Why cublas on GTX Titan is slower than single threaded CPU code? - c++

Question

I am testing Nvidia Cublas Library on my GTX Titan. I have the following code:
#include "cublas.h"
#include <stdlib.h>
#include <conio.h>
#include <Windows.h>
#include <iostream>
#include <iomanip>
/* Vector size */
#define N (1024 * 1024 * 32)
/* Main */
int main(int argc, char** argv)
{
LARGE_INTEGER frequency;
LARGE_INTEGER t1, t2;
float* h_A;
float* h_B;
float* d_A = 0;
float* d_B = 0;
/* Initialize CUBLAS */
cublasInit();
/* Allocate host memory for the vectors */
h_A = (float*)malloc(N * sizeof(h_A[0]));
h_B = (float*)malloc(N * sizeof(h_B[0]));
/* Fill the vectors with test data */
for (int i = 0; i < N; i++)
{
h_A[i] = rand() / (float)RAND_MAX;
h_B[i] = rand() / (float)RAND_MAX;
}
QueryPerformanceFrequency(&frequency);
QueryPerformanceCounter(&t1);
/* Allocate device memory for the vectors */
cublasAlloc(N, sizeof(d_A[0]), (void**)&d_A);
cublasAlloc(N, sizeof(d_B[0]), (void**)&d_B);
/* Initialize the device matrices with the host vectors */
cublasSetVector(N, sizeof(h_A[0]), h_A, 1, d_A, 1);
cublasSetVector(N, sizeof(h_B[0]), h_B, 1, d_B, 1);
/* Performs operation using cublas */
float res = cublasSdot(N, d_A, 1, d_B, 1);
/* Memory clean up */
cublasFree(d_A);
cublasFree(d_B);
QueryPerformanceCounter(&t2);
double elapsedTime = (t2.QuadPart - t1.QuadPart) * 1000.0 / frequency.QuadPart;
std::cout << "GPU time = " << std::setprecision(16) << elapsedTime << std::endl;
std::cout << "GPU result = " << res << std::endl;
QueryPerformanceFrequency(&frequency);
QueryPerformanceCounter(&t1);
float sum = 0.;
for (int i = 0; i < N; i++) {
sum += h_A[i] * h_B[i];
}
QueryPerformanceCounter(&t2);
elapsedTime = (t2.QuadPart - t1.QuadPart) * 1000.0 / frequency.QuadPart;
std::cout << "CPU time = " << std::setprecision(16) << elapsedTime << std::endl;
std::cout << "CPU result = " << sum << std::endl;
free(h_A);
free(h_B);
/* Shutdown */
cublasShutdown();
getch();
return EXIT_SUCCESS;
}
When I run the code I get the following result:
GPU time = 164.7487009845991
GPU result = 8388851
CPU time = 45.22368030957917
CPU result = 7780599.5
Why using cublas library on GTX Titan is 3 times slower than calculations on one Xeon 2.4GHz IvyBridge core?
When I increase or decrease the vector sizes, I get the same results: GPU is slower than CPU. Double precision doesn't change it.

Answer 1

Because dot product is a function that uses each vector element only once. That means that the time to send it to the video card is much greater than to calculate everything on cpu, because PCIExpress is much slower than RAM.

Answer 2

I think you should read this:
http://blog.theincredibleholk.org/blog/2012/12/10/optimizing-dot-product/
There are three main points, I will briefly comment on those:
GPUs are good at hiding latencies with lots of computations (if you can balance between calculations and data transfers), here the memory is accessed a lot (bandwidth limited problem)and there isn't enough computation to hide latencies that, indeed, kill your performances.
Furthermore data is read only once so caching capabilities aren't exploited at all while CPUs are extremely good at predicting which data will be accessed next.
Plus you're also timing the allocation times.. that means PCI-E bus time which is very slow compared to main memory accesses.
All of the above render the example you just posted a case in which CPU outperform a massive parallel architecture like your GPU.
Optimizations for such a problem could be:
Keeping data on the device as much as possible
Having threads calculate more elements (and thus hide latencies)
Also: http://www.nvidia.com/object/nvidia_research_pub_001.html