I was looking for the fastest way to popcount large arrays of data. I encountered a very weird effect: Changing the loop variable from unsigned to uint64_t made the performance drop by 50% on my PC.

The Benchmark

#include <iostream>
#include <chrono>
#include <x86intrin.h>

int main(int argc, char* argv[]) {

    using namespace std;
    if (argc != 2) {
       cerr << "usage: array_size in MB" << endl;
       return -1;
    }

    uint64_t size = atol(argv[1])<<20;
    uint64_t* buffer = new uint64_t[size/8];
    char* charbuffer = reinterpret_cast<char*>(buffer);
    for (unsigned i=0; i<size; ++i)
        charbuffer[i] = rand()%256;

    uint64_t count,duration;
    chrono::time_point<chrono::system_clock> startP,endP;
    {
        startP = chrono::system_clock::now();
        count = 0;
        for( unsigned k = 0; k < 10000; k++){
            // Tight unrolled loop with unsigned
            for (unsigned i=0; i<size/8; i+=4) {
                count += _mm_popcnt_u64(buffer[i]);
                count += _mm_popcnt_u64(buffer[i+1]);
                count += _mm_popcnt_u64(buffer[i+2]);
                count += _mm_popcnt_u64(buffer[i+3]);
            }
        }
        endP = chrono::system_clock::now();
        duration = chrono::duration_cast<std::chrono::nanoseconds>(endP-startP).count();
        cout << "unsigned	" << count << '	' << (duration/1.0E9) << " sec 	"
             << (10000.0*size)/(duration) << " GB/s" << endl;
    }
    {
        startP = chrono::system_clock::now();
        count=0;
        for( unsigned k = 0; k < 10000; k++){
            // Tight unrolled loop with uint64_t
            for (uint64_t i=0;i<size/8;i+=4) {
                count += _mm_popcnt_u64(buffer[i]);
                count += _mm_popcnt_u64(buffer[i+1]);
                count += _mm_popcnt_u64(buffer[i+2]);
                count += _mm_popcnt_u64(buffer[i+3]);
            }
        }
        endP = chrono::system_clock::now();
        duration = chrono::duration_cast<std::chrono::nanoseconds>(endP-startP).count();
        cout << "uint64_t	"  << count << '	' << (duration/1.0E9) << " sec 	"
             << (10000.0*size)/(duration) << " GB/s" << endl;
    }

    free(charbuffer);
}

As you see, we create a buffer of random data, with the size being x megabytes where x is read from the command line. Afterwards, we iterate over the buffer and use an unrolled version of the x86 popcount intrinsic to perform the popcount. To get a more precise result, we do the popcount 10,000 times. We measure the times for the popcount. In the upper case, the inner loop variable is unsigned, in the lower case, the inner loop variable is uint64_t. I thought that this should make no difference, but the opposite is the case.

The (absolutely crazy) results

I compile it like this (g++ version: Ubuntu 4.8.2-19ubuntu1):

g++ -O3 -march=native -std=c++11 test.cpp -o test

Here are the results on my Haswell Core i7-4770K CPU @ 3.50 GHz, running test 1 (so 1 MB random data):

• unsigned 41959360000 0.401554 sec 26.113 GB/s
• uint64_t 41959360000 0.759822 sec 13.8003 GB/s

As you see, the throughput of the uint64_t version is only half the one of the unsigned version! The problem seems to be that different assembly gets generated, but why? First, I thought of a compiler bug, so I tried clang++ (Ubuntu Clang version 3.4-1ubuntu3):

clang++ -O3 -march=native -std=c++11 teest.cpp -o test

Result: test 1

• unsigned 41959360000 0.398293 sec 26.3267 GB/s
• uint64_t 41959360000 0.680954 sec 15.3986 GB/s

So, it is almost the same result and is still strange. But now it gets super strange. I replace the buffer size that was read from input with a constant 1, so I change:

uint64_t size = atol(argv[1]) << 20;

to

uint64_t size = 1 << 20;

Thus, the compiler now knows the buffer size at compile time. Maybe it can add some optimizations! Here are the numbers for g++:

• unsigned 41959360000 0.509156 sec 20.5944 GB/s
• uint64_t 41959360000 0.508673 sec 20.6139 GB/s

Now, both versions are equally fast. However, the unsigned got even slower! It dropped from 26 to 20 GB/s, thus replacing a non-constant by a constant value lead to a deoptimization. Seriously, I have no clue what is going on here! But now to clang++ with the new version:

• unsigned 41959360000 0.677009 sec 15.4884 GB/s
• uint64_t 41959360000 0.676909 sec 15.4906 GB/s

Wait, what? Now, both versions dropped to the slow number of 15 GB/s. Thus, replacing a non-constant by a constant value even lead to slow code in both cases for Clang!

I asked a colleague with an Ivy Bridge CPU to compile my benchmark. He got similar results, so it does not seem to be Haswell. Because two compilers produce strange results here, it also does not seem to be a compiler bug. We do not have an AMD CPU here, so we could only test with Intel.

More madness, please!

Take the first example (the one with atol(argv[1])) and put a static before the variable, i.e.:

static uint64_t size=atol(argv[1])<<20;

Here are my results in g++:

• unsigned 41959360000 0.396728 sec 26.4306 GB/s
• uint64_t 41959360000 0.509484 sec 20.5811 GB/s

Yay, yet another alternative. We still have the fast 26 GB/s with u32, but we managed to get u64 at least from the 13 GB/s to the 20 GB/s version! On my collegue's PC, the u64 version became even faster than the u32 version, yielding the fastest result of all. Sadly, this only works for g++, clang++ does not seem to care about static.

My question

Can you explain these results? Especially:

• How can there be such a difference between u32 and u64?
• How can replacing a non-constant by a constant buffer size trigger less optimal code?
• How can the insertion of the static keyword make the u64 loop faster? Even faster than the original code on my collegue's computer!

I know that optimization is a tricky territory, however, I never thought that such small changes can lead to a 100% difference in execution time and that small factors like a constant buffer size can again mix results totally. Of course, I always want to have the version that is able to popcount 26 GB/s. The only reliable way I can think of is copy paste the assembly for this case and use inline assembly. This is the only way I can get rid of compilers that seem to go mad on small changes. What do you think? Is there another way to reliably get the code with most performance?

The Disassembly

Here is the disassembly for the various results:

26 GB/s version from g++ / u32 / non-const bufsize:

0x400af8:
lea 0x1(%rdx),%eax
popcnt (%rbx,%rax,8),%r9
lea 0x2(%rdx),%edi
popcnt (%rbx,%rcx,8),%rax
lea 0x3(%rdx),%esi
add %r9,%rax
popcnt (%rbx,%rdi,8),%rcx
add $0x4,%edx
add %rcx,%rax
popcnt (%rbx,%rsi,8),%rcx
add %rcx,%rax
mov %edx,%ecx
add %rax,%r14
cmp %rbp,%rcx
jb 0x400af8

13 GB/s version from g++ / u64 / non-const bufsize:

0x400c00:
popcnt 0x8(%rbx,%rdx,8),%rcx
popcnt (%rbx,%rdx,8),%rax
add %rcx,%rax
popcnt 0x10(%rbx,%rdx,8),%rcx
add %rcx,%rax
popcnt 0x18(%rbx,%rdx,8),%rcx
add $0x4,%rdx
add %rcx,%rax
add %rax,%r12
cmp %rbp,%rdx
jb 0x400c00

15 GB/s version from clang++ / u64 / non-const bufsize:

0x400e50:
popcnt (%r15,%rcx,8),%rdx
add %rbx,%rdx
popcnt 0x8(%r15,%rcx,8),%rsi
add %rdx,%rsi
popcnt 0x10(%r15,%rcx,8),%rdx
add %rsi,%rdx
popcnt 0x18(%r15,%rcx,8),%rbx
add %rdx,%rbx
add $0x4,%rcx
cmp %rbp,%rcx
jb 0x400e50

20 GB/s version from g++ / u32&u64 / const bufsize:

0x400a68:
popcnt (%rbx,%rdx,1),%rax
popcnt 0x8(%rbx,%rdx,1),%rcx
add %rax,%rcx
popcnt 0x10(%rbx,%rdx,1),%rax
add %rax,%rcx
popcnt 0x18(%rbx,%rdx,1),%rsi
add $0x20,%rdx
add %rsi,%rcx
add %rcx,%rbp
cmp $0x100000,%rdx
jne 0x400a68

15 GB/s version from clang++ / u32&u64 / const bufsize:

0x400dd0:
popcnt (%r14,%rcx,8),%rdx
add %rbx,%rdx
popcnt 0x8(%r14,%rcx,8),%rsi
add %rdx,%rsi
popcnt 0x10(%r14,%rcx,8),%rdx
add %rsi,%rdx
popcnt 0x18(%r14,%rcx,8),%rbx
add %rdx,%rbx
add $0x4,%rcx
cmp $0x20000,%rcx
jb 0x400dd0

Interestingly, the fastest (26 GB/s) version is also the longest! It seems to be the only solution that uses lea. Some versions use jb to jump, others use jne. But apart from that, all versions seem to be comparable. I don't see where a 100% performance gap could originate from, but I am not too adept at deciphering assembly. The slowest (13 GB/s) version looks even very short and good. Can anyone explain this?

Lessons learned

No matter what the answer to this question will be; I have learned that in really hot loops every detail can matter, even details that do not seem to have any association to the hot code. I have never thought about what type to use for a loop variable, but as you see such a minor change can make a 100% difference! Even the storage type of a buffer can make a huge difference, as we saw with the insertion of the static keyword in front of the size variable! In the future, I will always test various alternatives on various compilers when writing really tight and hot loops that are crucial for system performance.

The interesting thing is also that the performance difference is still so high although I have already unrolled the loop four times. So even if you unroll, you can still get hit by major performance deviations. Quite interesting.