Given a 64-bit variable in a register in a kernel, let's say std::uint64_t var, I want to do some calculations and set each bit of this variable using 64 different threads separately but in parallel. Is it possible to write on a shared variable in parallel?
__shared__ std::uint64_t var = 0
in each thread (tid = 0 to 63):
do some calculations
if we should set the bit with index = tid then:
var |= ((std::uint64_t) 1 << tid)
I also realized that using atomicOr does not benefit us as it only works on integers.
You can do this using a 64-bit shared atomicOr():
$ cat t2082.cu
#include <cstdio>
#include <cstdint>
__global__ void k(){
__shared__ unsigned long long var;
if (!threadIdx.x) var = 0;
__syncthreads();
atomicOr(&var, 1<<threadIdx.x);
__syncthreads();
if (!threadIdx.x) printf("0x%lx\n", var);
}
int main(){
k<<<1,64>>>();
cudaDeviceSynchronize();
}
$ nvcc -o t2082 t2082.cu
$ compute-sanitizer ./t2082
========= COMPUTE-SANITIZER
0xffffffffffffffff
========= ERROR SUMMARY: 0 errors
$
The 64-bit shared atomicOr is supported on devices of cc3.5 or greater, which is the same device footprint supported by CUDA 11.
If you were, for example, on CUDA 10.x and using a cc3.0 device, you could do this with two 32-bit variables:
$ cat t2082.cu
#include <cstdio>
#include <cstdint>
__global__ void k(){
__shared__ unsigned var[2];
if (!threadIdx.x) {var[0] = 0; var[1] = 0;}
__syncthreads();
if (threadIdx.x < 32)
atomicOr(&(var[0]), 1<<threadIdx.x);
else
atomicOr(&(var[1]), 1<<(threadIdx.x-32));
__syncthreads();
unsigned long long var64 = (((unsigned long long)var[1])<<32) + var[0];
if (!threadIdx.x) printf("0x%lx\n", var64);
}
int main(){
k<<<1,64>>>();
cudaDeviceSynchronize();
}
$ nvcc -o t2082 t2082.cu
$ compute-sanitizer ./t2082
========= COMPUTE-SANITIZER
0xffffffffffffffff
========= ERROR SUMMARY: 0 errors
$
Related
It is strange that when I do not add cuda-memcheck before ./main, the program runs without any warning or error message, however, when I add it, it will have error message like following.
========= Invalid __global__ write of size 8
========= at 0x00000120 in initCurand(curandStateXORWOW*, unsigned long)
========= by thread (9,0,0) in block (3,0,0)
========= Address 0x5005413b0 is out of bounds
========= Saved host backtrace up to driver entry point at kernel launch time
========= Host Frame:/usr/lib/x86_64-linux-gnu/libcuda.so.1 (cuLaunchKernel + 0x2c5) [0x204115]
========= Host Frame:./main [0x18e11]
========= Host Frame:./main [0x369b3]
========= Host Frame:./main [0x3403]
========= Host Frame:./main [0x308c]
========= Host Frame:./main [0x30b7]
========= Host Frame:./main [0x2ebb]
========= Host Frame:/lib/x86_64-linux-gnu/libc.so.6 (__libc_start_main + 0xf0) [0x20830]
Here is my functions, a brief introduction on the code, I try to generate a random numbers and save them to a device variable weights, then use this vector to sample from discrete numbers.
#include<iostream>
#include<curand.h>
#include<curand_kernel.h>
#include<time.h>
using namespace std;
#define num 100
__device__ float weights[num];
// function to define seed
__global__ void initCurand(curandState *state, unsigned long seed){
int idx = threadIdx.x + blockIdx.x * blockDim.x;
curand_init(seed, idx, 0, &state[idx]);
}
__device__ void sampling(float *weight, float max_weight, int *index, curandState *state){
int j;
float u;
do{
j = (int)(curand_uniform(state) * (num + 0.999999));
u = curand_uniform(state); //sample from uniform distribution;
}while( u > weight[j]/max_weight);
*index = j;
}
__global__ void test(int *dev_sample, curandState *state){
int idx = threadIdx.x + blockIdx.x * blockDim.x;\
// generate random numbers from uniform distribution and save them to weights
weights[idx] = curand_uniform(&state[idx]);
// run sampling function, in which, weights is an input for the function on each thread
sampling(weights, 1, dev_sample+idx, &state[idx]);
}
int main(){
// define the seed of random generator
curandState *devState;
cudaMalloc((void**)&devState, num*sizeof(curandState));
int *h_sample;
h_sample = (int*) malloc(num*sizeof(int));
int *d_sample;
cudaMalloc((void**)&d_sample, num*sizeof(float));
initCurand<<<(int)num/32 + 1, 32>>>(devState, 1);
test<<<(int)num/32 + 1, 32>>>(d_sample, devState);
cudaMemcpy(h_sample, d_sample, num*sizeof(float), cudaMemcpyDeviceToHost);
for (int i = 0; i < num; ++i)
{
cout << *(h_sample + i) << endl;
}
//free memory
cudaFree(devState);
free(h_sample);
cudaFree(d_sample);
return 0;
}
Just start to learn cuda, if the methods to access the global memory is incorrect, please help me with that. Thanks
This is launching "extra" threads:
initCurand<<<(int)num/32 + 1, 32>>>(devState, 1);
num is 100, so the above config will launch 4 blocks of 32 threads each, i.e. 128 threads. But you are only allocating space for 100 curandState here:
cudaMalloc((void**)&devState, num*sizeof(curandState));
So your initCurand kernel will have some threads (idx = 100-127) that are attempting to initialize some curandState that you haven't allocated. As a result when you run cuda-memcheck which does fairly rigorous out-of-bounds checking, an error is reported.
One possible solution would be to modify your initCurand kernel as follows:
__global__ void initCurand(curandState *state, unsigned long seed, int num){
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < num)
curand_init(seed, idx, 0, &state[idx]);
}
This will prevent any out-of-bounds threads from doing anything. Note that you will need to modify the kernel call to pass num to it. Also, it appears to me you have a similar problem in your test kernel. You may want to do something similar to fix it there. This is a common construct in CUDA kernels, I call it a "thread check". You can find other questions here on the SO tag discussing this same concept.
I have a problem! I need to initialize a constant global array in cuda c. To initialize the array i need to use a for! I need to do this because I have to use this array in some kernels and my professor told me to define as a constant visible only in the device.
How can I do this??
I want to do something like this:
#include <stdio.h>
#include <math.h>
#define N 8
__constant__ double H[N*N];
__global__ void prodotto(double *v, double *w){
int k=threadIdx.x+blockDim.x*blockIdx.x;
w[k]=0;
for(int i=0;i<N;i++) w[k]=w[k]+H[k*N+i]*v[i];
}
int main(){
double v[8]={1, 1, 1, 1, 1, 1, 1, 1};
double *dev_v, *dev_w, *w;
double *host_H;
host_H=(double*)malloc((N*N)*sizeof(double));
cudaMalloc((void**)&dev_v,sizeof(double));
cudaMalloc((void**)&dev_w,sizeof(double));
for(int k=0;k<N;k++){
host_H[2*N*k+2*k]=1/1.414;
host_H[2*N*k+2*k+1]=1/1.414;
host_H[(2*k+1)*N+2*k]=1/1.414;
host_H[(2*k+1)+2*k+1]=-1/1.414;
}
cudaMemcpyToSymbol(H, host_H, (N*N)*sizeof(double));
cudaMemcpy(dev_v, v, N*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(dev_w, w, N*sizeof(double), cudaMemcpyHostToDevice);
prodotto<<<1,N>>>(dev_v, dev_w);
cudaMemcpy(v, dev_v, N*sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(w, dev_w, N*sizeof(double), cudaMemcpyDeviceToHost);
for(int i=0;i<N;i++) printf("\n%f %f", v[i], w[i]);
return 0;
}
But the output is an array of zeros...I want the output array to be filled with the product of the matrix H(here seen as an array) and the array v.
Thanks !!!!!
Something like this should work:
#define DSIZE 32
__constant__ int mydata[DSIZE];
int main(){
...
int *h_mydata;
h_mydata = new int[DSIZE];
for (int i = 0; i < DSIZE; i++)
h_mydata[i] = ....; // initialize however you wish
cudaMemcpyToSymbol(mydata, h_mydata, DSIZE*sizeof(int));
...
}
Not difficult. You can then use the __constant__ data directly in a kernel:
__global__ void mykernel(...){
...
int myval = mydata[threadIdx.x];
...
}
You can read about __constant__ variables in the programming guide. __constant__ variables are read-only from the perspective of device code (kernel code). But from the host, they can be read from or written to using the cudaMemcpyToSymbol/cudaMemcpyFromSymbol API.
EDIT: Based on the code you've now posted, there were at least 2 errors:
Your allocation sizes for dev_v and dev_w were not correct.
You had no host allocation for w.
The following code seems to work correctly for me with those 2 fixes:
$ cat t579.cu
#include <stdio.h>
#include <math.h>
#define N 8
__constant__ double H[N*N];
__global__ void prodotto(double *v, double *w){
int k=threadIdx.x+blockDim.x*blockIdx.x;
w[k]=0;
for(int i=0;i<N;i++) w[k]=w[k]+H[k*N+i]*v[i];
}
int main(){
double v[N]={1, 1, 1, 1, 1, 1, 1, 1};
double *dev_v, *dev_w, *w;
double *host_H;
host_H=(double*)malloc((N*N)*sizeof(double));
w =(double*)malloc( (N)*sizeof(double));
cudaMalloc((void**)&dev_v,N*sizeof(double));
cudaMalloc((void**)&dev_w,N*sizeof(double));
for(int k=0;k<N;k++){
host_H[2*N*k+2*k]=1/1.414;
host_H[2*N*k+2*k+1]=1/1.414;
host_H[(2*k+1)*N+2*k]=1/1.414;
host_H[(2*k+1)+2*k+1]=-1/1.414;
}
cudaMemcpyToSymbol(H, host_H, (N*N)*sizeof(double));
cudaMemcpy(dev_v, v, N*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(dev_w, w, N*sizeof(double), cudaMemcpyHostToDevice);
prodotto<<<1,N>>>(dev_v, dev_w);
cudaMemcpy(v, dev_v, N*sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(w, dev_w, N*sizeof(double), cudaMemcpyDeviceToHost);
for(int i=0;i<N;i++) printf("\n%f %f", v[i], w[i]);
printf("\n");
return 0;
}
$ nvcc -arch=sm_20 -o t579 t579.cu
$ cuda-memcheck ./t579
========= CUDA-MEMCHECK
1.000000 0.000000
1.000000 -0.707214
1.000000 -0.707214
1.000000 -1.414427
1.000000 1.414427
1.000000 0.707214
1.000000 1.414427
1.000000 0.707214
========= ERROR SUMMARY: 0 errors
$
A few notes:
Any time you're having trouble with a CUDA code, it's good practice to use proper cuda error checking.
You can run your code with cuda-memcheck (just as I have above) to get a quick read of whether any CUDA errors are encountered.
I've not verified the numerical results or worked through the math. If it's not what you wanted, I assume you can sort it out.
I've not made any changes to your code other than what seemed sensible to me to fix the obvious errors and make the results presentable for educational purposes. Certainly there can be discussions about preferred allocation methods, printf vs. cout, and what have you. I'm focused primarily on CUDA topics in this answer.
I am trying to apply a kernel function on a __device__ variable, which, according to the specs, resides "in global memory"
#include <stdio.h>
#include "sys_data.h"
#include "my_helper.cuh"
#include "helper_cuda.h"
#include <cuda_runtime.h>
double X[10] = {1,-2,3,-4,5,-6,7,-8,9,-10};
double Y[10] = {0};
__device__ double DEV_X[10];
int main(void) {
checkCudaErrors(cudaMemcpyToSymbol(DEV_X, X,10*sizeof(double)));
vector_projection<double><<<1,10>>>(DEV_X, 10);
getLastCudaError("oops");
checkCudaErrors(cudaMemcpyFromSymbol(Y, DEV_X, 10*sizeof(double)));
return 0;
}
The kernel function vector_projection is defined in my_helper.cuh as follows:
template<typename T> __global__ void vector_projection(T *dx, int n) {
int tid;
tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < n) {
if (dx[tid] < 0)
dx[tid] = (T) 0;
}
}
As you can see, I use cudaMemcpyToSymbol and cudaMemcpyFromSymbol to transfer data to and from the device. However, I'm getting the following error:
CUDA error at ../src/vectorAdd.cu:19 code=4(cudaErrorLaunchFailure)
"cudaMemcpyFromSymbol(Y, DEV_X, 10*sizeof(double))"
Footnote: I can of course avoid to use __device__ variables and go for something like this which works fine; I just want to see how to do the same thing (if possible) with __device__ variables.
Update: The output of cuda-memcheck can be found at http://pastebin.com/AW9vmjFs. The error messages I get are as follows:
========= Invalid __global__ read of size 8
========= at 0x000000c8 in /home/ubuntu/Test0001/Debug/../src/my_helper.cuh:75:void vector_projection<double>(double*, int)
========= by thread (9,0,0) in block (0,0,0)
========= Address 0x000370e8 is out of bounds
The root of the problem is that you are not allowed to take the address of a device variable in ordinary host code:
vector_projection<double><<<1,10>>>(DEV_X, 10);
^
Although this seems to compile correctly, the actual address passed is garbage.
To take the address of a device variable in host code, we can use cudaGetSymbolAddress
Here is a worked example that compiles and runs correctly for me:
$ cat t577.cu
#include <stdio.h>
double X[10] = {1,-2,3,-4,5,-6,7,-8,9,-10};
double Y[10] = {0};
__device__ double DEV_X[10];
template<typename T> __global__ void vector_projection(T *dx, int n) {
int tid;
tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < n) {
if (dx[tid] < 0)
dx[tid] = (T) 0;
}
}
int main(void) {
cudaMemcpyToSymbol(DEV_X, X,10*sizeof(double));
double *my_dx;
cudaGetSymbolAddress((void **)&my_dx, DEV_X);
vector_projection<double><<<1,10>>>(my_dx, 10);
cudaMemcpyFromSymbol(Y, DEV_X, 10*sizeof(double));
for (int i = 0; i < 10; i++)
printf("%d: %f\n", i, Y[i]);
return 0;
}
$ nvcc -arch=sm_35 -o t577 t577.cu
$ cuda-memcheck ./t577
========= CUDA-MEMCHECK
0: 1.000000
1: 0.000000
2: 3.000000
3: 0.000000
4: 5.000000
5: 0.000000
6: 7.000000
7: 0.000000
8: 9.000000
9: 0.000000
========= ERROR SUMMARY: 0 errors
$
This is not the only way to address this. It is legal to take the address of a device variable in device code, so you could modify your kernel with a line something like this:
T *dx = DEV_X;
and forgo passing of the device variable as a kernel parameter. As suggested in the comments, you could also modify your code to use Unified Memory.
Regarding error checking, if you deviate from proper cuda error checking and are not careful in your deviations, the results may be confusing. Most cuda API calls can, in addition to errors arising from their own behavior, return an error that resulted from some previous CUDA asynchronous activity (usually kernel calls).
I have a Cuda C++ code that uses Thrust currently working properly on a single GPU. I'd now like to modify it for multi-gpu. I have a host function that includes a number of Thrust calls that sort, copy, calculate differences etc on device arrays. I want to use each GPU to run this sequence of Thrust calls on it's own (independent) set of arrays at the same time. I've read that Thrust functions that return values are synchronous but can I use OpenMP to have each host thread call up a function (with Thrust calls) that runs on a separate GPU?
For example (coded in browser):
#pragma omp parallel for
for (int dev=0; dev<Ndev; dev++){
cudaSetDevice(dev);
runthrustfunctions(dev);
}
void runthrustfunctions(int dev){
/*lots of Thrust functions running on device arrays stored on corresponding GPU*/
//for example this is just a few of the lines"
thrust::device_ptr<double> pos_ptr = thrust::device_pointer_cast(particle[dev].pos);
thrust::device_ptr<int> list_ptr = thrust::device_pointer_cast(particle[dev].list);
thrust::sequence(list_ptr,list_ptr+length);
thrust::sort_by_key(pos_ptr, pos_ptr+length,list_ptr);
thrust::device_vector<double> temp(length);
thrust::gather(list_ptr,list_ptr+length,pos_ptr,temp.begin());
thrust::copy(temp.begin(), temp.end(), pos_ptr);
}`
I think I also need the structure "particle[0]" to be stored on GPU 0, particle[1] on GPU 1 etc and I my guess is this not possible. An option might be to use "switch" with separate code for each GPU case.
I'd like to know if this is a correct approach or if there is a better way?
Thanks
Yes, you can combine thrust and OpenMP.
Here's a complete worked example with results:
$ cat t340.cu
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <time.h>
#include <sys/time.h>
#define DSIZE 200000000
using namespace std;
int main(int argc, char *argv[])
{
timeval t1, t2;
int num_gpus = 0; // number of CUDA GPUs
printf("%s Starting...\n\n", argv[0]);
// determine the number of CUDA capable GPUs
cudaGetDeviceCount(&num_gpus);
if (num_gpus < 1)
{
printf("no CUDA capable devices were detected\n");
return 1;
}
// display CPU and GPU configuration
printf("number of host CPUs:\t%d\n", omp_get_num_procs());
printf("number of CUDA devices:\t%d\n", num_gpus);
for (int i = 0; i < num_gpus; i++)
{
cudaDeviceProp dprop;
cudaGetDeviceProperties(&dprop, i);
printf(" %d: %s\n", i, dprop.name);
}
printf("initialize data\n");
// initialize data
typedef thrust::device_vector<int> dvec;
typedef dvec *p_dvec;
std::vector<p_dvec> dvecs;
for(unsigned int i = 0; i < num_gpus; i++) {
cudaSetDevice(i);
p_dvec temp = new dvec(DSIZE);
dvecs.push_back(temp);
}
thrust::host_vector<int> data(DSIZE);
thrust::generate(data.begin(), data.end(), rand);
// copy data
for (unsigned int i = 0; i < num_gpus; i++) {
cudaSetDevice(i);
thrust::copy(data.begin(), data.end(), (*(dvecs[i])).begin());
}
printf("start sort\n");
gettimeofday(&t1,NULL);
// run as many CPU threads as there are CUDA devices
omp_set_num_threads(num_gpus); // create as many CPU threads as there are CUDA devices
#pragma omp parallel
{
unsigned int cpu_thread_id = omp_get_thread_num();
cudaSetDevice(cpu_thread_id);
thrust::sort((*(dvecs[cpu_thread_id])).begin(), (*(dvecs[cpu_thread_id])).end());
cudaDeviceSynchronize();
}
gettimeofday(&t2,NULL);
printf("finished\n");
unsigned long et = ((t2.tv_sec * 1000000)+t2.tv_usec) - ((t1.tv_sec * 1000000) + t1.tv_usec);
if (cudaSuccess != cudaGetLastError())
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
printf("sort time = %fs\n", (float)et/(float)(1000000));
// check results
thrust::host_vector<int> result(DSIZE);
thrust::sort(data.begin(), data.end());
for (int i = 0; i < num_gpus; i++)
{
cudaSetDevice(i);
thrust::copy((*(dvecs[i])).begin(), (*(dvecs[i])).end(), result.begin());
for (int j = 0; j < DSIZE; j++)
if (data[j] != result[j]) { printf("mismatch on device %d at index %d, host: %d, device: %d\n", i, j, data[j], result[j]); return 1;}
}
printf("Success\n");
return 0;
}
$ nvcc -Xcompiler -fopenmp -O3 -arch=sm_20 -o t340 t340.cu -lgomp
$ CUDA_VISIBLE_DEVICES="0" ./t340
./t340 Starting...
number of host CPUs: 12
number of CUDA devices: 1
0: Tesla M2050
initialize data
start sort
finished
sort time = 0.398922s
Success
$ ./t340
./t340 Starting...
number of host CPUs: 12
number of CUDA devices: 4
0: Tesla M2050
1: Tesla M2070
2: Tesla M2050
3: Tesla M2070
initialize data
start sort
finished
sort time = 0.460058s
Success
$
We can see that when I restrict the program to using a single device, the sort operation takes about 0.4 seconds. Then when I allow it to use all 4 devices (repeating the same sort on all 4 devices) the overall operation only take 0.46 seconds, even though we're doing 4 times as much work.
For this particular case I happened to be using CUDA 5.0 with thrust v1.7, and gcc 4.4.6 (RHEL 6.2)
I have added some printf() statements in my CUDA program
__device__ __global__ void Kernel(float *, float * ,int );
void DeviceFunc(float *temp_h , int numvar , float *temp1_h)
{ .....
//Kernel call
printf("calling kernel\n");
Kernel<<<dimGrid , dimBlock>>>(a_d , b_d , numvar);
printf("kernel called\n");
....
}
int main(int argc , char **argv)
{ ....
printf("beforeDeviceFunc\n\n");
DeviceFunc(a_h , numvar , b_h); //Showing the data
printf("after DeviceFunc\n\n");
....
}
Also in the Kernel.cu, I wrote:
#include<cuda.h>
#include <stdio.h>
__device__ __global__ void Kernel(float *a_d , float *b_d ,int size)
{
int idx = threadIdx.x ;
int idy = threadIdx.y ;
//Allocating memory in the share memory of the device
__shared__ float temp[16][16];
//Copying the data to the shared memory
temp[idy][idx] = a_d[(idy * (size+1)) + idx] ;
printf("idx=%d, idy=%d, size=%d", idx, idy, size);
....
}
Then I compile using -arch=sm_20 like this:
nvcc -c -arch sm_20 main.cu
nvcc -c -arch sm_20 Kernel.cu
nvcc -arch sm_20 main.o Kernel.o -o main
Now when I run the program, I see:
beforeDeviceFunc
calling kernel
kernel called
after DeviceFunc
So the printf() inside the kernel is not printed. How can I fix that?
printf() output is only displayed if the kernel finishes successfully, so check the return codes of all CUDA function calls and make sure no errors are reported.
Furthermore printf() output is only displayed at certain points in the program. Appendix B.32.2 of the Programming Guide lists these as
Kernel launch via <<<>>> or cuLaunchKernel() (at the start of the launch, and if the CUDA_LAUNCH_BLOCKING environment variable is set to 1, at the end of the launch as well),
Synchronization via cudaDeviceSynchronize(), cuCtxSynchronize(), cudaStreamSynchronize(), cuStreamSynchronize(), cudaEventSynchronize(), or cuEventSynchronize(),
Memory copies via any blocking version of cudaMemcpy*() or cuMemcpy*(),
Module loading/unloading via cuModuleLoad() or cuModuleUnload(),
Context destruction via cudaDeviceReset() or cuCtxDestroy().
Prior to executing a stream callback added by cudaStreamAddCallback() or cuStreamAddCallback().
To check this is your problem, put the following code after your kernel invocation:
{
cudaError_t cudaerr = cudaDeviceSynchronize();
if (cudaerr != cudaSuccess)
printf("kernel launch failed with error \"%s\".\n",
cudaGetErrorString(cudaerr));
}
You should then see either the output of your kernel or an error message.
More conveniently, cuda-memcheck will automatically check all return codes for you if you run your executable under it. While you should always check for errors anyway, this comes handy when resolving concrete issues.
I had the same error just now and decreasing the block size to 512 helped. According to documentation maximum block size can be either 512 or 1024.
I have written a simple test that showed that my GTX 1070 has a maximum block size of 1024. UPD: you can check if your kernel has ever executed by using cudaError_t cudaPeekAtLastError() that returns cudaSuccess if the kernel has started successfully, and only after it is worse calling cudaError_t cudaDeviceSynchronize().
Testing block size of 1023
Testing block size of 1024
Testing block size of 1025
CUDA error: invalid configuration argument
Block maximum size is 1024
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
__global__
void set1(int* t)
{
t[threadIdx.x] = 1;
}
inline bool failed(cudaError_t error)
{
if (cudaSuccess == error)
return false;
fprintf(stderr, "CUDA error: %s\n", cudaGetErrorString(error));
return true;
}
int main()
{
int blockSize;
for (blockSize = 1; blockSize < 1 << 12; blockSize++)
{
printf("Testing block size of %d\n", blockSize);
int* t;
if(failed(cudaMallocManaged(&t, blockSize * sizeof(int))))
{
failed(cudaFree(t));
break;
}
for (int i = 0; i < blockSize; i++)
t[0] = 0;
set1 <<<1, blockSize>>> (t);
if (failed(cudaPeekAtLastError()))
{
failed(cudaFree(t));
break;
}
if (failed(cudaDeviceSynchronize()))
{
failed(cudaFree(t));
break;
}
bool hasError = false;
for (int i = 0; i < blockSize; i++)
if (1 != t[i])
{
printf("CUDA error: t[%d] = %d but not 1\n", i, t[i]);
hasError = true;
break;
}
if (hasError)
{
failed(cudaFree(t));
break;
}
failed(cudaFree(t));
}
blockSize--;
if(blockSize <= 0)
{
printf("CUDA error: block size cannot be 0\n");
return 1;
}
printf("Block maximum size is %d", blockSize);
return 0;
}
P.S. Please note, that the only thing in block sizing is warp granularity which is 32 nowadays, so if 0 == yourBlockSize % 32 the warps are used pretty efficiently. The only reason to make blocks bigger then 32 is when the code needs synchronization as synchronization is available only among threads in a single block which makes a developer to use a single large block instead of many small ones. So running with higher number of smaller blocks can be even more efficient than running with lower number of larger blocks.