GPU code running slower than CPU version

Question

I am working on an application which divides a string into pieces and assigns each to a block. Within each block the the text is scanned character by character and a shared array of int, D is to be updated by different threads in parallel based on the character read. At the end of each iteration the last element of D is checked, and if it satisfied the condition, a global int array m is set to 1 at the position corresponding to the text. This code was executed on a NVIDIA GEForce Fermi 550, and runs even slower than the CPU version. I have just included the kernel here:

__global__ void match(uint32_t* BB_d,const char* text_d,int n, int m,int k,int J,int lc,int start_addr,int tBlockSize,int overlap ,int* matched){
    __shared__ int D[MAX_THREADS+2];
    __shared__ char Text_S[MAX_PATTERN_SIZE];
    __shared__ int DNew[MAX_THREADS+2];
    __shared__ int BB_S[4][MAX_THREADS];
    int w=threadIdx.x+1;

    for(int i=0;i<4;i++)
    {
        BB_S[i][threadIdx.x]= BB_d[i*J+threadIdx.x];
    }

    {
        D[threadIdx.x] = 0;
        {
            D[w] = (1<<(k+1)) -1;

            for(int i = 0; i < lc - 1; i++)
            {
                D[w] = (D[w] << k+2) + (1<<(k+1)) -1;
            }
        }
        D[J+1] = (1<<((k+2)*lc)) - 1;
    }
    int startblock=(blockIdx.x == 0?start_addr:(start_addr+(blockIdx.x * (tBlockSize-overlap))));
    int size= (((startblock + tBlockSize) > n )? ((n- (startblock))):( tBlockSize));

    int copyBlock=(size/J)+ ((size%J)==0?0:1);
    if((threadIdx.x * copyBlock) <= size)
        memcpy(Text_S+(threadIdx.x*copyBlock),text_d+(startblock+threadIdx.x*copyBlock),(((((threadIdx.x*copyBlock))+copyBlock) > size)?(size-(threadIdx.x*copyBlock)):copyBlock));
    memcpy(DNew, D, (J+2)*sizeof(int));
    __syncthreads();
    uint32_t initial = D[1];
    uint32_t x;
    uint32_t mask = 1;
    for(int i = 0; i < lc - 1; i++)mask = (mask<<(k+2)) + 1;
    for(int i = 0; i < size;i++)
    {
        {
            x =  ((D[w] >> (k+2)) | (D[w - 1] << ((k + 2)* (lc - 1))) | (BB_S[(((int)Text_S[i])/2)%4][w-1])) & ((1 << (k + 2)* lc) - 1);
            DNew[w] = ((D[w]<<1) | mask)
                & (((D[w] << k+3) | mask|((D[w +1] >>((k+2)*(lc - 1)))<<1)))
                & (((x + mask) ^ x) >> 1)
                & initial;
        }
        __syncthreads();
        memcpy(D, DNew, (J+2)*sizeof(int));
        if(!(D[J] & 1<<(k + (k + 2)*(lc*J -m + k ))))
        {
            matched[startblock+i] = 1;
            D[J] |= ((1<<(k + 1 + (k + 2)*(lc*J -m + k ))) - 1);
        }
    }
}

I am not very familiar with CUDA so I dont quite understand issues such as shared memory bank conflicts. Could that be the bottleneck here?

As asked, this is the code where I launch the kernels:

#include <stdio.h>
#include <assert.h>
#include <cuda.h>
#define uint32_t unsigned int
#define MAX_THREADS 512
#define MAX_PATTERN_SIZE 1024
#define MAX_BLOCKS 8
#define MAX_STREAMS 16
#define TEXT_MAX_LENGTH 1000000000
void calculateBBArray(uint32_t** BB,const char* pattern_h,int m,int k , int lc , int J){};
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);
    }    
}
char* getTextString() {
 FILE *input, *output;
 char c;
 char * inputbuffer=(char *)malloc(sizeof(char)*TEXT_MAX_LENGTH);

int numchars = 0, index  = 0;

input = fopen("sequence.fasta", "r");
c = fgetc(input);
while(c != EOF)
{
inputbuffer[numchars] = c;
numchars++;
c = fgetc(input);
}
fclose(input);
inputbuffer[numchars] = '\0'; 
return inputbuffer;
}

int main(void) {
const char pattern_h[] = "TACACGAGGAGAGGAGAAGAACAACGCGACAGCAGCAGACTTTTTTTTTTTTACAC";
char * text_h=getTextString();  //reading text from file, supported upto 200MB currently

int k = 13;
int i;
int count=0;
char *pattern_d, *text_d;     // pointers to device memory
char* text_new_d;
int* matched_d;
int* matched_new_d;
uint32_t* BB_d;
uint32_t* BB_new_d;
int* matched_h = (int*)malloc(sizeof(int)* strlen(text_h));
cudaMalloc((void **) &pattern_d, sizeof(char)*strlen(pattern_h)+1);
cudaMalloc((void **) &text_d, sizeof(char)*strlen(text_h)+1);
cudaMalloc((void **) &matched_d, sizeof(int)*strlen(text_h));
cudaMemcpy(pattern_d, pattern_h, sizeof(char)*strlen(pattern_h)+1, cudaMemcpyHostToDevice);
cudaMemcpy(text_d, text_h, sizeof(char)*strlen(text_h)+1, cudaMemcpyHostToDevice);
cudaMemset(matched_d, 0,sizeof(int)*strlen(text_h));

int m = strlen(pattern_h);
int n = strlen(text_h);

uint32_t* BB_h[4];
    unsigned int maxLc = ((((m-k)*(k+2)) > (31))?(31/(k+2)):(m-k));
unsigned int lc=2;   // Determines the number of threads per block
    // can be varied upto maxLc for tuning performance
if(lc>maxLc)
{
    exit(0);
}
unsigned int noWordorNfa =((m-k)/lc) + (((m-k)%lc)  == 0?0:1);
cudaMalloc((void **) &BB_d, sizeof(int)*noWordorNfa*4);
if(noWordorNfa >= MAX_THREADS)
{
    printf("Error: max threads\n");
    exit(0);
}

calculateBBArray(BB_h,pattern_h,m,k,lc,noWordorNfa);  // not included this function

for(i=0;i<4;i++)
{
    cudaMemcpy(BB_d+ i*noWordorNfa, BB_h[i], sizeof(int)*noWordorNfa, cudaMemcpyHostToDevice);
}
int overlap=m;
int textBlockSize=(((m+k+1)>n)?n:(m+k+1));
cudaStream_t stream[MAX_STREAMS];
for(i=0;i<MAX_STREAMS;i++) {
    cudaStreamCreate( &stream[i] );
    }

int start_addr=0,index=0,maxNoBlocks=0;
if(textBlockSize>n)
{
    maxNoBlocks=1;
}
else
{
     maxNoBlocks=((1 + ((n-textBlockSize)/(textBlockSize-overlap)) + (((n-textBlockSize)%(textBlockSize-overlap)) == 0?0:1)));
}
int kernelBlocks = ((maxNoBlocks > MAX_BLOCKS)?MAX_BLOCKS:maxNoBlocks);
int blocksRemaining =maxNoBlocks;
printf(" maxNoBlocks %d kernel Blocks %d \n",maxNoBlocks,kernelBlocks);
while(blocksRemaining >0)
{
kernelBlocks = ((blocksRemaining > MAX_BLOCKS)?MAX_BLOCKS:blocksRemaining);
printf(" Calling %d Blocks with starting Address %d , textBlockSize %d \n",kernelBlocks,start_addr,textBlockSize);
match<<<kernelBlocks,noWordorNfa,0,stream[(index++)%MAX_STREAMS]>>>(BB_d,text_d,n,m,k,noWordorNfa,lc,start_addr,textBlockSize,overlap,matched_d);
start_addr+=kernelBlocks*(textBlockSize-overlap);;
blocksRemaining -= kernelBlocks;
}
cudaMemcpy(matched_h, matched_d, sizeof(int)*strlen(text_h), cudaMemcpyDeviceToHost);
checkCUDAError("Matched Function");
for(i=0;i<MAX_STREAMS;i++)
    cudaStreamSynchronize( stream[i] ); 
    // do stuff with matched
    // ....
    // ....
free(matched_h);cudaFree(pattern_d);cudaFree(text_d);cudaFree(matched_d);
    return 0;

}

Number of threads launched per block depends upon the length pattern_h(could be at most maxLc above). I expect it to be around 30 in this case. Shoudn't that be enough to see a good amount of concurrency? As for blocks, I see no point in launching more than MAX_BLOCKS (=10) at a time since the hardware can schedule only 8 simultaneously

NOTE: I don't have GUI access.

Answer 1

I found that I was copying the whole array Dnew to D in each thread rather than copying only the portion each thread was supposed to update D[w]. This would cause the threads to execute serially, although I don't know if it could be called a shared memory bank conflict. Now it gives 8-9x speedup for large enough patterns(=more threads). This is much less than what I expected. I will try to increase number of blocks as suggested. I dont know how to increase the # of threads

Answer 2

With all the shared memory you're using, you could be running into bank conflicts if consecutive threads are not reading from consecutive addresses in the shared arrays ... that could cause serialization of the memory accesses, which in turn will kill the parallel performance of your algorithm.

Answer 3

I breifly looked at your code but it looks like your sending data to the gpu back and forth creating a bottle neck on the bus? did you try profiling it?