CUDA local array initalization modifies program output - arrays

Question

I have a program which (for now) calculates values of two functions in random points on GPU , sends these values back to host, and then visualizes them. This is what I get, some nice semi-random points:
Now, if I modify my kernel code, and add the local array initalization code at the very end,
__global__ void optymalize(curandState * state, float* testPoints)
{
int ind=blockDim.x*blockIdx.x+threadIdx.x;
int step=blockDim.x*gridDim.x;
for(int i=ind*2;i<NOF*TEST_POINTS;i+=step*2)
{
float* x=generateX(state);
testPoints[i]=ZDT_f1(x);
testPoints[i+1]=ZDT_f2(x);
}
//works fine with 'new'
//float* test_array=new float[2];
float test_array[2]={1.0f,2.0f};
}
I get something like this everytime:
Does anyone know the cause of this behavior? All the drawn points are computed BEFORE test_array is initialized, yet they are affected by it. It doesn't happen when I initialize test_array before the 'for' loop.
Host/device code:
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "curand_kernel.h"
#include "device_functions.h"
#include <random>
#include <iostream>
#include <time.h>
#include <fstream>
using namespace std;
#define XSIZE 5
#define TEST_POINTS 100
#define NOF 2
#define BLOCK_COUNT 64
#define THR_COUNT 128
#define POINTS_PER_THREAD (NOF*TEST_POINTS+THR_COUNT*BLOCK_COUNT-1)/(THR_COUNT*BLOCK_COUNT)
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, char *file, int line, bool abort=false)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
__device__ float g(float* x)
{
float tmp=1;
for(int i=1;i<XSIZE;i++)
tmp*=x[i];
return 1+9*(tmp/(XSIZE-1));
}
__device__ float ZDT_f1(float* x)
{
return x[0];
}
__device__ float ZDT_f2(float* x)
{
float gp=g(x);
return gp*(1-sqrtf(x[0]/gp));
}
__device__ bool oneDominatesTwo(float* x1, float* x2)
{
for(int i=0;i<XSIZE;i++)
if(x1[i]>=x2[i])
return false;
return true;
}
__device__ float* generateX(curandState* globalState)
{
int ind = threadIdx.x;
float x[XSIZE];
for(int i=0;i<XSIZE;i++)
x[i]=curand_uniform(&globalState[ind]);
return x;
}
__global__ void setup_kernel ( curandState * state, unsigned long seed )
{
int id = blockDim.x*blockIdx.x+threadIdx.x;
curand_init ( seed, id, 0, &state[id] );
}
__global__ void optymalize(curandState * state, float* testPoints)
{
int ind=blockDim.x*blockIdx.x+threadIdx.x;
int step=blockDim.x*gridDim.x;
for(int i=ind*2;i<NOF*TEST_POINTS;i+=step*2)
{
float* x=generateX(state);
testPoints[i]=ZDT_f1(x);
testPoints[i+1]=ZDT_f2(x);
}
__syncthreads();
//float* test_array=new float[2];
//test_array[0]=1.0f;
//test_array[1]=1.0f;
float test_array[2]={1.0f,1.0f};
}
void saveResultToFile(float* result)
{
ofstream resultFile;
resultFile.open ("result.txt");
for(unsigned int i=0;i<NOF*TEST_POINTS;i+=NOF)
{
resultFile << result[i] << " "<<result[i+1]<<"\n";
}
resultFile.close();
}
int main()
{
float* dev_fPoints;
float* fPoints=new float[NOF*TEST_POINTS];
gpuErrchk(cudaMalloc((void**)&dev_fPoints, NOF * TEST_POINTS * sizeof(float)));
curandState* devStates;
gpuErrchk(cudaMalloc(&devStates,THR_COUNT*sizeof(curandState)));
cudaEvent_t start;
gpuErrchk(cudaEventCreate(&start));
cudaEvent_t stop;
gpuErrchk(cudaEventCreate(&stop));
gpuErrchk(cudaThreadSetLimit(cudaLimitMallocHeapSize, 128*1024*1024));
gpuErrchk(cudaEventRecord(start, NULL));
setup_kernel<<<BLOCK_COUNT, THR_COUNT>>>(devStates,unsigned(time(NULL)));
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaGetLastError());
optymalize<<<BLOCK_COUNT,THR_COUNT>>>(devStates, dev_fPoints);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaGetLastError());
gpuErrchk(cudaMemcpy(fPoints, dev_fPoints, NOF * TEST_POINTS * sizeof(float), cudaMemcpyDeviceToHost));
gpuErrchk(cudaEventRecord(stop, NULL));
gpuErrchk(cudaEventSynchronize(stop));
float msecTotal = 0.0f;
cudaEventElapsedTime(&msecTotal, start, stop);
cout<<"Kernel execution time: "<<msecTotal<< "ms"<<endl;
saveResultToFile(fPoints);
system("start pythonw plot_data.py result.txt");
cudaFree(dev_fPoints);
cudaFree(devStates);
system("pause");
return 0;
}
Plot script code:
import matplotlib.pyplot as plt;
import sys;
if len(sys.argv)<2:
print("Usage: python PlotScript <filename>");
sys.exit(0);
path=sys.argv[1];
x=[]
y=[]
with open(path,"r") as f:
for line in f:
vals=line.strip().split(" ");
x.append(vals[0]);
y.append(vals[1]);
plt.plot(x,y,'ro')
plt.show();

Answer 1

The basic problem was in code you originally didn't show in your question, specifically this:
__device__ float* generateX(curandState* globalState)
{
int ind = threadIdx.x;
float x[XSIZE];
for(int i=0;i<XSIZE;i++)
x[i]=curand_uniform(&globalState[ind]);
return x;
}
Returning an address or reference to a local scope variable from a function results in undefined behaviour. It is only valid to use x by reference or value within generateX while it is in scope. There should be no surprise that adding or moving other local scope variables around within the kernel changes the kernel behaviour.
Fix this function so it populates an array passed by reference, rather than returning the address of a local scope array. And pay attention to compiler warnings - there will have been one for this which should have immediately set off alarm bells that there was something wrong.