to use AVX1 vpermilps as a LUT with the LUT vector having those bytes in the upper half as well.
vpermilps is better: it's fewer uops on AMD Zen 1, and lower latency everywhere because it's in-lane. (Might cause a bypass delay on some CPUs, cutting into the latency benefit, but still not worse than vpermd).
For some reason vpermilps with a vector control has 2 cycle throughput on Zen2 even though it's still a single uop. Or 4 cycles on Zen1 (for the 2 uop YMM version). It's 1 cycle on Intel. vpermd is even worse on AMD: more uops and same poor throughput.
vpermilps xmm (16-byte vector) on Piledriver has 1/clock throughput according to Agner Fog's testing, and runs in the "ivec" domain. (So it actually has extra bypass delay latency when used on the "intended" floating point operands, but not on integer).
// Or for Piledriver, __m128 version of this
__m256 bytepatterns = _mm256_casts256_ps(_mm256_set_epi32(
1<<24, 1<<16, 1<<8, 1<<0,
1<<24, 1<<16, 1<<8, 1<<0) );
__m256i v = _mm256_loadu_si256((const __m256i*)&input[i]);
v = _mm256_castps_si256(_mm256_permutevar_ps(bytepatterns, v)); // vpermilps 32-bit variable shuffle
counts = _mm256_add_epi8(counts, v);
// after some inner iterations, separate out the
// set1_epi32(0x000000ff) counts, 0x0000ff00 counts, etc.
This will produce interleaved counters inside each int element. They will overflow if you don't accumulate them before 256 counts. See How to count character occurrences using SIMD for a simple version of that with a single counter.
Here we might unroll and use 2 different LUT vectors so when we want to group all the counts for 0 together, we could blend 2 vectors together and mask away the others.
Alternatively to shuffling, we can do this with AVX2 variable shifts.
sums += 1UL << (array[i]*8); where the *8 is the number of bits in a byte, also done with a shift. I wrote it as a scalar C++ expression because now's your chance to see how your bytes-in-an-integer idea can really work. As long as we don't let an individual byte overflow, it doesn't matter if SIMD bytes adds block carry between bytes or if we use 32-bit dword elements.
We'd do this with AVX2 as:
__m256i v = loadu...();
v = _mm256_slli_epi32(v, 3); // v *= 8
v = _mm256_sllv_epi32(_mm256_set1_epi32(1), v);
counts = _mm256_add_epi8(counts, v);
This is 2 shift instructions plus the vpaddb. On Skylake the variable-count shifts vpsllvd is cheap: single-uop and runs on multiple ports. But on Haswell and Zen it's slower. (Same throughput as vpermilps on AMD)
And 2 uops for 2 ports still doesn't beat 1 uop for 1 port for the shuffle version. (Unless you use both strategies alternating to distribute the work over all ALU ports on SKL.)
So either way the inner-most loop can go 1 vector per clock or maybe slightly better with careful interleaving of shift vs. shuffle methods.
But it will require some small amount of overhead amortized over 128 or 255 inner loop iterations.
That cleanup at the end might blend 2 vectors together to get a vector with counts for just 2 buckets, then vpshufb (_mm256_shuffle_epi8) to group byte counters for the same bucket into the same qwords. Then vpsadbw (_mm256_sad_epu8) against zero can horizontal sum those byte elements within each qword for _mm256_add_epi64. So outer-loop work should be 2 vpblendw, 2x vpshufb, 2x vpsadbw, 2x vpaddq then back into another 255 iterations of the inner loop. Probably also checking if you're within 255 iterations of the end of the array to set the loop bound for the inner iteration.
Solution 2:
As mentioned by Peter Cordes, you could use SIMD to add multiple values together at a time, See vector. But it is not clear to me if this would actually help.
Edit: If you are running .Net core there are also SIMD intrinstics that provides lower level access to the hardware.
As mentioned by NerualHandle it might be better to use a for-loop than a foreach. But when I test it there does not seem to be a significant difference. I would guess the compiler can optimize foreach in this particular case.
When I am running your testbenchmark00 code it completes in ~6ms on my computer. Some rough calculations suggest each iteration of the loop takes about 0.78ns, or about 2-4 processor cycles, this seem to be near optimal. It seem odd that it takes ~20 times longer for you. Are you running in release mode?
You could parallelize the problem. Split the indexers array into multiple parts, and build the historgram for each part on different threads, and sum the historgram for each thread in the end. See Parallel.For since this can do the partitioning etc for you, but it requires the use of localInit and localFinally to ensure each thread writes to separate histograms to avoid concurrency issues.
As always with performance optimization, the recommended order to do things is:
- Profile code to identify problem areas
- Look for algorithmic improvements
- Look for ways to do less work, like caching
- Do existing work faster
Solution 3:
This is the untested C# version of @PeterCordes answer.
private static Vector128<int> HsumTranspose( ReadOnlySpan<Vector256<int>> counts )
{
var sum01 = Avx2.HorizontalAdd( counts[ 0 ], counts[ 1 ] );
var sum23 = Avx2.HorizontalAdd( counts[ 2 ], counts[ 3 ] );
var sum0123 = Avx2.HorizontalAdd( sum01, sum23 );
var sumHigh = Avx2.ExtractVector128( sum0123, 1 );
var sumLow = Avx2.ExtractVector128( sum0123, 0 );
return Sse2.Add( sumHigh, sumLow );
}
private unsafe static int[ ] CountElements( ReadOnlySpan<int> input )
{
var outputCounts = new int[ 4 ];
// Four vectors of zeroed counters each vector holds
// counts for one bucket, to be hsummed at the end.
Span<Vector256<int>> counts = stackalloc Vector256<int>[ 4 ]
{
Vector256<int>.Zero,
Vector256<int>.Zero,
Vector256<int>.Zero,
Vector256<int>.Zero
};
unsafe
{
fixed ( int* fixedInput = input )
{
var size = input.Length;
for ( var i = 0; i < size; i += 8 )
{
var v = Avx.LoadVector256( &fixedInput[ i ] );
for ( var val = 0; val < 3; val++ )
{
var match = Avx2.CompareEqual( v, Vector256.Create( val ) );
counts[ val ] = Avx2.Subtract( counts[ val ], match );
}
}
Vector128<int> summedCounts = HsumTranspose( counts );
fixed ( int* fixedOutputCounts = outputCounts )
Sse2.Store( fixedOutputCounts, summedCounts );
outputCounts[ 3 ] = size - outputCounts[ 0 ] -
outputCounts[ 1 ] - outputCounts[ 2 ];
// TODO: handle the last size%8 input elements; scalar would be easy
}
}
}
return outputCounts;
}
Solution 4:
I have tried to rewrite the code for Vector128<byte> and have come up with this code.
I have first created indexesToSumFirst which is the number of iterations so the remaining will be a multiple of 16 to be consumed exactly by the following loops.
I have created 3 loops where an innerloop exists of 16x16 = 256 to not create any overflow for byte. Then the "outerloop" has an exact count that is calculated from before to maintain this.
After those 3 loops. The rest which is below 16*16 iterations is summed up in its own loop.
When I runned a benchmark between: normalCalculation and CountElements the CountElements SIMD approach is about 7.2 times faster.
void calc()
{
//Create 16 indexes with numbers between: 0-3. The goal is to count how many of those occurences we have for the numbers: 0-3
int times = 6250;
int bytes = times * 16;
byte[] v1 = new byte[bytes];
for (int i = 0; i < times; i++)
{
v1[0 + (i * 16)] = 0;
v1[1 + (i * 16)] = 1;
v1[2 + (i * 16)] = 2;
v1[3 + (i * 16)] = 3;
v1[4 + (i * 16)] = 1;
v1[5 + (i * 16)] = 1;
v1[6 + (i * 16)] = 1;
v1[7 + (i * 16)] = 1;
v1[8 + (i * 16)] = 1;
v1[9 + (i * 16)] = 0;
v1[10 + (i * 16)] = 0;
v1[11 + (i * 16)] = 3;
v1[12 + (i * 16)] = 1;
v1[13 + (i * 16)] = 1;
v1[14 + (i * 16)] = 1;
v1[15 + (i * 16)] = 3;
}
/*---------------*/
ReadOnlySpan<byte> input = v1;
//Call function
//normalCalculation(input);
CountElements(input);
}
void normalCalculation(ReadOnlySpan<byte> inputArray)
{
int[] countArray0 = new int[4];
for (int i = 0; i < inputArray.Length; i++)
{
countArray0[inputArray[i]]++;
}
}
private unsafe static int[] CountElements(ReadOnlySpan<byte> inputArray)
{
//100000 indexes (This SIMD code goes 7.2 times faster than normal C# code)
double[] countArray = new double[4];
double arraylength = inputArray.Length; int loops = Convert.ToInt32(arraylength);
double loopcount = arraylength / 3840; //100000 / 240 * 16 = 26.04
double indexesToSumFirst = loopcount - Math.Floor(loopcount); //26.04 - 26 = 0.04
indexesToSumFirst = indexesToSumFirst * 3840; //Num of indexes to be SUMMED first
loopcount = arraylength - indexesToSumFirst; //100000 - 153.6 = 99846.4
int outerloop = Convert.ToInt32(loopcount / 3840); //24
//Sum the first indexes first. So the loops after those are exactly counts of: x16
int index = Convert.ToInt32(indexesToSumFirst);
if (index > 0)
{
for (int t = 0; t < index; t++)
{
countArray[inputArray[t]]++;
}
}
//Below starts the SIMD calculations!
Span<Vector128<byte>> counts = stackalloc Vector128<byte>[3];
Span<Vector128<UInt64>> sum64 = stackalloc Vector128<UInt64>[3];
unsafe
{
fixed (byte* fixedInput = inputArray)
{
for (int i = 0; i < outerloop; i++)
{
counts.Clear();
for (int i2 = 0; i2 < 240; i2++)
{
var v = Avx.LoadVector128(&fixedInput[index]);
for (byte val = 0; val < 3; val++)
{
var match = Avx.CompareEqual(v, Vector128.Create(val)); //[1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0] == [1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]
counts[val] = Avx.Subtract(counts[val], match);
}
index += 16;
}
//Here sum
for (int i3 = 0; i3 < 3; i3++)
{
//SumAbsoluteDifferences
sum64[i3] = Sse2.Add(sum64[i3], Sse2.SumAbsoluteDifferences(counts[i3], Vector128<byte>.Zero).AsUInt64()); //sum64: <2,0,0,0,3,0,0,0>
}
}
//UnpackHigh and get the lower element from the Vector128<UInt64>
if (outerloop > 0)
{
for (int i3 = 0; i3 < 3; i3++)
{
Vector128<UInt64> upper = Sse2.UnpackHigh(sum64[i3], sum64[i3]).AsUInt64(); //3
countArray[i3] += Sse2.Add(sum64[i3], upper).ToScalar();
}
}
//Calculate the last index
countArray[3] = loops - countArray[0] - countArray[1] - countArray[2];
}
}
var outputCounts = new int[4];
return outputCounts;
}