Channels ▼
RSS

Parallel

CUDA, Supercomputing for the Masses: Part 3


Shared Memory Version

The following source listing is for arrayReversal_multiblock_fast.cu, which I discuss in the next installment. I provide it now for your convenience so you can see how to use shared memory on this problem right now.

// includes, system
#include <stdio.h>
#include <assert.h>

// Simple utility function to check for CUDA runtime errors
void checkCUDAError(const char* msg);

// Part 2 of 2: implement the fast kernel using shared memory
__global__ void reverseArrayBlock(int *d_out, int *d_in)
{
    extern __shared__ int s_data[];

    int inOffset  = blockDim.x * blockIdx.x;
    int in  = inOffset + threadIdx.x;

    // Load one element per thread from device memory and store it 
    // *in reversed order* into temporary shared memory
    s_data[blockDim.x - 1 - threadIdx.x] = d_in[in];

    // Block until all threads in the block have 
    // written their data to shared mem
    __syncthreads();

    // write the data from shared memory in forward order, 
    // but to the reversed block offset as before

    int outOffset = blockDim.x * (gridDim.x - 1 - blockIdx.x);

    int out = outOffset + threadIdx.x;
    d_out[out] = s_data[threadIdx.x];
}
/////////////////////////////////////////////////////////////////////
// Program main
/////////////////////////////////////////////////////////////////////
int main( int argc, char** argv) 
{
    // pointer for host memory and size
    int *h_a;
    int dimA = 256 * 1024; // 256K elements (1MB total)

    // pointer for device memory
    int *d_b, *d_a;

    // define grid and block size
    int numThreadsPerBlock = 256;

    // Compute number of blocks needed based on array size 
    // and desired block size
    int numBlocks = dimA / numThreadsPerBlock;  

    // Part 1 of 2: Compute number of bytes of shared memory needed
    // This is used in the kernel invocation below
    int sharedMemSize = numThreadsPerBlock * sizeof(int);

    // allocate host and device memory
    size_t memSize = numBlocks * numThreadsPerBlock * sizeof(int);
    h_a = (int *) malloc(memSize);
    cudaMalloc( (void **) &d_a, memSize );
    cudaMalloc( (void **) &d_b, memSize );

    // Initialize input array on host
    for (int i = 0; i < dimA; ++i)
    {
        h_a[i] = i;
    }

    // Copy host array to device array
    cudaMemcpy( d_a, h_a, memSize, cudaMemcpyHostToDevice );

    // launch kernel
    dim3 dimGrid(numBlocks);
    dim3 dimBlock(numThreadsPerBlock);
    reverseArrayBlock<<< dimGrid, dimBlock, 
             sharedMemSize >>>( d_b, d_a );

    // block until the device has completed
    cudaThreadSynchronize();

    // check if kernel execution generated an error
    // Check for any CUDA errors
    checkCUDAError("kernel invocation");

    // device to host copy
    cudaMemcpy( h_a, d_b, memSize, cudaMemcpyDeviceToHost );

    // Check for any CUDA errors
    checkCUDAError("memcpy");

    // verify the data returned to the host is correct
    for (int i = 0; i < dimA; i++)
    {
        assert(h_a[i] == dimA - 1 - i );
    }

    // free device memory
    cudaFree(d_a);
    cudaFree(d_b);

    // free host memory
    free(h_a);

    // If the program makes it this far, then results are correct and
    // there are no run-time errors.  Good work!
    printf("Correct!\n");

    return 0;
}
void checkCUDAError(const char *msg)
{
    cudaError_t err = cudaGetLastError();
    if( cudaSuccess != err) 
    {
        fprintf(stderr, "Cuda error: %s: %s.\n", msg, 
                             cudaGetErrorString( err) );
        exit(EXIT_FAILURE);
    }                         
}

In the next column, I begin looking at the use of shared memory to increase performance. Until then, I suggest looking into the CUDA memory types -- specifically __shared__, __constant__, and register memory.

For More Information

Click here for more information on CUDA and here for more information on NVIDIA.


Rob Farber is a senior scientist at Pacific Northwest National Laboratory. He has worked in massively parallel computing at several national laboratories and as co-founder of several startups. He can be reached at rmfarber@gmail.com.


Related Reading


More Insights






Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

Dr. Dobb's encourages readers to engage in spirited, healthy debate, including taking us to task. However, Dr. Dobb's moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing or spam. Dr. Dobb's further reserves the right to disable the profile of any commenter participating in said activities.

 
Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.
 

Video