Max GPU kernel function only work with one block - cuda

using namespace std;
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
const int threadsPerBlock = 256;
const int N = 40000;
void generateArray(double *data, int count) {
for (int i = 0; i < count; i++)
data[i] = rand() / ((rand() + rand()) / 2.0 + 1);
}
double maxCPU(double *arr, int count) {
int max = arr[0];
for (int i = 0; i < count; i++)
if (arr[i] > max)
max = arr[i];
return max;
}
__global__ void MaxGPU(double *a, int count, double *result){
__shared__ double cache[threadsPerBlock];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int cacheIndex = threadIdx.x;
int temp = a[tid];
tid+= blockDim.x * gridDim.x;
while(tid < count){
if(a[tid] > temp)
temp = a[tid];
tid+= blockDim.x * gridDim.x;
}
cache[cacheIndex] = temp;
__syncthreads();
int i = blockDim.x/2;
while(i!=0){
if(cacheIndex < i)
if(cache[cacheIndex + i] > cache[cacheIndex])
cache[cacheIndex] = cache[cacheIndex + i];
__syncthreads();
i/=2;
}
if(cacheIndex == 0)
result[blockIdx.x] = cache[0];
}
int main(void) {
double *arr = new double[N], resultGPU;
generateArray(arr, N);
double *devA, *dev_partial_result;
double resultCPU = maxCPU(arr, N);
cudaMalloc((void**)&devA, N * sizeof(double));
cudaMalloc((void**)&dev_partial_result, 512 * sizeof(double));
cudaMemcpy(devA, arr, N * sizeof(double), cudaMemcpyHostToDevice);
MaxGPU<<<1, 256>>>(devA, N, dev_partial_result);
cudaMemcpy(&resultGPU, dev_partial_result,sizeof(double), cudaMemcpyDeviceToHost);
cout << "Max CPU: " << resultCPU << endl;
cout << "Max GPU: " << resultGPU << endl;
cudaFree(devA);
cudaFree(dev_partial_result);
delete [] arr;
return 0;
}
I wrote above code. I don't why but it only works with one block. It does not work with say, 256 or 512 blocks. Why? What's wrong?

Try change
double resultGPU; to
double* resultGPU = new double[blocks_count];
and
cudaMemcpy(&resultGPU, dev_partial_result,sizeof(double), cudaMemcpyDeviceToHost); to
cudaMemcpy(resultGPU, dev_partial_result,blocks_count*sizeof(double), cudaMemcpyDeviceToHost);

Related

Dot product in Cuda by example does not work for me

I'm starting to read "Cuda By Example" Book and I've been a problem with the dot example using "shared memory". I copy-paste the example from the book and I set: N = x * 1024; threadsPerBlock = 32; blocksPerGrid = 8. Where I test the "x" values with 2, 3, 4, 5.
If I set x = 3, the result is bad, but when I used x = 2,4,5 all is ok. I don't understand where is the problem. The code is:
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#define imin(a, b) (a<b?a:b)
#define sum_squares(x) (x*(x+1)*(2*x+1)/6)
const int x = 3;
const int N = 3 * 1024;
const int threadsPerBlock = 32;
const int blocksPerGrid = 8;
__global__ void dot(float *a, float *b, float *c)
{
__shared__ float cache[threadsPerBlock];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int cacheIndex = threadIdx.x;
float temp = 0;
while (tid < N)
{
temp += a[tid] * b[tid];
tid += blockDim.x * gridDim.x;
}
cache[cacheIndex] = temp;
__syncthreads();
int i = blockDim.x / 2;
while (i != 0)
{
if (cacheIndex < i)
cache[cacheIndex] += cache[cacheIndex + i];
__syncthreads();
i /= 2;
}
if (cacheIndex == 0)
c[blockIdx.x] = cache[0];
}
int main()
{
float *a, *b, *partial_c, result;
float *d_a, *d_b, *d_partial_c;
a = (float *)malloc(N * sizeof(float));
b = (float *)malloc(N * sizeof(float));
partial_c = (float *)malloc(blocksPerGrid * sizeof(float));
cudaMalloc((void **)&d_a, N * sizeof(float));
cudaMalloc((void **)&d_b, N * sizeof(float));
cudaMalloc((void **)&d_partial_c, blocksPerGrid * sizeof(float));
for (int i = 0; i < N; i++)
{
a[i] = i;
b[i] = 2 * i;
}
cudaMemcpy(d_a, a, N * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, N * sizeof(float), cudaMemcpyHostToDevice);
dot << <blocksPerGrid, threadsPerBlock >> >(d_a, d_b, d_partial_c);
cudaMemcpy(partial_c, d_partial_c, blocksPerGrid * sizeof(float), cudaMemcpyDeviceToHost);
result = 0;
for (int i = 0; i < blocksPerGrid; i++)
result += partial_c[i];
if (2 * sum_squares((float)(N - 1)) == result)
printf(":)\n");
else
printf(":(\n");
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_partial_c);
free(a);
free(b);
free(partial_c);
getchar();
return 0;
}
Because float does not have enough precision, which is ~7 decimal digits only. But for x=3; your expected result is
19317916672
containing 11 digits.
for x=4,5, the results are bad on my machine too.

cuda calc distance of two points

Here I want to calculate the distance of each two points, and decide if they are neighbours. here is my simple code in cuda.
__global__ void calcNeighbors(const DataPoint* points,
const float doubleRadius, bool* neighbors) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
float dis = 0.0f;
while (tid < N) {
DataPoint p1 = points[tid];
for (int i=0; i<N; i++) {
DataPoint p2 = points[i];
dis = 0;
dis += (p1.pfDimens[0]-p2.pfDimens[0]) * (p1.pfDimens[0]-p2.pfDimens[0]) +
(p1.pfDimens[1]-p2.pfDimens[1]) * (p1.pfDimens[1]-p2.pfDimens[1]) +
(p1.pfDimens[2]-p2.pfDimens[2]) * (p1.pfDimens[2]-p2.pfDimens[2]);
if (dis <= doubleRadius) {
neighbors[tid*N+i] = true;
} else {
neighbors[tid*N+i] = false;
}
}
tid += blockDim.x * gridDim.x;
}
}
The DataPoint is a struct is
typedef struct DataPoint {
float pfDimens[3];
} DataPoint;
so here i want to reduce the time, How can i do? I have tried to use memory coalesing and share memory, but i didn't get a good speed up?
===============use share memory==============
__global__ void calcNeighbors2(const DataPoint* points,
const float doubleRadius, bool* neighbors) {
__shared__ DataPoint sharedpoints[threadsPerBlock];
int start = blockIdx.x * blockDim.x;
int len = start+threadIdx.x;
if (len < N) {
sharedpoints[threadIdx.x] = points[len];
}
len = imin(N, blockDim.x + start);
__syncthreads();
int tid = threadIdx.x;
float dis;
while (tid < N) {
DataPoint p1 = points[tid];
for (int i=start; i<len; i++) {
dis = 0;
dis += (p1.pfDimens[0]-sharedpoints[i-start].pfDimens[0]) * (p1.pfDimens[0]-sharedpoints[i-start].pfDimens[0]) +
(p1.pfDimens[1]-sharedpoints[i-start].pfDimens[1]) * (p1.pfDimens[1]-sharedpoints[i-start].pfDimens[1]) +
(p1.pfDimens[2]-sharedpoints[i-start].pfDimens[2]) * (p1.pfDimens[2]-sharedpoints[i-start].pfDimens[2]);
if (dis <= doubleRadius) {
neighbors[i*N+tid] = true;
} else {
neighbors[i*N+tid] = false;
}
}
tid += blockDim.x;
}
}
Here i changed the neighbors[tid*N+i] to neighbors[i*N+tid], it give me amlost 8x speed up on Tesla K10.G2.8GB. But when i use share memory to store some points, it is no use?
There are at least 4 ideas, some of which have already been stated in the comments:
Transform your point distance storage from AoS format:
struct DataPoint {
float pfDimens[3];
};
to SoA format:
struct DataPoint {
float pfDimens_x[NPTS];
float pfDimens_y[NPTS];
float pfDimens_z[NPTS];
};
this will enable full coalescing on loading of the data. In fact, to help with point 4 below, I would just switch to using 3 bare arrays, rather than a structure.
reduce the computation to (slightly less than) half:
for (int i=N-1; i>tid; i--) {
then, either in the thread code itself, or in the host, you can populate the other "half" of the output matrix by copying data.
Transpose the storage in your output matrix, so that you can write a storage operation like this:
neighbors[i*N+tid] = true;
which will nicely coalesce, as opposed to this:
neighbors[tid*N+i] = true;
which will not.
Since your input point data is read only, mark the kernel parameter appropriately:
const float * __restrict__ points_x, const float * __restrict__ points_y, const float * __restrict__ points_z
in some cases, and on some GPUs, this will often lead to a speed-up due to use of the read-only cache. If you really want to get aggressive with caching, and your data array is small enough (4K or less float points), you could put a copy of the point data in global memory as well as a copy in __constant__ memory, and load the "uniform" load you are doing here through constant memory:
DataPoint p2 = c_points[i];
thus you could perform the coalesced load through the read-only cache, the uniform load through the constant cache, and the coalesced store going to ordinary global memory.
On a K40c, on linux/CUDA 7, for N = 4096, the net effect of these changes appears to be about a 3.5x speedup, at the kernel level:
$ cat t749.cu
#include <stdio.h>
#define N 4096
// if N is 16K/3 or less, we can use constant
#define USE_CONSTANT
#define THRESH 0.2f
#define nTPB 256
#define nBLK (N/nTPB+1)
#define cudaCheckErrors(msg) \
do { \
cudaError_t __err = cudaGetLastError(); \
if (__err != cudaSuccess) { \
fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
msg, cudaGetErrorString(__err), \
__FILE__, __LINE__); \
fprintf(stderr, "*** FAILED - ABORTING\n"); \
exit(1); \
} \
} while (0)
#include <time.h>
#include <sys/time.h>
#define USECPSEC 1000000ULL
unsigned long long dtime_usec(unsigned long long start){
timeval tv;
gettimeofday(&tv, 0);
return ((tv.tv_sec*USECPSEC)+tv.tv_usec)-start;
}
struct DataPoint {
float pfDimens[3];
};
__global__ void calcNeighbors(const DataPoint* points,
const float doubleRadius, bool* neighbors) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
float dis = 0.0f;
while (tid < N) {
DataPoint p1 = points[tid];
for (int i=0; i<N; i++) {
DataPoint p2 = points[i];
dis = 0;
dis += (p1.pfDimens[0]-p2.pfDimens[0]) * (p1.pfDimens[0]-p2.pfDimens[0]) +
(p1.pfDimens[1]-p2.pfDimens[1]) * (p1.pfDimens[1]-p2.pfDimens[1]) +
(p1.pfDimens[2]-p2.pfDimens[2]) * (p1.pfDimens[2]-p2.pfDimens[2]);
if (dis <= doubleRadius) {
neighbors[tid*N+i] = true;
} else {
neighbors[tid*N+i] = false;
}
}
tid += blockDim.x * gridDim.x;
}
}
#ifdef USE_CONSTANT
__constant__ float cpx[N];
__constant__ float cpy[N];
__constant__ float cpz[N];
#endif
__global__ void calcNeighbors2(const float * __restrict__ pts_x, const float * __restrict__ pts_y, const float * __restrict__ pts_z, const float doubleRadius, bool * __restrict__ neighbors) {
int tid = threadIdx.x+blockDim.x*blockIdx.x;
while (tid < N) {
float p1x = pts_x[tid];
float p1y = pts_y[tid];
float p1z = pts_z[tid];
for (int i = N-1; i > tid; i--){
float p2x, p2y, p2z;
#ifdef USE_CONSTANT
p2x = cpx[i];
p2y = cpy[i];
p2z = cpz[i];
#else
p2x = pts_x[i];
p2y = pts_y[i];
p2z = pts_z[i];
#endif
float dis = ((p1x-p2x)*(p1x-p2x)) + ((p1y-p2y)*(p1y-p2y)) + ((p1z-p2z)*(p1z-p2z));
neighbors[i*N+tid] = (dis <= doubleRadius);
}
tid += blockDim.x * gridDim.x;
}
}
int main(){
float *dx, *dy, *dz, *hx, *hy, *hz;
DataPoint *dp, *hp;
bool *dn, *hn1, *hn2;
hx =(float *)malloc(N*sizeof(float));
hy =(float *)malloc(N*sizeof(float));
hz =(float *)malloc(N*sizeof(float));
hp =(DataPoint *)malloc(N*sizeof(DataPoint));
hn1=(bool *)malloc(N*N*sizeof(bool));
hn2=(bool *)malloc(N*N*sizeof(bool));
cudaMalloc(&dx, N*sizeof(float));
cudaMalloc(&dy, N*sizeof(float));
cudaMalloc(&dz, N*sizeof(float));
cudaMalloc(&dp, N*sizeof(DataPoint));
cudaMalloc(&dn, N*N*sizeof(bool));
for (int i =0; i < N; i++){
hx[i] = rand()/(float)RAND_MAX;
hy[i] = rand()/(float)RAND_MAX;
hz[i] = rand()/(float)RAND_MAX;
hp[i].pfDimens[0] = hx[i];
hp[i].pfDimens[1] = hy[i];
hp[i].pfDimens[2] = hz[i];}
cudaMemcpy(dx, hx, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dy, hy, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dz, hz, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dp, hp, N*sizeof(DataPoint), cudaMemcpyHostToDevice);
// warm-up
calcNeighbors<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaMemset(dn, 0, N*N*sizeof(bool));
unsigned long long t1 = dtime_usec(0);
calcNeighbors<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaCheckErrors("kernel 1 error");
t1 = dtime_usec(t1);
cudaMemcpy(hn1, dn, N*N*sizeof(bool), cudaMemcpyDeviceToHost);
// warm-up
calcNeighbors2<<<nBLK, nTPB>>>(dx, dy, dz, THRESH, dn);
cudaDeviceSynchronize();
cudaMemset(dn, 0, N*N*sizeof(bool));
unsigned long long t2 = dtime_usec(0);
calcNeighbors2<<<nBLK, nTPB>>>(dx, dy, dz, THRESH, dn);
cudaDeviceSynchronize();
cudaCheckErrors("kernel 2 error");
t2 = dtime_usec(t2);
cudaMemcpy(hn2, dn, N*N*sizeof(bool), cudaMemcpyDeviceToHost);
cudaCheckErrors("some error");
printf("t1: %fs, t2: %fs\n", t1/(float)USECPSEC, t2/(float)USECPSEC);
// results validation
for (int i = 0; i < N; i++)
for (int j = i+1; j < N; j++)
if (hn1[i*N+j] != hn2[j*N+i]) {printf("mismatch at %d, %d, was: %d, should be: %d\n", i, j, hn2[j*N+i], hn1[i*N+j]); return 1;}
return 0;
}
$ nvcc -arch=sm_35 -o t749 t749.cu
$ ./t749
t1: 0.004903s, t2: 0.001395s
$
In the case of K40c, the limited number of blocks being launched above (16) is a significant impediment to performance, due to latency. If we comment out the USE_CONSTANT define, and change N to 16384, we observe an even higher speedup with the improved kernel:
$ ./t749
t1: 0.267107s, t2: 0.008209s
$
the resultant ~48 blocks being enough to approximately "fill" the K40c which has 15 SMs.
EDIT: now that you've posted a shared memory kernel, I added it to my test case as calcNeighbors3 and compared it's timing performance (as t3). It is almost as fast as my kernel, and it seems to provide the correct result (matches your original kernel) so I'm not sure what your concerns are.
Here's the updated code and test case:
$ cat t749.cu
#include <stdio.h>
#include <math.h>
#define imin(X,Y) ((X)<(Y))?(X):(Y)
#define N 32768
// if N is 16K/3 or less, we can use constant
// #define USE_CONSTANT
#define THRESH 0.2f
#define nTPB 256
#define nBLK (N/nTPB+1)
#define cudaCheckErrors(msg) \
do { \
cudaError_t __err = cudaGetLastError(); \
if (__err != cudaSuccess) { \
fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
msg, cudaGetErrorString(__err), \
__FILE__, __LINE__); \
fprintf(stderr, "*** FAILED - ABORTING\n"); \
exit(1); \
} \
} while (0)
#include <time.h>
#include <sys/time.h>
#define USECPSEC 1000000ULL
unsigned long long dtime_usec(unsigned long long start){
timeval tv;
gettimeofday(&tv, 0);
return ((tv.tv_sec*USECPSEC)+tv.tv_usec)-start;
}
struct DataPoint {
float pfDimens[3];
};
__global__ void calcNeighbors(const DataPoint* points,
const float doubleRadius, bool* neighbors) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
float dis = 0.0f;
while (tid < N) {
DataPoint p1 = points[tid];
for (int i=0; i<N; i++) {
DataPoint p2 = points[i];
dis = 0;
dis += (p1.pfDimens[0]-p2.pfDimens[0]) * (p1.pfDimens[0]-p2.pfDimens[0]) +
(p1.pfDimens[1]-p2.pfDimens[1]) * (p1.pfDimens[1]-p2.pfDimens[1]) +
(p1.pfDimens[2]-p2.pfDimens[2]) * (p1.pfDimens[2]-p2.pfDimens[2]);
if (dis <= doubleRadius) {
neighbors[tid*N+i] = true;
} else {
neighbors[tid*N+i] = false;
}
}
tid += blockDim.x * gridDim.x;
}
}
#ifdef USE_CONSTANT
__constant__ float cpx[N];
__constant__ float cpy[N];
__constant__ float cpz[N];
#endif
__global__ void calcNeighbors2(const float * __restrict__ pts_x, const float * __restrict__ pts_y, const float * __restrict__ pts_z, const float doubleRadius, bool * __restrict__ neighbors) {
int tid = threadIdx.x+blockDim.x*blockIdx.x;
while (tid < N) {
float p1x = pts_x[tid];
float p1y = pts_y[tid];
float p1z = pts_z[tid];
for (int i = N-1; i > tid; i--){
float p2x, p2y, p2z;
#ifdef USE_CONSTANT
p2x = cpx[i];
p2y = cpy[i];
p2z = cpz[i];
#else
p2x = pts_x[i];
p2y = pts_y[i];
p2z = pts_z[i];
#endif
float dis = ((p1x-p2x)*(p1x-p2x)) + ((p1y-p2y)*(p1y-p2y)) + ((p1z-p2z)*(p1z-p2z));
neighbors[i*N+tid] = (dis <= doubleRadius);
}
tid += blockDim.x * gridDim.x;
}
}
__global__ void calcNeighbors3(const DataPoint* points,
const float doubleRadius, bool* neighbors) {
__shared__ DataPoint sharedpoints[nTPB];
int start = blockIdx.x * blockDim.x;
int len = start+threadIdx.x;
if (len < N) {
sharedpoints[threadIdx.x] = points[len];
}
len = imin(N, blockDim.x + start);
__syncthreads();
int tid = threadIdx.x;
float dis;
while (tid < N) {
DataPoint p1 = points[tid];
for (int i=start; i<len; i++) {
dis = 0;
dis += (p1.pfDimens[0]-sharedpoints[i-start].pfDimens[0]) * (p1.pfDimens[0]-sharedpoints[i-start].pfDimens[0]) +
(p1.pfDimens[1]-sharedpoints[i-start].pfDimens[1]) * (p1.pfDimens[1]-sharedpoints[i-start].pfDimens[1]) +
(p1.pfDimens[2]-sharedpoints[i-start].pfDimens[2]) * (p1.pfDimens[2]-sharedpoints[i-start].pfDimens[2]);
if (dis <= doubleRadius) {
neighbors[i*N+tid] = true;
} else {
neighbors[i*N+tid] = false;
}
}
tid += blockDim.x;
}
}
int main(){
float *dx, *dy, *dz, *hx, *hy, *hz;
DataPoint *dp, *hp;
bool *dn, *hn1, *hn2, *hn3;
hx =(float *)malloc(N*sizeof(float));
hy =(float *)malloc(N*sizeof(float));
hz =(float *)malloc(N*sizeof(float));
hp =(DataPoint *)malloc(N*sizeof(DataPoint));
hn1=(bool *)malloc(N*N*sizeof(bool));
hn2=(bool *)malloc(N*N*sizeof(bool));
hn3=(bool *)malloc(N*N*sizeof(bool));
cudaMalloc(&dx, N*sizeof(float));
cudaMalloc(&dy, N*sizeof(float));
cudaMalloc(&dz, N*sizeof(float));
cudaMalloc(&dp, N*sizeof(DataPoint));
cudaMalloc(&dn, N*N*sizeof(bool));
for (int i =0; i < N; i++){
hx[i] = rand()/(float)RAND_MAX;
hy[i] = rand()/(float)RAND_MAX;
hz[i] = rand()/(float)RAND_MAX;
hp[i].pfDimens[0] = hx[i];
hp[i].pfDimens[1] = hy[i];
hp[i].pfDimens[2] = hz[i];}
cudaMemcpy(dx, hx, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dy, hy, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dz, hz, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dp, hp, N*sizeof(DataPoint), cudaMemcpyHostToDevice);
#ifdef USE_CONSTANT
cudaMemcpyToSymbol(cpx, hx, N*sizeof(float));
cudaMemcpyToSymbol(cpy, hy, N*sizeof(float));
cudaMemcpyToSymbol(cpz, hz, N*sizeof(float));
#endif
// warm-up
calcNeighbors<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaMemset(dn, 0, N*N*sizeof(bool));
unsigned long long t1 = dtime_usec(0);
calcNeighbors<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaCheckErrors("kernel 1 error");
t1 = dtime_usec(t1);
cudaMemcpy(hn1, dn, N*N*sizeof(bool), cudaMemcpyDeviceToHost);
// warm-up
calcNeighbors2<<<nBLK, nTPB>>>(dx, dy, dz, THRESH, dn);
cudaDeviceSynchronize();
cudaMemset(dn, 0, N*N*sizeof(bool));
unsigned long long t2 = dtime_usec(0);
calcNeighbors2<<<nBLK, nTPB>>>(dx, dy, dz, THRESH, dn);
cudaDeviceSynchronize();
cudaCheckErrors("kernel 2 error");
t2 = dtime_usec(t2);
cudaMemcpy(hn2, dn, N*N*sizeof(bool), cudaMemcpyDeviceToHost);
// warm-up
calcNeighbors3<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaMemset(dn, 0, N*N*sizeof(bool));
unsigned long long t3 = dtime_usec(0);
calcNeighbors3<<<nBLK, nTPB>>>(dp, THRESH, dn);
cudaDeviceSynchronize();
cudaCheckErrors("kernel 3 error");
t3 = dtime_usec(t3);
cudaMemcpy(hn3, dn, N*N*sizeof(bool), cudaMemcpyDeviceToHost);
cudaCheckErrors("some error");
printf("t1: %fs, t2: %fs, t3: %fs\n", t1/(float)USECPSEC, t2/(float)USECPSEC, t3/(float)USECPSEC);
// results validation
for (int i = 0; i < N; i++)
for (int j = i+1; j < N; j++)
if (hn1[i*N+j] != hn2[j*N+i]) {printf("1:2 mismatch at %d, %d, was: %d, should be: %d\n", i, j, hn2[j*N+i], hn1[i*N+j]); return 1;}
for (int i = 0; i < N*N; i++)
if (hn1[i] != hn3[i]) {printf("1:3 mismatch at %d, was: %d, should be: %d\n", i, hn1[i], hn3[i]); return 1;}
return 0;
}
$ nvcc -arch=sm_35 -o t749 t749.cu
$ ./t749
t1: 1.260010s, t2: 0.022661s, t3: 0.029632s
$
For this test, I have changed the data set size to 32768 since that is closer to the range you care about. Your shared memory kernel shows about a 42x speedup over your original kernel, and my kernel shows about a 55x speedup, on my K40c.

From given vector find max value and its index by reduction method in CUDA

I am new to CUDA, for the vector to find max value and its index I use CUDA
here its my code:
#include < cuda.h >
#include < stdio.h >
#include < time.h >
#include <iostream>
using namespace std;
#define tbp 256
#define nblocks 1
__global__ void kernel_max(int *a, int *d, int *index,int *idx)
{
__shared__ int sdata[tbp]; //"static" shared memory
int tid = threadIdx.x;
int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = a[i];
index[tid] = i;
__syncthreads();
for(int s=tbp/2 ; s >= 1 ; s=s/2)
{
if(tid < s)
{
if(sdata[tid] < sdata[tid + s])
{
sdata[tid] = sdata[tid + s];
index[tid] = index[tid+s];
__syncthreads();
}
__syncthreads();
}
__syncthreads();
}
__syncthreads();
if(tid == 0 )
{
d[blockIdx.x] = sdata[0];
idx[blockIdx.x] = index[0];
}
__syncthreads();
}
int main()
{
int i;
const int N=tbp*nblocks;
srand(time(NULL));
int *a;
a = (int*)malloc(N * sizeof(int));
int *d;
d = (int*)malloc(nblocks * sizeof(int));
int *index;
index = (int*)malloc(N * sizeof(int));
int *idx;
idx = (int*)malloc(nblocks * sizeof(int));
int *dev_a, *dev_d, *dev_index,*dev_idx;
cudaMalloc((void **) &dev_a, N*sizeof(int));
cudaMalloc((void **) &dev_d, nblocks*sizeof(int));
cudaMalloc((void **) &dev_index, N*sizeof(int));
cudaMalloc((void **) &dev_idx, nblocks*sizeof(int));
int mmm=0;
int ddd=0;
for( i = 0 ; i < N ; i++)
{
a[i] = rand()% 100 + 5;
index[i]=i;
//printf("%d\n",a[i]);
if(mmm<a[i])
{
mmm=a[i];
ddd=i;
}
}
printf("");
printf("");
printf("");
printf("");
cudaMemcpy(dev_a , a, N*sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(dev_index , index, N*sizeof(int),cudaMemcpyHostToDevice);
kernel_max <<< nblocks,tbp >>>(dev_a,dev_d,dev_index,dev_idx);
cudaMemcpy(d, dev_d, nblocks*sizeof(int),cudaMemcpyDeviceToHost);
cudaMemcpy(index, dev_index, N*sizeof(int),cudaMemcpyDeviceToHost);
cudaMemcpy(idx, dev_idx, nblocks*sizeof(int),cudaMemcpyDeviceToHost);
printf("cpu max= %d, gpu_max = %d ,cpu index: %d, gpu index: %d",mmm,d[0],ddd,idx[0]);
printf("\n");
if(ddd!=idx[0])
{
cout<<"index mismatch!damn!!"<<endl;
}
else
{
cout<<"congratulations!!"<<endl;
}
/*
for(i=0;i<N;i++)
cout<<*(index+i)<<endl;
*/
cudaFree(dev_a);
cudaFree(dev_d);
cudaFree(dev_index);
cudaFree(dev_idx);
free(a);
free(d);
free(index);
free(idx);
return 0;
}
The problem is that for the tbp < 128 it can get correct result both in value and index
when increase to 256,512,1024, the result will sometimes go wrong.
Can anyone given a explanation for this situation?Thanks.
Use another loop to deal with the index to avoid same max value with different index problem in this computation
int temp=0;
for(i=0;i<tbp;i++)
{
if(d[blockIdx.x]==a[i] && temp==0)
{temp = i;}
}
idx[0] = temp;
you need set int temp= -1 instead 0 to avoid the case of maximum value lcoated at 0.

CUDA Performance - Always return different values

This my code:
using namespace std;
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
const int N = 8000;
void fillArray(int *data, int count) {
for (int i = 0; i < count; i++)
data[i] = rand() % 100;
}
__global__ void add(int* a, int *b, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = a[tid] + b[tid];
}
}
__global__ void subtract(int* a, int *b, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = a[tid] - b[tid];
}
}
__global__ void multiply(int* a, int *b, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = a[tid] * b[tid];
}
}
__global__ void divide(int* a, int *b, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = a[tid] / b[tid];
}
}
__global__ void modu(int* a, int *b, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = a[tid] % b[tid];
}
}
__global__ void neg(int *data, int *c) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N) {
c[tid] = -data[tid];
}
}
float duration(int *devA, int *devB, int *devC, int blocksPerGrid, int threadsPerBlock) {
cudaEvent_t start, stop;
float elapsedTime;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
int hArrayC[N];
add<<<blocksPerGrid, threadsPerBlock>>>(devA, devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
subtract<<<blocksPerGrid, threadsPerBlock>>>(devA, devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
multiply<<<blocksPerGrid, threadsPerBlock>>>(devA, devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
divide<<<blocksPerGrid, threadsPerBlock>>>(devA, devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
modu<<<blocksPerGrid, threadsPerBlock>>>(devA, devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
neg<<<blocksPerGrid, threadsPerBlock>>>(devA,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
neg<<<blocksPerGrid, threadsPerBlock>>>(devB,devC);
cudaMemcpy(hArrayC,devC,N*sizeof(int),cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
return elapsedTime;
}
int main(void) {
int *a, *b;
a = new int[N];
b = new int [N];
float dur = 0;
int *devA, *devB,*devC;
cudaMalloc((void**) &devA, N * sizeof(int));
cudaMalloc((void**) &devB, N * sizeof(int));
cudaMalloc((void**) &devC, N * sizeof(int));
fillArray(a, N);
fillArray(b, N);
cudaMemcpy(devA, a, N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(devB, b, N * sizeof(int), cudaMemcpyHostToDevice);
dur = duration(devA, devB, devC,N, 1);
cout << "Global memory version:\n";
cout << "Process completed in " << dur;
cout << " for a data set of " << N << " integers.";
cudaFree(devA);
cudaFree(devB);
delete [] a;
delete [] b;
return 0;
}
What i want to know the total miliseconds in duration function. But miliseconds always return in different values. Sometimes it is 10 ms sometimes it is 0.78652 sometimes it is 30 miliseconds.Why? What is wrong with my code?
This may be caused by the loading/unloading of the NVIDIA drivers. Think of it as an initialization step for the GPU.
You can either set your GPU to persistence mode:
nvidia-smi -pm 1
Or you could run a dummy kernel before timing your GPU code to trigger the loading of the drivers:
__global__ void dummy()
{
// This kernel does nothing, this is just a "warm-up"
}
// Before your cudaEventRecord etc.
dummy<<<blocksPerGrid, threadsPerBlock>>>();
Or maybe just use cudaThreadSynchronize() before timing your kernels.

Cannot read out Values from Texture Memory

Hi I'm writing a simple Program for practicing to work with texture memory. I Just want to write my data into Texture Memory and write it back into Global Memory. But i cannont read out the Values. Here is the code.
#include <stdio.h>
#include <iostream>
#include "cuda.h"
#include <stdlib.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "HelloWorld.h"
#include "linearInterpolation_kernel4.cu"
using namespace std;
using std::cout;
const int blocksize = 16;
__global__
void hello(char *a, int *b) {
a[threadIdx.x] += b[threadIdx.x];
}
////////////////////////////////////////////////////////////////////////////////
// These are CUDA Helper functions
// This will output the proper CUDA error strings in the event that a CUDA host call returns an error
#define checkCudaErrors(err) __checkCudaErrors (err, __FILE__, __LINE__)
inline void __checkCudaErrors( cudaError err, const char *file, const int line )
{
if( cudaSuccess != err) {
printf("%s(%i) : CUDA Runtime API error %d: %s.\n",file, line, (int)err, cudaGetErrorString( err ) );
}
}
// This will output the proper error string when calling cudaGetLastError
#define getLastCudaError(msg) __getLastCudaError (msg, __FILE__, __LINE__)
inline void __getLastCudaError( const char *errorMessage, const char *file, const int line )
{
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err) {
printf("%s(%i) : getLastCudaError() CUDA error : %s : (%d) %s.\n", file, line, errorMessage, (int)err, cudaGetErrorString( err ) );
}
}
int main()
{
int N = 40;
float *A;
A = (float *) malloc(N*sizeof(float));
float *B;
B = (float *) malloc(N*sizeof(float));
float *result;
result = (float *) malloc(N*sizeof(float));
float angle = 0.8f;
for(int i = 0; i < N; i++){
A[i] = i; //(float)rand();
B[i] = i+1; //(float)rand();
}
ipLinearTexture2(A,B,result,angle,N);
float result2;
result2 = (angle)*A[4] + (1-angle)*B[4];
printf(" A %f B %f Result %f\n", A[4], B[4], result[4]);
cout << result2 << endl;
return 1;
}
void ipLinearTexture2(float *A, float* B, float* result, float angle, int N)
{
float cuTime;
int N2 = N * 2;
float *dev_result;
float **AB;
AB = (float **) malloc( N * sizeof(float *));
if(AB)
{
for(int i = 0; i < N; i++)
{
AB[i] = (float *) malloc( 2 * sizeof(float *));
}
}
for (int i = 0; i < N; i = i++)
{
AB[i][0] = A[i];
AB[i][1] = B[i];
}
cudaMalloc(&dev_result, N * sizeof(float));
unsigned int size = N2 * sizeof(float);
//cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc(32, 0, 0, 0, cudaChannelFormatKindFloat);
cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<float>();
cudaArray* cu_array;
checkCudaErrors(cudaMallocArray( &cu_array, &channelDesc,N,2));
cudaMemcpy2DToArray(cu_array,0,0,AB,N * sizeof(float), N * sizeof(float), 2, cudaMemcpyHostToDevice);
// set texture parameters
tex2.normalized = true;
tex2.filterMode = cudaFilterModeLinear;
tex2.addressMode[0] = cudaAddressModeWrap; //cudaAddressModeWrap;
tex2.addressMode[1] = cudaAddressModeWrap; //cudaAddressModeClamp;
checkCudaErrors(cudaBindTextureToArray( tex2, cu_array, channelDesc));
dim3 dimBlock(10, 1, 1);
dim3 dimGrid((int)ceil((double)N*2/dimBlock.x), 1, 1);
transformKernel4<<< 256, 256, 0 >>>( dev_result, N, 2, angle);
checkCudaErrors(cudaMemcpy(result, dev_result, N * sizeof(float), cudaMemcpyDeviceToHost));
cout << "==================================================" << endl;
for (int i = 0 ; i < N ;i++)
{
cout << result[i] << " on " << i << endl;
}
cout << "==================================================" << endl;
checkCudaErrors(cudaUnbindTexture(tex));
checkCudaErrors(cudaFree(dev_result));
checkCudaErrors(cudaFreeArray(cu_array));
}
and here is the kernel code
#ifndef _SIMPLETEXTURE_KERNEL5_H_
#define _SIMPLETEXTURE_KERNEL5_H_
// Texture references
texture<float, 2, cudaReadModeElementType> tex2;
__global__ void
transformKernel4(float* g_odata, int width, int height, float theta)
{
unsigned int xid = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int yid = blockIdx.y * blockDim.y + threadIdx.y;
if (xid >= width || yid >= height) return;
float dx = 1.0f / (float)width;
float dy = 1.0f / (float)height;
float x = ((float)xid + 0.5f) * dx;
float y = ((float)yid + 0.5f) * dy;
float value = tex2D(tex2, x , y);
printf("wert %f xid %i yid %i \n",value, xid, yid);
g_odata[yid * width + xid] = value;
}
#endif // #ifndef _SIMPLETEXTURE_KERNEL_H_
Can somebody tell what i am doing wrong?
I have edited it to remove the first 2 logical mistake. Put why am I need able to print out my data?
It was the wrong binding of the Arrays. You can not use multidimensional Arrays in C that can be copied. You have to use a onedimensional array that respresents a multidimensional.
I can see 2 logical errors here.
The first one is the one pointed out by #asm.
The output should be stored by calculating linear index from 2D x and y indices.
outputIndex = yid * width + xid;
The second one is that the memory allocation for the cudaArray structure is internally aligned.
You should consider using cudaMemcpy2DToArray function to avoid erroneous data copying.
cudaMemcpy2DToArray(cu_array,0,0,AB,N * sizeof(float), N * sizeof(float), 2, cudaMemcpyHostToDevice);