How to optimize my C++ OpenMp Matrix Multiplication code - c++

Question

I have written a C++ OpenMp Matrix Multiplication code that multiplies two 1000x1000 matrices.
So far I have gotten a 0.700 sec execution time using OpenMp but I want to see if there is other ways I can make it faster using OpenMp?
I appreciate any advice or tips you can give me.
Here is my code:
#include <iostream>
#include <time.h>
#include <omp.h>
using namespace std;
void Multiply()
{
//initialize matrices with random numbers
int aMatrix[1000][1000], i, j;
for( i = 0; i < 1000; ++i)
{for( j = 0; j < 1000; ++j)
{aMatrix[i][j] = rand();}
}
int bMatrix[1000][1000], i1, j2;
for( i1 = 0; i1 < 1000; ++i1)
{for( j2 = 0; j2 < 1000; ++j2)
{bMatrix[i1][j2] = rand();}
}
//Result Matrix
int product[1000][1000] = {0};
for (int row = 0; row < 1000; row++) {
for (int col = 0; col < 1000; col++) {
// Multiply the row of A by the column of B to get the row, column of product.
for (int inner = 0; inner < 1000; inner++) {
product[row][col] += aMatrix[row][inner] * bMatrix[inner][col];
}
}
}
}
int main() {
time_t begin, end;
time(&begin);
Multiply();
time(&end);
time_t elapsed = end - begin;
cout << ("Time measured: %ld seconds.\n", elapsed);
return 0;
}

Answer 1

Following things can be done for speedup:
Using OpenMP for parallelizing external loop, like you did (and like I also did in my following code). Or alternatively using std::async for multi-threading like it was used in another answer.
Transpose B matrix, this will help to increase L1 cache hits, because you will read from sequential memory each B column (or row in transposed variant).
Use vectorized SIMD instructions, this will allow to do several multiplications (and additions) within one CPU cycle. Often compilers do auto-vectorization of your loops well enough through SIMD instructions without your help, but I did it explicitly in my code.
Run several independent SIMD instructions within loop. This will help to occupy whole CPU pipeline of SIMD. I did so in my code by using four SIMD registers r0, r1, r2, r3. In compilers this is usually called loop unrolling.
Align your matrix starting address on 64-bytes boundary. This will help SIMD instructions to do fast aligned read/write.
Align starting address of each matrix row on 64-bytes boundary. I did this in my code by padding each row with zeros till multiple of 64-bytes. This also helps SIMD instructions to do fast aligned read/write.
In my following code I did all 1. - 6. steps above. Memory 64-bytes alignment I did through implementing AlignmentAllocator that was used in std::vector. Also I did time measurements for float/double/int.
On my old 4-core laptop I got following time measurements for the case of 1000x1000 matrix multiplying by 1000x1000:
float: time 0.1569 sec
double: time 0.3168 sec
int: time 0.1565 sec
To compare my hardware capabilities I did measurements of another answer of #doug for the case of int:
Threads w transpose 0.2164 secs.
As one can see my solution is 1.4x times faster that the other answer, I guess due to memory 64-bytes alignment and maybe due to using explicit SIMD (instead of relying on compiler auto-vectorization of a loop).
To compile my program, don't forget to add -fopenmp -lgomp options (for OpenMP support) and -march=native -O3 -std=c++20 options (for SIMD support, optimizations and standard) if you're compiling under GCC/CLang, while MSVC I guess adds OpenMP automatically and doesn't need any special options (use /O2 /GL /std:c++latest for optimizations and standard in MSVC).
In my code I only implemented SSE2/SSE4/AVX/AVX2 instructions for SIMD, if you have more powerful machine you may tell me and I implement also FMA/AVX-512, they will give even twice more speed boost.
My Mul() function is quite generic, it is templated, and you just pass pointers to matrices and row/col count, so your matrices may be stored on calling side in any way (through std::vector or std::array or plain 2D array).
At start of Run() function you may change number of rows and columns if you need a bigger test. Notice that all my functions support any rows and columns, you may even multiply matrix of size 1234x2345 by 2345x3456.
Try it online!
#include <cstdint>
#include <cstring>
#include <stdexcept>
#include <iostream>
#include <iomanip>
#include <vector>
#include <memory>
#include <string>
#include <immintrin.h>
#define USE_OPENMP 1
#define ASSERT_MSG(cond, msg) { if (!(cond)) throw std::runtime_error("Assertion (" #cond ") failed at line " + std::to_string(__LINE__) + "! Msg '" + std::string(msg) + "'."); }
#define ASSERT(cond) ASSERT_MSG(cond, "")
#if defined(_MSC_VER)
#define IS_MSVC 1
#else
#define IS_MSVC 0
#endif
#if USE_OPENMP
#include <omp.h>
#endif
template <typename T, std::size_t N>
class AlignmentAllocator {
public:
typedef T value_type;
typedef std::size_t size_type;
typedef std::ptrdiff_t difference_type;
typedef T * pointer;
typedef const T * const_pointer;
typedef T & reference;
typedef const T & const_reference;
public:
inline AlignmentAllocator() throw() {}
template <typename T2> inline AlignmentAllocator(const AlignmentAllocator<T2, N> &) throw() {}
inline ~AlignmentAllocator() throw() {}
inline pointer adress(reference r) { return &r; }
inline const_pointer adress(const_reference r) const { return &r; }
inline pointer allocate(size_type n);
inline void deallocate(pointer p, size_type);
inline void construct(pointer p, const value_type & wert);
inline void destroy(pointer p) { p->~value_type(); }
inline size_type max_size() const throw() { return size_type(-1) / sizeof(value_type); }
template <typename T2> struct rebind { typedef AlignmentAllocator<T2, N> other; };
bool operator!=(const AlignmentAllocator<T, N> & other) const { return !(*this == other); }
bool operator==(const AlignmentAllocator<T, N> & other) const { return true; }
};
template <typename T, std::size_t N>
inline typename AlignmentAllocator<T, N>::pointer AlignmentAllocator<T, N>::allocate(size_type n) {
#if IS_MSVC
auto p = (pointer)_aligned_malloc(n * sizeof(value_type), N);
#else
auto p = (pointer)std::aligned_alloc(N, n * sizeof(value_type));
#endif
ASSERT(p);
return p;
}
template <typename T, std::size_t N>
inline void AlignmentAllocator<T, N>::deallocate(pointer p, size_type) {
#if IS_MSVC
_aligned_free(p);
#else
std::free(p);
#endif
}
template <typename T, std::size_t N>
inline void AlignmentAllocator<T, N>::construct(pointer p, const value_type & wert) {
new (p) value_type(wert);
}
template <typename T>
using AlignedVector = std::vector<T, AlignmentAllocator<T, 64>>;
template <typename T>
struct RegT;
#ifdef __AVX__
template <> struct RegT<float> { static size_t constexpr bisize = 256; using type = __m256; static type zero() { return _mm256_setzero_ps(); } };
template <> struct RegT<double> { static size_t constexpr bisize = 256; using type = __m256d; static type zero() { return _mm256_setzero_pd(); } };
inline void MulAddReg(float const * a, float const * b, __m256 & c) {
c = _mm256_add_ps(c, _mm256_mul_ps(_mm256_load_ps(a), _mm256_load_ps(b)));
}
inline void MulAddReg(double const * a, double const * b, __m256d & c) {
c = _mm256_add_pd(c, _mm256_mul_pd(_mm256_load_pd(a), _mm256_load_pd(b)));
}
inline void StoreReg(float * dst, __m256 const & src) { _mm256_store_ps(dst, src); }
inline void StoreReg(double * dst, __m256d const & src) { _mm256_store_pd(dst, src); }
#else // SSE2
template <> struct RegT<float> { static size_t constexpr bisize = 128; using type = __m128; static type zero() { return _mm_setzero_ps(); } };
template <> struct RegT<double> { static size_t constexpr bisize = 128; using type = __m128d; static type zero() { return _mm_setzero_pd(); } };
inline void MulAddReg(float const * a, float const * b, __m128 & c) {
c = _mm_add_ps(c, _mm_mul_ps(_mm_load_ps(a), _mm_load_ps(b)));
}
inline void MulAddReg(double const * a, double const * b, __m128d & c) {
c = _mm_add_pd(c, _mm_mul_pd(_mm_load_pd(a), _mm_load_pd(b)));
}
inline void StoreReg(float * dst, __m128 const & src) { _mm_store_ps(dst, src); }
inline void StoreReg(double * dst, __m128d const & src) { _mm_store_pd(dst, src); }
#endif
#ifdef __AVX2__
template <> struct RegT<int32_t> { static size_t constexpr bisize = 256; using type = __m256i; static type zero() { return _mm256_setzero_si256(); } };
//template <> struct RegT<int64_t> { static size_t constexpr bisize = 256; using type = __m256i; static type zero() { return _mm256_setzero_si256(); } };
inline void MulAddReg(int32_t const * a, int32_t const * b, __m256i & c) {
c = _mm256_add_epi32(c, _mm256_mullo_epi32(_mm256_load_si256((__m256i*)a), _mm256_load_si256((__m256i*)b)));
}
//inline void MulAddReg(int64_t const * a, int64_t const * b, __m256i & c) {
// c = _mm256_add_epi64(c, _mm256_mullo_epi64(_mm256_load_si256((__m256i*)a), _mm256_load_si256((__m256i*)b)));
//}
inline void StoreReg(int32_t * dst, __m256i const & src) { _mm256_store_si256((__m256i*)dst, src); }
//inline void StoreReg(int64_t * dst, __m256i const & src) { _mm256_store_si256((__m256i*)dst, src); }
#else // SSE2
template <> struct RegT<int32_t> { static size_t constexpr bisize = 128; using type = __m128i; static type zero() { return _mm_setzero_si128(); } };
//template <> struct RegT<int64_t> { static size_t constexpr bisize = 128; using type = __m128i; static type zero() { return _mm_setzero_si128(); } };
inline void MulAddReg(int32_t const * a, int32_t const * b, __m128i & c) {
c = _mm_add_epi32(c, _mm_mullo_epi32(_mm_load_si128((__m128i*)a), _mm_load_si128((__m128i*)b)));
}
//inline void MulAddReg(int64_t const * a, int64_t const * b, __m128i & c) {
// c = _mm_add_epi64(c, _mm_mullo_epi64(_mm_load_si128((__m128i*)a), _mm_load_si128((__m128i*)b)));
//}
inline void StoreReg(int32_t * dst, __m128i const & src) { _mm_store_si128((__m128i*)dst, src); }
//inline void StoreReg(int64_t * dst, __m128i const & src) { _mm_store_si128((__m128i*)dst, src); }
#endif
template <typename T>
void Mul(T const * A0, size_t A_rows, size_t A_cols, T const * B0, size_t B_rows, size_t B_cols, T * C) {
size_t constexpr reg_cnt = RegT<T>::bisize / 8 / sizeof(T), block = 4 * reg_cnt;
ASSERT(A_cols == B_rows);
size_t const A_cols_aligned = (A_cols + block - 1) / block * block, B_rows_aligned = (B_rows + block - 1) / block * block;
// Copy aligned A
AlignedVector<T> Av(A_rows * A_cols_aligned);
for (size_t i = 0; i < A_rows; ++i)
std::memcpy(&Av[i * A_cols_aligned], &A0[i * A_cols], sizeof(Av[0]) * A_cols);
T const * A = Av.data();
// Transpose B
AlignedVector<T> Bv(B_cols * B_rows_aligned);
for (size_t j = 0; j < B_cols; ++j)
for (size_t i = 0; i < B_rows; ++i)
Bv[j * B_rows_aligned + i] = B0[i * B_cols + j];
T const * Bt = Bv.data();
ASSERT(uintptr_t(A) % 64 == 0 && uintptr_t(Bt) % 64 == 0);
ASSERT(uintptr_t(&A[A_cols_aligned]) % 64 == 0 && uintptr_t(&Bt[B_rows_aligned]) % 64 == 0);
// Multiply
#pragma omp parallel for
for (size_t i = 0; i < A_rows; ++i) {
// Aligned Reg storage
AlignedVector<T> Regs(block);
for (size_t j = 0; j < B_cols; ++j) {
T const * Arow = &A[i * A_cols_aligned + 0], * Btrow = &Bt[j * B_rows_aligned + 0];
using Reg = typename RegT<T>::type;
Reg r0 = RegT<T>::zero(), r1 = RegT<T>::zero(), r2 = RegT<T>::zero(), r3 = RegT<T>::zero();
size_t const k_hi = A_cols - A_cols % block;
for (size_t k = 0; k < k_hi; k += block) {
MulAddReg(&Arow[k + reg_cnt * 0], &Btrow[k + reg_cnt * 0], r0);
MulAddReg(&Arow[k + reg_cnt * 1], &Btrow[k + reg_cnt * 1], r1);
MulAddReg(&Arow[k + reg_cnt * 2], &Btrow[k + reg_cnt * 2], r2);
MulAddReg(&Arow[k + reg_cnt * 3], &Btrow[k + reg_cnt * 3], r3);
}
StoreReg(&Regs[reg_cnt * 0], r0);
StoreReg(&Regs[reg_cnt * 1], r1);
StoreReg(&Regs[reg_cnt * 2], r2);
StoreReg(&Regs[reg_cnt * 3], r3);
T sum1 = 0, sum2 = 0, sum3 = 0;
for (size_t k = 0; k < Regs.size(); ++k)
sum1 += Regs[k];
//for (size_t k = 0; k < A_cols - A_cols % block; ++k) sum3 += Arow[k] * Btrow[k];
for (size_t k = k_hi; k < A_cols; ++k)
sum2 += Arow[k] * Btrow[k];
C[i * A_rows + j] = sum2 + sum1;
}
}
}
#include <random>
#include <thread>
#include <chrono>
#include <type_traits>
template <typename T>
void Test(T const * A, size_t A_rows, size_t A_cols, T const * B, size_t B_rows, size_t B_cols, T const * C, T eps) {
for (size_t i = 0; i < A_rows / 16; ++i)
for (size_t j = 0; j < B_cols / 16; ++j) {
T sum = 0;
for (size_t k = 0; k < A_cols; ++k)
sum += A[i * A_cols + k] * B[k * B_cols + j];
ASSERT_MSG(std::abs(C[i * A_rows + j] - sum) <= eps * A_cols, "i " + std::to_string(i) + " j " + std::to_string(j) +
" C " + std::to_string(C[i * A_rows + j]) + " ref " + std::to_string(sum));
}
}
double Time() {
static auto const gtb = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::duration<double>>(
std::chrono::high_resolution_clock::now() - gtb).count();
}
template <typename T>
void Run() {
size_t constexpr A_rows = 1000, A_cols = 1000, B_rows = 1000, B_cols = 1000;
std::string const tname = std::is_same_v<T, float> ? "float" : std::is_same_v<T, double> ?
"double" : std::is_same_v<T, int32_t> ? "int" : "<unknown>";
bool const is_int = tname == "int";
std::mt19937_64 rng{123};
std::vector<T> A(A_rows * A_cols), B(B_rows * B_cols), C(A_rows * B_cols);
for (size_t i = 0; i < A.size(); ++i)
A[i] = is_int ? (int64_t(rng() % (1 << 11)) - (1 << 10)) : (T(int64_t(rng() % (1 << 28)) - (1 << 27)) / T(1 << 27));
for (size_t i = 0; i < B.size(); ++i)
B[i] = is_int ? (int64_t(rng() % (1 << 11)) - (1 << 10)) : (T(int64_t(rng() % (1 << 28)) - (1 << 27)) / T(1 << 27));
auto tim = Time();
Mul(&A[0], A_rows, A_cols, &B[0], B_rows, B_cols, &C[0]);
tim = Time() - tim;
std::cout << std::setw(6) << tname << ": time " << std::fixed << std::setprecision(4) << tim << " sec" << std::endl;
Test(&A[0], A_rows, A_cols, &B[0], B_rows, B_cols, &C[0], tname == "float" ? T(1e-7) : tname == "double" ? T(1e-15) : T(0));
}
int main() {
try {
#if USE_OPENMP
omp_set_num_threads(std::thread::hardware_concurrency());
#endif
Run<float>();
Run<double>();
Run<int32_t>();
return 0;
} catch (std::exception const & ex) {
std::cout << "Exception: " << ex.what() << std::endl;
return -1;
}
}
Output:
float: time 0.1569 sec
double: time 0.3168 sec
int: time 0.1565 sec

Answer 2

Here's straight c++ code that runs in .08s with ints and .14s with floats or doubles. My system is 10 years old with relatively slow memory. Good at the time but now is now.
I agree with #VictorEijkhout that the best results would be with tuned code. There has been huge amounts of work optimizing those.
#include <vector>
#include <array>
#include <random>
#include <cassert>
#include <iostream>
#include <iomanip>
#include <thread>
#include <future>
#include <chrono>
struct Timer {
std::chrono::system_clock::time_point snapTime;
Timer() { snapTime = std::chrono::system_clock::now(); }
operator double() { return std::chrono::duration<double>(std::chrono::system_clock::now() - snapTime).count(); }
};
using DataType = int;
using std::array, std::vector;
constexpr int N = 1000, THREADS = 12;
static auto launchType = std::launch::async;
using Matrix = vector<array<DataType, N>>;
Matrix create_matrix() { return Matrix(N); };
Matrix product(Matrix const& v0, Matrix const& v1, double& time)
{
Matrix ret = create_matrix();
Matrix v2 = create_matrix();
Timer timer;
for (size_t r = 0; r < N; r++) // transpose first
for (size_t c = 0; c < N; c++)
v2[c][r] = v1[r][c];
// lambda to process sets of rows in separate threads
auto do_row_set = [&v0, &v2, &ret](size_t start, size_t last) {
for (size_t row = start; row < last; row++)
for (size_t col = 0; col < N; col++)
{
DataType tmp{}; // separate tmp variable significantly improves optimization
for (size_t col_t = 0; col_t < N; col_t++)
tmp += v0[row][col_t] * v2[col][col_t];
ret[row][col] = tmp;
}
};
vector<size_t> seq;
const size_t NN = N / THREADS;
// make a sequence of NN+1 rows from start to end
for (size_t thread_n = 0; thread_n < N; thread_n += NN)
seq.push_back(thread_n);
seq.push_back(N);
vector<std::future<void>> results; results.reserve(THREADS);
for (size_t i = 0; i < THREADS; i++)
results.emplace_back(std::async(launchType, do_row_set, seq[i], seq[i + 1]));
for (auto& x : results)
x.get();
time = timer;
return ret;
}
bool operator==(Matrix const& v0, Matrix const& v1)
{
for (size_t r = 0; r < N; r++)
for (size_t c = 0; c < N; c++)
if (v0[r][c] != v1[r][c])
return false;
return true;
}
int main()
{
auto fill = [](Matrix& v) {
std::mt19937_64 r(1);
std::normal_distribution dist(1.);
for (size_t row = 0; row < N; row++)
for (size_t col = 0; col < N; col++)
v[row][col] = DataType(dist(r));
};
Matrix m1 = create_matrix(), m2 = create_matrix(), m3 = create_matrix();
fill(m1); fill(m2);
auto process_test = [&m1, &m2](Matrix& out) {
const int rpt_count = 4;
double sum = 0;
for (int i = 0; i < rpt_count; i++)
{
double time;
out = product(m1, m2, time);
sum += time / rpt_count;
}
return sum;
};
std::cout << std::fixed << std::setprecision(4);
double time{};
time = process_test(m3);
std::cout << "Threads w transpose " << time << " secs.\n";
}