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allGenes_thd.cu
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allGenes_thd.cu
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/*
* getSSSim version used in the Scientific reports paper
* Author: Hirak Kashyap
* Date: 01/24/2016
* Update Makefile to link to this source file
*/
#include <sys/time.h>
#include <stdio.h>
#include <cuda_runtime.h>
#include <string.h>
#include <math.h>
#define nrows 9084
#define ncols 28
#define sBuffer 1024
float h_data[nrows][ncols];
float h_score[nrows][nrows];
/**
* CUDA Kernel Device code
*
*/
__global__ void getSSSim(const float *data, float *score)
{
int tid_x = blockIdx.x*blockDim.x + threadIdx.x;
int tid_y = blockIdx.y*blockDim.y + threadIdx.y;
int s_grid_width = gridDim.x * blockDim.x;
int s_index = s_grid_width * tid_y + tid_x;
int d_grid_width = gridDim.z * blockDim.z;
int dx_index = d_grid_width * tid_x;
int dy_index = d_grid_width * tid_y;
float *gx_d = (float*)((char*) data + dx_index * sizeof(float));
float *gy_d = (float*)((char*) data + dy_index * sizeof(float));
//__shared__ float sum;
//float sum = 0;
float score_s = 0;
float score_l[27];
float gx[27];
float gy[27];
float gx_2min1 = gx_d[1] - gx_d[0];
float gy_2min1 = gy_d[1] - gy_d[0];
//0-26
for(int i=0; i<ncols-1; i++)
{
gx[i] = (gx_d[i+1] - gx_d[i])/gx_2min1;
gy[i] = (gy_d[i+1] - gy_d[i])/gy_2min1;
}
__syncthreads();
//0 and 26
score_l[0]=(gx[0]+gx[1]+gy[0]+gy[1])/4;
score_l[ncols-2]=(gx[ncols-3]+gx[ncols-2]+gy[ncols-3]+gy[ncols-2])/4;
//1-25
for(int i=1; i<ncols-2; i++)
{
score_l[i]=(gx[i-1]+gx[i]+gx[i+1]+gy[i-1]+gy[i]+gy[i+1])/6;
}
// __syncthreads();
int n_diff=0;
for(int i=0; i<ncols-1; i++)
{
score_l[i] = 2 * fmaxf(fabsf(score_l[i]-gx[i]),fabsf(score_l[i]-gy[i]));
if (score_l[i]>=0.00001)
{
n_diff++;
score_s = score_s + fabsf(gx[i]-gy[i])/score_l[i];
}
}
__syncthreads();
// score[s_index] = 1 - (score_s/ncols);
score[s_index] = 1 - (score_s/n_diff);
__syncthreads();
}
void populateArrays(){
char buf[sBuffer];
FILE *fp;
fp = fopen("ADNormal_onlyExp.csv", "r");
for(int i=0;i<nrows;i++)
{
fgets(buf, sizeof(buf), fp);
char *tok = strtok(buf,",");
h_data[i][0] = atof(tok);
for(int j=1;j<ncols;j++)
{
tok = strtok(NULL, ",");
h_data[i][j] = atof(tok);
}
}
}
void print_cal()
{
float maxdiff = 0;
//float score[nrows][nrows];
float score;
for(int i=0; i< nrows; i++)
{
//printf("\n");
for(int j=0; j<nrows; j++)
{
float score_s = 0;
float score_l[27];
float gx_2min1 = h_data[j][1] - h_data[j][0];
float gy_2min1 = h_data[i][1] - h_data[i][0];
//float gy_2min1 = gy[1] - gy[0];
float gx[27], gy[27];
for(int k=0; k<ncols-1; k++)
{
gx[k] = (h_data[j][k+1] - h_data[j][k])/gx_2min1;
gy[k] = (h_data[i][k+1] - h_data[i][k])/gy_2min1;
}
//__syncthreads();
//0 and 26
score_l[0]=(gx[0]+gx[1]+gy[0]+gy[1])/4;
score_l[ncols-2]=(gx[ncols-3]+gx[ncols-2]+gy[ncols-3]+gy[ncols-2])/4;
//1-25
for(int k=1; k<ncols-2; k++)
{
score_l[k]=(gx[k-1]+gx[k]+gx[k+1]+gy[k-1]+gy[k]+gy[k+1])/6;
}
//__syncthreads();
int n_diff=0;
for(int k=0; k<ncols-1; k++)
{
score_l[k] = 2 * fmaxf(fabsf(score_l[k]-gx[k]),fabsf(score_l[k]-gy[k]));
if (score_l[k]>=0.00001)
{
n_diff++;
score_s = score_s + fabsf(gx[k]-gy[k])/score_l[k];
}
//__syncthreads();
}
//score = 1 - (score_s/(ncols-1));
score = 1 - (score_s/n_diff);
//printf("%f - %f\t",score, h_score[i][j]);
if (abs(score - h_score[i][j])>maxdiff)
maxdiff = abs(score - h_score[i][j]);
/* */
}
}
printf("\nMax diff %f", maxdiff);
}
void saveResults()
{
FILE *fp;
fp =fopen("results_nonan.csv","w");
for(int i=0; i<nrows; i++)
{
float score;
for(int j=0; j<nrows-1; j++)
{
score = h_score[i][j];
if(isnan(score)){
score = 0.0;
}
fprintf(fp, "%f,", score);
}
score = h_score[i][nrows-1];
if(isnan(score)){
score = 0.0;
}
fprintf(fp, "%f\n", score);
}
fclose(fp);
}
/**
* Host main routine
*/
int
main(void)
{
printf("[ getSSSim of all genes ]");
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
timeval t0, t1, t0_cpu, t1_cpu;
//size_t height = nrows;
//size_t w_size = ncols * sizeof(float);
//size_t h_size = nrows * sizeof(float);
// Allocate the host output score matrix
// Pitches for data and score
//size_t pitch_data, pitch_score;
populateArrays();
//starts actual execution
gettimeofday(&t0, 0);
// Allocate the device input data matrix
float *d_data = NULL;
//err = cudaMallocPitch((void **)&d_data, &pitch_data, w_size, nrows);
err = cudaMalloc((void **)&d_data, nrows*ncols*sizeof(float));
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector d_data (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Allocate the device output score matrix
float *d_score = NULL;
err = cudaMalloc((void **)&d_score, nrows*nrows*sizeof(float));
//err = cudaMallocPitch((void **)&d_score, &pitch_score, h_size, nrows);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector d_score (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
//Copy input data from the host memory to the CUDA device
//err = cudaMemcpy(d_g1, h_g1, size, cudaMemcpyHostToDevice);
err = cudaMemcpy(d_data, h_data, nrows*ncols*sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector g1 from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
//launch the getSSSim kernel
dim3 blockSize;
blockSize.x = 4;
blockSize.y = 4;
blockSize.z = ncols;
dim3 gridSize;
gridSize.x = nrows/blockSize.x;
gridSize.y = nrows/blockSize.y;
getSSSim<<<gridSize, blockSize>>>(d_data, d_score);
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Copy the device result vector in device memory to the host result vector
// in host memory.
//err = cudaMemcpy2D(h_score, h_size, d_score, pitch_score, h_size, nrows, cudaMemcpyDeviceToHost);
err = cudaMemcpy(h_score, d_score, nrows*nrows*sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector g1 from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
gettimeofday(&t1, 0);
long long elapsed = (t1.tv_sec-t0.tv_sec)*1000000LL + t1.tv_usec-t0.tv_usec;
printf("\nTime elapsed GPU (microsecond):%lld\n", elapsed);
printf("\nCPU\n");
gettimeofday(&t0_cpu, 0);
print_cal();
gettimeofday(&t1_cpu, 0);
long long elapsed_cpu = (t1_cpu.tv_sec-t0_cpu.tv_sec)*1000000LL + t1_cpu.tv_usec-t0_cpu.tv_usec;
printf("\nTime elapsed CPU (microsecond):%lld\n", elapsed_cpu);
//Save results into a csv file
saveResults();
// Free device global memory
err = cudaFree(d_data);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector data (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaFree(d_score);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector score (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Free host memory
//free(h_data);
//free(h_score);
//free(h_score);
// Reset the device and exit
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
err = cudaDeviceReset();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
printf("Done\n");
return 0;
}