Seeking knowledge on array of arrays memory performance - c++

Context: Multichannel real time digital audio processing.
Access pattern: "Column-major", like so:
for (int sample = 0; sample < size; ++sample)
{
for (int channel = 0; channel < size; ++channel)
{
auto data = arr[channel][sample];
// do some computations
}
}
I'm seeking advice on how to make the life easier for the CPU and memory, in general. I realize interleaving the data would be better, but it's not possible.
My theory is, that as long as you sequentially access memory for a while, the CPU will prefetch it - will this hold for N (channel) buffers? What about size of the buffers, any "breaking points"?
Will it be very beneficial to have the channels in contiguous memory (increasing locality), or does that only hold for very small buffers (like, size of cache lines)? We could be talking buffersizes > 100 kb apart.
I guess there would also be a point where the time of the computational part makes memory optimizations negligible - ?
Is this a case, where manual prefetching makes sense?
I could test/profile my own system, but I only have that - 1 system. So any design choices I make may only positively affect that particular system. Any knowledge on these matters are appreciated, links, literature etc., platform specific knowledge.
Let me know if the question is too vague, I primarily thought it would be nice to have some wiki-ish experience / info on this area.
edit:
I created a program, that tests the three cases I mentioned (distant, adjecant and contiguous mentioned in supposedly increasing performance order), which tests these patterns on small and big data sets. Maybe people will run it and report anomalies.
#include <iostream>
#include <chrono>
#include <algorithm>
const int b = 196000;
const int s = 64 / sizeof(float);
const int extra_it = 16;
float sbuf1[s];
float bbuf1[b];
int main()
{
float sbuf2[s];
float bbuf2[b];
float * sbuf3 = new float[s];
float * bbuf3 = new float[b];
float * sbuf4 = new float[s * 3];
float * bbuf4 = new float[b * 3];
float use = 0;
while (1)
{
using namespace std;
int c;
bool sorb;
cout << "small or big test (0/1)? ";
if (!(cin >> sorb))
return -1;
cout << endl << "test distant buffers (0), contiguous access (1) or adjecant access (2)? ";
if (!(cin >> c))
return -1;
auto t = std::chrono::high_resolution_clock::now();
if (c == 0)
{
// "worst case scenario", 3 distant buffers constantly touched
if (sorb)
{
for (int k = 0; k < b * extra_it; ++k)
for (int i = 0; i < s; ++i)
{
sbuf1[i] = k; // static memory
sbuf2[i] = k; // stack memory
sbuf3[i] = k; // heap memory
}
}
else
{
for (int k = 0; k < s * extra_it; ++k)
for (int i = 0; i < b; ++i)
{
bbuf1[i] = k; // static memory
bbuf2[i] = k; // stack memory
bbuf3[i] = k; // heap memory
}
}
}
else if (c == 1)
{
// "best case scenario", only contiguous memory touched, interleaved
if (sorb)
{
for (int k = 0; k < b * extra_it; ++k)
for (int i = 0; i < s * 3; i += 3)
{
sbuf4[i] = k;
sbuf4[i + 1] = k;
sbuf4[i + 2] = k;
}
}
else
{
for (int k = 0; k < s * extra_it; ++k)
for (int i = 0; i < b * 3; i += 3)
{
bbuf4[i] = k;
bbuf4[i + 1] = k;
bbuf4[i + 2] = k;
}
}
}
else if (c == 2)
{
// "compromise", adjecant memory buffers touched
if (sorb)
{
auto b1 = sbuf4;
auto b2 = sbuf4 + s;
auto b3 = sbuf4 + s * 2;
for (int k = 0; k < b * extra_it; ++k)
for (int i = 0; i < s; ++i)
{
b1[i] = k;
b2[i] = k;
b3[i] = k;
}
}
else
{
auto b1 = bbuf4;
auto b2 = bbuf4 + b;
auto b3 = bbuf4 + b * 2;
for (int k = 0; k < s * extra_it; ++k)
for (int i = 0; i < b; ++i)
{
b1[i] = k;
b2[i] = k;
b3[i] = k;
}
}
}
else
break;
cout << chrono::duration_cast<chrono::milliseconds>(chrono::high_resolution_clock::now() - t).count() << " ms" << endl;
// basically just touching the buffers, avoiding clever optimizations
use += std::accumulate(sbuf1, sbuf1 + s, 0);
use += std::accumulate(sbuf2, sbuf2 + s, 0);
use += std::accumulate(sbuf3, sbuf3 + s, 0);
use += std::accumulate(sbuf4, sbuf4 + s * 3, 0);
use -= std::accumulate(bbuf1, bbuf1 + b, 0);
use -= std::accumulate(bbuf2, bbuf2 + b, 0);
use -= std::accumulate(bbuf3, bbuf3 + b, 0);
use -= std::accumulate(bbuf4, bbuf4 + b * 3, 0);
}
std::cout << use;
std::cin.get();
}
On my Intel i7-3740qm surprisingly, distant buffers consistently outperforms the more locality-friendly tests. It is close, however.

Related

How is numpy so fast?

I'm trying to understand how numpy can be so fast, based on my shocking comparison with optimized C/C++ code which is still far from reproducing numpy's speed.
Consider the following example:
Given a 2D array with shape=(N, N) and dtype=float32, which represents a list of N vectors of N dimensions, I am computing the pairwise differences between every pair of vectors. Using numpy broadcasting, this simply writes as:
def pairwise_sub_numpy( X ):
return X - X[:, None, :]
Using timeit I can measure the performance for N=512: it takes 88 ms per call on my laptop.
Now, in C/C++ a naive implementation writes as:
#define X(i, j) _X[(i)*N + (j)]
#define res(i, j, k) _res[((i)*N + (j))*N + (k)]
float* pairwise_sub_naive( const float* _X, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
for (int k = 0; k < N; k++)
res(i,j,k) = X(i,k) - X(j,k);
}
}
return _res;
}
Compiling using gcc 7.3.0 with -O3 flag, I get 195 ms per call for pairwise_sub_naive(X), which is not too bad given the simplicity of the code, but about 2 times slower than numpy.
Now I start getting serious and add some small optimizations, by indexing the row vectors directly:
float* pairwise_sub_better( const float* _X, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
for (int i = 0; i < N; i++) {
const float* xi = & X(i,0);
for (int j = 0; j < N; j++) {
const float* xj = & X(j,0);
float* r = &res(i,j,0);
for (int k = 0; k < N; k++)
r[k] = xi[k] - xj[k];
}
}
return _res;
}
The speed stays the same at 195 ms, which means that the compiler was able to figure that much. Let's now use SIMD vector instructions:
float* pairwise_sub_simd( const float* _X, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
// create caches for row vectors which are memory-aligned
float* xi = (float*)aligned_alloc(32, N * sizeof(float));
float* xj = (float*)aligned_alloc(32, N * sizeof(float));
for (int i = 0; i < N; i++) {
memcpy(xi, & X(i,0), N*sizeof(float));
for (int j = 0; j < N; j++) {
memcpy(xj, & X(j,0), N*sizeof(float));
float* r = &res(i,j,0);
for (int k = 0; k < N; k += 256/sizeof(float)) {
const __m256 A = _mm256_load_ps(xi+k);
const __m256 B = _mm256_load_ps(xj+k);
_mm256_store_ps(r+k, _mm256_sub_ps( A, B ));
}
}
}
free(xi);
free(xj);
return _res;
}
This only yields a small boost (178 ms instead of 194 ms per function call).
Then I was wondering if a "block-wise" approach, like what is used to optimize dot-products, could be beneficials:
float* pairwise_sub_blocks( const float* _X, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
#define B 8
float cache1[B*B], cache2[B*B];
for (int bi = 0; bi < N; bi+=B)
for (int bj = 0; bj < N; bj+=B)
for (int bk = 0; bk < N; bk+=B) {
// load first 8x8 block in the cache
for (int i = 0; i < B; i++)
for (int k = 0; k < B; k++)
cache1[B*i + k] = X(bi+i, bk+k);
// load second 8x8 block in the cache
for (int j = 0; j < B; j++)
for (int k = 0; k < B; k++)
cache2[B*j + k] = X(bj+j, bk+k);
// compute local operations on the caches
for (int i = 0; i < B; i++)
for (int j = 0; j < B; j++)
for (int k = 0; k < B; k++)
res(bi+i,bj+j,bk+k) = cache1[B*i + k] - cache2[B*j + k];
}
return _res;
}
And surprisingly, this is the slowest method so far (258 ms per function call).
To summarize, despite some efforts with some optimized C++ code, I can't come anywhere close the 88 ms / call that numpy achieves effortlessly. Any idea why?
Note: By the way, I am disabling numpy multi-threading and anyway, this kind of operation is not multi-threaded.
Edit: Exact code to benchmark the numpy code:
import numpy as np
def pairwise_sub_numpy( X ):
return X - X[:, None, :]
N = 512
X = np.random.rand(N,N).astype(np.float32)
import timeit
times = timeit.repeat('pairwise_sub_numpy( X )', globals=globals(), number=1, repeat=5)
print(f">> best of 5 = {1000*min(times):.3f} ms")
Full benchmark for C code:
#include <stdio.h>
#include <string.h>
#include <xmmintrin.h> // compile with -mavx -msse4.1
#include <pmmintrin.h>
#include <immintrin.h>
#include <time.h>
#define X(i, j) _x[(i)*N + (j)]
#define res(i, j, k) _res[((i)*N + (j))*N + (k)]
float* pairwise_sub_naive( const float* _x, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
for (int k = 0; k < N; k++)
res(i,j,k) = X(i,k) - X(j,k);
}
}
return _res;
}
float* pairwise_sub_better( const float* _x, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
for (int i = 0; i < N; i++) {
const float* xi = & X(i,0);
for (int j = 0; j < N; j++) {
const float* xj = & X(j,0);
float* r = &res(i,j,0);
for (int k = 0; k < N; k++)
r[k] = xi[k] - xj[k];
}
}
return _res;
}
float* pairwise_sub_simd( const float* _x, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
// create caches for row vectors which are memory-aligned
float* xi = (float*)aligned_alloc(32, N * sizeof(float));
float* xj = (float*)aligned_alloc(32, N * sizeof(float));
for (int i = 0; i < N; i++) {
memcpy(xi, & X(i,0), N*sizeof(float));
for (int j = 0; j < N; j++) {
memcpy(xj, & X(j,0), N*sizeof(float));
float* r = &res(i,j,0);
for (int k = 0; k < N; k += 256/sizeof(float)) {
const __m256 A = _mm256_load_ps(xi+k);
const __m256 B = _mm256_load_ps(xj+k);
_mm256_store_ps(r+k, _mm256_sub_ps( A, B ));
}
}
}
free(xi);
free(xj);
return _res;
}
float* pairwise_sub_blocks( const float* _x, int N )
{
float* _res = (float*) aligned_alloc( 32, N*N*N*sizeof(float));
#define B 8
float cache1[B*B], cache2[B*B];
for (int bi = 0; bi < N; bi+=B)
for (int bj = 0; bj < N; bj+=B)
for (int bk = 0; bk < N; bk+=B) {
// load first 8x8 block in the cache
for (int i = 0; i < B; i++)
for (int k = 0; k < B; k++)
cache1[B*i + k] = X(bi+i, bk+k);
// load second 8x8 block in the cache
for (int j = 0; j < B; j++)
for (int k = 0; k < B; k++)
cache2[B*j + k] = X(bj+j, bk+k);
// compute local operations on the caches
for (int i = 0; i < B; i++)
for (int j = 0; j < B; j++)
for (int k = 0; k < B; k++)
res(bi+i,bj+j,bk+k) = cache1[B*i + k] - cache2[B*j + k];
}
return _res;
}
int main()
{
const int N = 512;
float* _x = (float*) malloc( N * N * sizeof(float) );
for( int i = 0; i < N; i++)
for( int j = 0; j < N; j++)
X(i,j) = ((i+j*j+17*i+101) % N) / float(N);
double best = 9e9;
for( int i = 0; i < 5; i++)
{
struct timespec start, stop;
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &start);
//float* res = pairwise_sub_naive( _x, N );
//float* res = pairwise_sub_better( _x, N );
//float* res = pairwise_sub_simd( _x, N );
float* res = pairwise_sub_blocks( _x, N );
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &stop);
double t = (stop.tv_sec - start.tv_sec) * 1e6 + (stop.tv_nsec - start.tv_nsec) / 1e3; // in microseconds
if (t < best) best = t;
free( res );
}
printf("Best of 5 = %f ms\n", best / 1000);
free( _x );
return 0;
}
Compiled using gcc 7.3.0 gcc -Wall -O3 -mavx -msse4.1 -o test_simd test_simd.c
Summary of timings on my machine:
Implementation
Time
numpy
88 ms
C++ naive
194 ms
C++ better
195 ms
C++ SIMD
178 ms
C++ blocked
258 ms
C++ blocked (gcc 8.3.1)
217 ms
As pointed out by some of the comments numpy uses SIMD in its implementation and it does not allocate memory at the point of computation. If I eliminate the memory allocation from your implementation, pre-allocating all the buffers ahead of the computation then I get a better time compared to numpy even with the scaler version(that is the one without any optimizations).
Also in terms of SIMD and why your implementation does not perform much better than the scaler is because your memory access patterns are not ideal for SIMD usage - you do memcopy and you load into SIMD registers from locations that are far apart from each other - e.g. you fill vectors from line 0 and line 511, which might not play well with the cache or with the SIMD prefetcher.
There is also a mistake in how you load the SIMD registers(if I understood correctly what you're trying to compute): a 256 bit SIMD register can load 8 single-precision floating-point numbers 8 * 32 = 256, but in your loop you jump k by "256/sizeof(float)" which is 256/4 = 64; _x and _res are float pointers and the SIMD intrinsics expect also float pointers as arguments so instead of reading all elements from those lines every 8 floats you read them every 64 floats.
The computation can be optimized further by changing the access patterns but also by observing that you repeat some computations: e.g. when iterating with line0 as a base you compute line0 - line1 but at some future time, when iterating with line1 as a base, you need to compute line1 - line0 which is basically -(line0 - line1), that is for each line after line0 a lot of results could be reused from previous computations.
A lot of times SIMD usage or parallelization requires one to change how data is accessed or reasoned about in order to provide meaningful improvements.
Here is what I have done as a first step based on your initial implementation and it is faster than the numpy(don't mind the OpenMP stuff as it's not how its supposed to be done, I just wanted to see how it behaves trying the naive way).
C++
Time scaler version: 55 ms
Time SIMD version: 53 ms
**Time SIMD 2 version: 33 ms**
Time SIMD 3 version: 168 ms
Time OpenMP version: 59 ms
Python numpy
>> best of 5 = 88.794 ms
#include <cstdlib>
#include <xmmintrin.h> // compile with -mavx -msse4.1
#include <pmmintrin.h>
#include <immintrin.h>
#include <numeric>
#include <algorithm>
#include <chrono>
#include <iostream>
#include <cstring>
using namespace std;
float* pairwise_sub_naive (const float* input, float* output, int n)
{
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
for (int k = 0; k < n; k++)
output[(i * n + j) * n + k] = input[i * n + k] - input[j * n + k];
}
}
return output;
}
float* pairwise_sub_simd (const float* input, float* output, int n)
{
for (int i = 0; i < n; i++)
{
const int idxi = i * n;
for (int j = 0; j < n; j++)
{
const int idxj = j * n;
const int outidx = idxi + j;
for (int k = 0; k < n; k += 8)
{
__m256 A = _mm256_load_ps(input + idxi + k);
__m256 B = _mm256_load_ps(input + idxj + k);
_mm256_store_ps(output + outidx * n + k, _mm256_sub_ps( A, B ));
}
}
}
return output;
}
float* pairwise_sub_simd_2 (const float* input, float* output, int n)
{
float* line_buffer = (float*) aligned_alloc(32, n * sizeof(float));
for (int i = 0; i < n; i++)
{
const int idxi = i * n;
for (int j = 0; j < n; j++)
{
const int idxj = j * n;
const int outidx = idxi + j;
for (int k = 0; k < n; k += 8)
{
__m256 A = _mm256_load_ps(input + idxi + k);
__m256 B = _mm256_load_ps(input + idxj + k);
_mm256_store_ps(line_buffer + k, _mm256_sub_ps( A, B ));
}
memcpy(output + outidx * n, line_buffer, n);
}
}
return output;
}
float* pairwise_sub_simd_3 (const float* input, float* output, int n)
{
for (int i = 0; i < n; i++)
{
const int idxi = i * n;
for (int k = 0; k < n; k += 8)
{
__m256 A = _mm256_load_ps(input + idxi + k);
for (int j = 0; j < n; j++)
{
const int idxj = j * n;
const int outidx = (idxi + j) * n;
__m256 B = _mm256_load_ps(input + idxj + k);
_mm256_store_ps(output + outidx + k, _mm256_sub_ps( A, B ));
}
}
}
return output;
}
float* pairwise_sub_openmp (const float* input, float* output, int n)
{
int i, j;
#pragma omp parallel for private(j)
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
const int idxi = i * n;
const int idxj = j * n;
const int outidx = idxi + j;
for (int k = 0; k < n; k += 8)
{
__m256 A = _mm256_load_ps(input + idxi + k);
__m256 B = _mm256_load_ps(input + idxj + k);
_mm256_store_ps(output + outidx * n + k, _mm256_sub_ps( A, B ));
}
}
}
/*for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
for (int k = 0; k < n; k++)
{
output[(i * n + j) * n + k] = input[i * n + k] - input[j * n + k];
}
}
}*/
return output;
}
int main ()
{
constexpr size_t n = 512;
constexpr size_t input_size = n * n;
constexpr size_t output_size = n * n * n;
float* input = (float*) aligned_alloc(32, input_size * sizeof(float));
float* output = (float*) aligned_alloc(32, output_size * sizeof(float));
float* input_simd = (float*) aligned_alloc(32, input_size * sizeof(float));
float* output_simd = (float*) aligned_alloc(32, output_size * sizeof(float));
float* input_par = (float*) aligned_alloc(32, input_size * sizeof(float));
float* output_par = (float*) aligned_alloc(32, output_size * sizeof(float));
iota(input, input + input_size, float(0.0));
fill(output, output + output_size, float(0.0));
iota(input_simd, input_simd + input_size, float(0.0));
fill(output_simd, output_simd + output_size, float(0.0));
iota(input_par, input_par + input_size, float(0.0));
fill(output_par, output_par + output_size, float(0.0));
std::chrono::milliseconds best_scaler{100000};
for (int i = 0; i < 5; ++i)
{
auto start = chrono::high_resolution_clock::now();
pairwise_sub_naive(input, output, n);
auto stop = chrono::high_resolution_clock::now();
auto duration = chrono::duration_cast<chrono::milliseconds>(stop - start);
if (duration < best_scaler)
{
best_scaler = duration;
}
}
cout << "Time scaler version: " << best_scaler.count() << " ms\n";
std::chrono::milliseconds best_simd{100000};
for (int i = 0; i < 5; ++i)
{
auto start = chrono::high_resolution_clock::now();
pairwise_sub_simd(input_simd, output_simd, n);
auto stop = chrono::high_resolution_clock::now();
auto duration = chrono::duration_cast<chrono::milliseconds>(stop - start);
if (duration < best_simd)
{
best_simd = duration;
}
}
cout << "Time SIMD version: " << best_simd.count() << " ms\n";
std::chrono::milliseconds best_simd_2{100000};
for (int i = 0; i < 5; ++i)
{
auto start = chrono::high_resolution_clock::now();
pairwise_sub_simd_2(input_simd, output_simd, n);
auto stop = chrono::high_resolution_clock::now();
auto duration = chrono::duration_cast<chrono::milliseconds>(stop - start);
if (duration < best_simd_2)
{
best_simd_2 = duration;
}
}
cout << "Time SIMD 2 version: " << best_simd_2.count() << " ms\n";
std::chrono::milliseconds best_simd_3{100000};
for (int i = 0; i < 5; ++i)
{
auto start = chrono::high_resolution_clock::now();
pairwise_sub_simd_3(input_simd, output_simd, n);
auto stop = chrono::high_resolution_clock::now();
auto duration = chrono::duration_cast<chrono::milliseconds>(stop - start);
if (duration < best_simd_3)
{
best_simd_3 = duration;
}
}
cout << "Time SIMD 3 version: " << best_simd_3.count() << " ms\n";
std::chrono::milliseconds best_par{100000};
for (int i = 0; i < 5; ++i)
{
auto start = chrono::high_resolution_clock::now();
pairwise_sub_openmp(input_par, output_par, n);
auto stop = chrono::high_resolution_clock::now();
auto duration = chrono::duration_cast<chrono::milliseconds>(stop - start);
if (duration < best_par)
{
best_par = duration;
}
}
cout << "Time OpenMP version: " << best_par.count() << " ms\n";
cout << "Verification\n";
if (equal(output, output + output_size, output_simd))
{
cout << "PASSED\n";
}
else
{
cout << "FAILED\n";
}
return 0;
}
Edit: Small correction as there was a wrong call related to the second version of SIMD implementation.
As you can see now, the second implementation is the fastest as it behaves the best from the point of view of the locality of reference of the cache. Examples 2 and 3 of SIMD implementations are there to illustrate for you how changing memory access patterns to influence the performance of your SIMD optimizations.
To summarize(knowing that I'm far from being complete in my advice) be mindful of your memory access patterns and of the loads and stores to\from the SIMD unit; the SIMD is a different hardware unit inside the processor's core so there is a penalty in shuffling data back and forth, hence when you load a register from memory try to do as many operations as possible with that data and do not be too eager to store it back(of course, in your example that might be all you need to do with the data). Be mindful also that there is a limited number of SIMD registers available and if you load too many then they will "spill", that is they will be stored back to temporary locations in main memory behind the scenes killing all your gains. SIMD optimization, it's a true balance act!
There is some effort to put a cross-platform intrinsics wrapper into the standard(I developed myself a closed source one in my glorious past) and even it's far from being complete, it's worth taking a look at(read the accompanying papers if you're truly interested to learn how SIMD works).
https://github.com/VcDevel/std-simd
This is a complement to the answer posted by #celakev .
I think I finally got to understand what exactly was the issue. The issue was not about allocating the memory in the main function that does the computation.
What was actually taking time is to access new (fresh) memory. I believe that the malloc call returns pages of memory which are virtual, i.e. that does not corresponds to actual physical memory -- until it is explicitly accessed. What actually takes time is the process of allocating physical memory on the fly (which I think is OS-level) when it is accessed in the function code.
Here is a proof. Consider the two following trivial functions:
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
float* just_alloc( size_t N )
{
return (float*) aligned_alloc( 32, sizeof(float)*N );
}
void just_fill( float* _arr, size_t N )
{
for (size_t i = 0; i < N; i++)
_arr[i] = 1;
}
#define Time( code_to_benchmark, cleanup_code ) \
do { \
double best = 9e9; \
for( int i = 0; i < 5; i++) { \
struct timespec start, stop; \
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &start); \
code_to_benchmark; \
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &stop); \
double t = (stop.tv_sec - start.tv_sec) * 1e3 + (stop.tv_nsec - start.tv_nsec) / 1e6; \
printf("Time[%d] = %f ms\n", i, t); \
if (t < best) best = t; \
cleanup_code; \
} \
printf("Best of 5 for '" #code_to_benchmark "' = %f ms\n\n", best); \
} while(0)
int main()
{
const size_t N = 512;
Time( float* arr = just_alloc(N*N*N), free(arr) );
float* arr = just_alloc(N*N*N);
Time( just_fill(arr, N*N*N), ; );
free(arr);
return 0;
}
I get the following timings, which I now detail for each of the calls:
Time[0] = 0.000931 ms
Time[1] = 0.000540 ms
Time[2] = 0.000523 ms
Time[3] = 0.000524 ms
Time[4] = 0.000521 ms
Best of 5 for 'float* arr = just_alloc(N*N*N)' = 0.000521 ms
Time[0] = 189.822237 ms
Time[1] = 45.041083 ms
Time[2] = 46.331428 ms
Time[3] = 44.729433 ms
Time[4] = 42.241279 ms
Best of 5 for 'just_fill(arr, N*N*N)' = 42.241279 ms
As you can see, allocating memory is blazingly fast, but the first time that the memory is accessed, it is 5 times slower than the other times. So, basically the reason that my code was slow was because i was each time reallocating fresh memory that had no physical address yet. (Correct me if I'm wrong but I think that's the gist of it!)
A bit late to the party, but I wanted to add a pairwise method with Eigen, which is supposed to give C++ a high-level algebra manipulation capability and use SIMD under the hood. Just like numpy.
Here is the implementation
#include <iostream>
#include <vector>
#include <chrono>
#include <algorithm>
#include <Eigen/Dense>
auto pairwise_eigen(const Eigen::MatrixXf &input, std::vector<Eigen::MatrixXf> &output) {
for (int k = 0; k < input.cols(); ++k)
output[k] = input
// subtract matrix with repeated k-th column
- input.col(k) * Eigen::RowVectorXf::Ones(input.cols());
}
int main() {
constexpr size_t n = 512;
// allocate input and output
Eigen::MatrixXf input = Eigen::MatrixXf::Random(n, n);
std::vector<Eigen::MatrixXf> output(n);
std::chrono::milliseconds best_eigen{100000};
for (int i = 0; i < 5; ++i) {
auto start = std::chrono::high_resolution_clock::now();
pairwise_eigen(input, output);
auto end = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end-start);
if (duration < best_eigen)
best_eigen = duration;
}
std::cout << "Time Eigen version: " << best_eigen.count() << " ms\n";
return 0;
}
The full benchmark tests suggested by #celavek on my system are
Time scaler version: 57 ms
Time SIMD version: 58 ms
Time SIMD 2 version: 40 ms
Time SIMD 3 version: 58 ms
Time OpenMP version: 58 ms
Time Eigen version: 76 ms
Numpy >> best of 5 = 118.489 ms
Whit Eigen there is still a noticeable improvement with respect to Numpy, but not so impressive compared to the "raw" implementations (there is certainly some overhead).
An extra optimization is to allocate the output vector with copies of the input and then subtract directly from each vector entry, simply replacing the following lines
// inside the pairwise method
for (int k = 0; k < input.cols(); ++k)
output[k] -= input.col(k) * Eigen::RowVectorXf::Ones(input.cols());
// at allocation time
std::vector<Eigen::MatrixXf> output(n, input);
This pushes the best of 5 down to 60 ms.

Slow std::vector vs [] in C++ - Why?

I am a bit rusty with C++ - having used it 20 years ago. I am trying to understand why std::vector is so much slower than native arrays in the following code. Can anyone explain it to me? I would much prefer using the standard libraries but not at the cost of this performance penalty:
Vector:
const int grid_e_rows = 50;
const int grid_e_cols = 50;
int H(std::vector<std::vector<int>> &sigma) {
int h = 0;
for (int r = 0; r < grid_e_rows; ++r) {
int r2 = (r + 1) % grid_e_rows;
for (int c = 0; c < grid_e_cols; ++c) {
int c2 = (c + 1) % grid_e_cols;
h += 1 * sigma[r][c] * sigma[r][c2] + 1 * sigma[r][c] * sigma[r2][c];
}
}
return -h;
}
int main() {
auto start = std::chrono::steady_clock::now();
std::vector<std::vector<int>> sigma_a(grid_e_rows, std::vector<int>(grid_e_cols));
for (int i=0;i<600000;i++)
H(sigma_a);
auto end = std::chrono::steady_clock::now();
std::cout << "Calculation completed in " << std::chrono::duration_cast<std::chrono::seconds>(end - start).count()
<< " seconds";
return 0;
}
Output is:
Calculation completed in 23 seconds
Array:
const int grid_e_rows = 50;
const int grid_e_cols = 50;
typedef int (*Sigma)[grid_e_rows][grid_e_cols];
int H(Sigma sigma) {
int h = 0;
for (int r = 0; r < grid_e_rows; ++r) {
int r2 = (r + 1) % grid_e_rows;
for (int c = 0; c < grid_e_cols; ++c) {
int c2 = (c + 1) % grid_e_cols;
h += 1 * (*sigma)[r][c] * (*sigma)[r][c2] + 1 * (*sigma)[r][c] * (*sigma)[r2][c];
}
}
return -h;
}
int main() {
auto start = std::chrono::steady_clock::now();
int sigma_a[grid_e_rows][grid_e_cols];
for (int i=0;i<600000;i++)
H(&sigma_a);
auto end = std::chrono::steady_clock::now();
std::cout << "Calculation completed in " << std::chrono::duration_cast<std::chrono::seconds>(end - start).count()
<< " seconds";
return 0;
}
Output is:
Calculation completed in 6 seconds
Any help would be appreciated.
First, you're timing the initialization. For the array case, there is none (the array is completely uninitialized). In the vector case, the vector is initialized to zero and then copied into each row.
But the primary reason is cache locality. The array case is a single block of 50*50 integers which are all continuous in memory, and they can trivially fit in L1D cache. In the vector case, each row is allocated dynamically which means their addresses are almost certainly not contiguous and are instead spread all over the program's address space. Accessing one does not pull the adjacent rows into the cache.
Also, because the rows are relatively small, cache space is wasted on adjacent unrelated data, meaning even after you've touched everything to pull it into memory it may not fit in L1 anymore. And lastly, the access pattern is a lot less linear, and it may be beyond the capability of a hardware prefetcher to predict.
You are not compiling with optimizations.
Compare:
With vector of vector
With array
To give you a small taste of what the optimizer might be doing for you, consider the following modification to your H() function for the vector of vector case.
int H(std::vector<std::vector<int>> &arg) {
int h = 0;
auto sigma = arg.data();
for (int r = 0; r < grid_e_rows; ++r) {
int r2 = (r + 1) % grid_e_rows;
auto sr = sigma[r].data();
auto sr2 = sigma[r2].data();
for (int c = 0; c < grid_e_cols; ++c) {
int c2 = (c + 1) % grid_e_cols;
h += 1 * sr[c] * sr[c2] + 1 * sr[c] * sr2[c];
}
}
return -h;
}
You will find that without optimizations, this version will run closer to the performance of your array version.

Why does Windows sometimes use a critical section and sometimes use an atomic list for the heap lock?

I encountered a problem where during a parallelized operation, I get a severe performance issue on certain machines. For example, the code below scales well on a 2 chip machine with 8 cores each, but scales poorly on a 2 chip machine with 10 cores each.
The profiler indicates cache misses for the machine with 2x10 cores and indicates calls to the Windows RtlEnterCriticalSection. Most time here is spent on malloc/free. For the 2x8 core machine, most time is spent on the std::rand (the bare matrix matrix product is just to introduce some dummy number crunching).
For the 2x8 core machine, I don't get any cache misses at all, and I also dont' see calls to RtlEnterCriticalSection. Instead I see calls to RtlInterlockedPopSList which looks like Windows is using atomics to manage the heap lock. The question is why would it use the critical section on the other machine which is highly inefficient for multi-threading?
std::vector> futures;
for (auto iii = 0; iii != 40; iii++) {
futures.push_back(std::async([]() {
for (auto i = 0; i != 100000 / 40; i++) {
const int size = 10;
for (auto k = 0; k != size * size; k++) {
double* matrix1 = (double*)malloc(100 * sizeof(double));
double* matrix2 = (double*)malloc(100 * sizeof(double));
double* matrix3 = (double*)malloc(100 * sizeof(double));
for (auto i = 0; i != size; i++) {
for (auto j = 0; j != size; j++) {
matrix1[i * size + j] = std::rand() / RAND_MAX;
matrix2[i * size + j] = std::rand() / RAND_MAX;
}
}
for (auto i = 0; i != size; i++) {
for (auto j = 0; j != size; j++) {
double sum = 0.0;
for (auto k = 0; k != size; k++) {
sum += matrix1[i * size + k] * matrix2[k * size + j];
}
matrix3[i * size + j] = sum;
}
}
free(matrix1);
free(matrix2);
free(matrix3);
}
}
}));
}
for (auto& entry : futures)
entry.wait();

Optimization of C++ code - std::vector operations

I have this funcition (RotateSlownessTop) and it's called about 800 times computing the corresponding values. But the calculation is slow and is there a way I can make the computations faster.
The number of element in X/Y is 7202. (Fairly large set)
I did the performance analysis and the screenshot has been attached.
void RotateSlownessTop(vector <double> &XR1, vector <double> &YR1, float theta = 0.0)
{
Matrix2d a;
a(0,0) = cos(theta);
a(0,1) = -sin(theta);
a(1, 0) = sin(theta);
a(1, 1) = cos(theta);
vector <double> XR2(7202), YR2(7202);
for (size_t i = 0; i < X.size(); ++i)
{
XR2[i] = (a(0, 0)*X[i] + a(0, 1)*Y[i]);
YR2[i] = (a(1, 0)*X[i] + a(1, 1)*Y[i]);
}
size_t i = 0;
size_t j = 0;
while (i < YR2.size())
{
if (i > 0)
if ((XR2[i]>0) && (XR2[i-1]<0))
j = i;
if (YR2[i] > (-1e-10) && YR2[i]<0.0)
YR2[i] = 0.0;
if (YR2[i] < (1e-10) && YR2[i]>0.0)
YR2[i] = -YR2[i];
if ( YR2[i]<0.0)
{
YR2.erase(YR2.begin() + i);
XR2.erase(XR2.begin() + i);
--i;
}
++i;
}
size_t k = 0;
while (j < YR2.size())
{
YR1[k] = (YR2[j]);
XR1[k] = (XR2[j]);
YR2.erase(YR2.begin() + j);
XR2.erase(XR2.begin() + j);
++k;
}
size_t l = 0;
for (; k < XR1.size(); ++k)
{
XR1[k] = XR2[l];
YR1[k] = YR2[l];
l++;
}
}
Edit1: I have updated the code by replacing all push_back() with operator[], since I read somewhere that this is much faster.
However the whole program is still slow. Any suggestions are appreciated.
If the size is large, you can improve the push_back by pre-allocating the space needed. Add this before the loop:
XR2.reserve(X.size());
YR2.reserve(X.size());

Gradient descent converging towards the wrong value

I'm trying to implement a gradient descent algorithm in C++. Here's the code I have so far :
#include <iostream>
double X[] {163,169,158,158,161,172,156,161,154,145};
double Y[] {52, 68, 49, 73, 71, 99, 50, 82, 56, 46 };
double m, p;
int n = sizeof(X)/sizeof(X[0]);
int main(void) {
double alpha = 0.00004; // 0.00007;
m = (Y[1] - Y[0]) / (X[1] - X[0]);
p = Y[0] - m * X[0];
for (int i = 1; i <= 8; i++) {
gradientStep(alpha);
}
return 0;
}
double Loss_function(void) {
double res = 0;
double tmp;
for (int i = 0; i < n; i++) {
tmp = Y[i] - m * X[i] - p;
res += tmp * tmp;
}
return res / 2.0 / (double)n;
}
void gradientStep(double alpha) {
double pg = 0, mg = 0;
for (int i = 0; i < n; i++) {
pg += Y[i] - m * X[i] - p;
mg += X[i] * (Y[i] - m * X[i] - p);
}
p += alpha * pg / n;
m += alpha * mg / n;
}
This code converges towards m = 2.79822, p = -382.666, and an error of 102.88. But if I use my calculator to find out the correct linear regression model, I find that the correct values of m and p should respectively be 1.601 and -191.1.
I also noticed that the algorithm won't converge for alpha > 0.00007, which seems quite low, and the value of p barely changes during the 8 iterations (or even after 2000 iterations).
What's wrong with my code?
Here's a good overview of the algorithm I'm trying to implement. The values of theta0 and theta1 are called p and m in my program.
Other implementation in python
More about the algorithm
This link gives a comprehensive view of the algorithm; it turns out I was following a completely wrong approach.
The following code does not work properly (and I have no plans to work on it further), but should put on track anyone who's confronted to the same problem as me :
#include <vector>
#include <iostream>
typedef std::vector<double> vect;
std::vector<double> y, omega(2, 0), omega2(2, 0);;
std::vector<std::vector<double>> X;
int n = 10;
int main(void) {
/* Initialize x so that each members contains (1, x_i) */
/* Initialize x so that each members contains y_i */
double alpha = 0.00001;
display();
for (int i = 1; i <= 8; i++) {
gradientStep(alpha);
display();
}
return 0;
}
double f_function(const std::vector<double> &x) {
double c;
for (unsigned int i = 0; i < omega.size(); i++) {
c += omega[i] * x[i];
}
return c;
}
void gradientStep(double alpha) {
for (int i = 0; i < n; i++) {
for (unsigned int j = 0; j < X[0].size(); j++) {
omega2[j] -= alpha/(double)n * (f_function(X[i]) - y[i]) * X[i][j];
}
}
omega = omega2;
}
void display(void) {
double res = 0, tmp = 0;
for (int i = 0; i < n; i++) {
tmp = y[i] - f_function(X[i]);
res += tmp * tmp; // Loss functionn
}
std::cout << "omega = ";
for (unsigned int i = 0; i < omega.size(); i++) {
std::cout << "[" << omega[i] << "] ";
}
std::cout << "\tError : " << res * .5/(double)n << std::endl;
}