cudaMallocManaged and cudaDeviceSynchronize() - cuda

Question

I have the following two mostly identical example codes. code1.cu use cudaMalloc and cudaMemcpy to handling device/host variable value exchange.
The code2.cu use cudaMallocManaged and thus cudaMemcpy is not needed. When cudaMallocManaged is used, I have to include cudaDeviceSynchronize() to get the correct results, while for the one with cudaMalloc, this is not needed. I would appreciate some hint on why this is happening
code2.cu
#include <iostream>
#include <math.h>
#include <vector>
//
using namespace std;
// Kernel function to do nested loops
__global__
void add(int max_x, int max_y, float *tot, float *x, float *y)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
if(i < max_x && j<max_y) {
atomicAdd(tot, x[i] + y[j]);
}
}
int main(void)
{
int Nx = 1<<15;
int Ny = 1<<15;
float *d_x = NULL, *d_y = NULL;
float *d_tot = NULL;
cudaMalloc((void **)&d_x, sizeof(float)*Nx);
cudaMalloc((void **)&d_y, sizeof(float)*Ny);
cudaMallocManaged((void **)&d_tot, sizeof(float));
// Allocate Unified Memory – accessible from CPU or GPU
vector<float> vx;
vector<float> vy;
// initialize x and y arrays on the host
for (int i = 0; i < Nx; i++)
vx.push_back(i);
for (int i = 0; i < Ny; i++)
vy.push_back(i*10);
//
float tot = 0;
for(int i = 0; i<vx.size(); i++)
for(int j = 0; j<vy.size(); j++)
tot += vx[i] + vy[j];
cout<<"CPU: tot: "<<tot<<endl;
//
cudaMemcpy(d_x, vx.data(), vx.size()*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, vy.data(), vy.size()*sizeof(float), cudaMemcpyHostToDevice);
//
int blockSize; // The launch configurator returned block size
int minGridSize; // The minimum grid size needed to achieve the
cudaOccupancyMaxPotentialBlockSize( &minGridSize, &blockSize, add, 0, Nx+Ny);
//.. bx*by can not go beyond the blockSize, or hardware limit, which is 1024;
//.. bx*bx = blockSize && bx/by=Nx/Ny, solve the equation
int bx = sqrt(blockSize*Nx/(float)Ny);
int by = bx*Ny/(float)Nx;
dim3 blockSize_3D(bx, by);
dim3 gridSize_3D((Nx+bx-1)/bx, (Ny+by+1)/by);
cout<<"blockSize: "<<blockSize<<endl;
cout<<"bx: "<<bx<<" by: "<<by<<" gx: "<<gridSize_3D.x<<" gy: "<<gridSize_3D.y<<endl;
// calculate theoretical occupancy
int maxActiveBlocks;
cudaOccupancyMaxActiveBlocksPerMultiprocessor( &maxActiveBlocks, add, blockSize, 0);
int device;
cudaDeviceProp props;
cudaGetDevice(&device);
cudaGetDeviceProperties(&props, device);
float occupancy = (maxActiveBlocks * blockSize / props.warpSize) /
(float)(props.maxThreadsPerMultiProcessor /
props.warpSize);
printf("Launched blocks of size %d. Theoretical occupancy: %f\n",
blockSize, occupancy);
// Run kernel on 1M elements on the GPU
tot = 0;
add<<<gridSize_3D, blockSize_3D>>>(Nx, Ny, d_tot, d_x, d_y);
// Wait for GPU to finish before accessing on host
//cudaDeviceSynchronize();
tot =*d_tot;
//
//
cout<<" GPU: tot: "<<tot<<endl;
// Free memory
cudaFree(d_x);
cudaFree(d_y);
cudaFree(d_tot);
return 0;
}
code1.cu
#include <iostream>
#include <math.h>
#include <vector>
//
using namespace std;
// Kernel function to do nested loops
__global__
void add(int max_x, int max_y, float *tot, float *x, float *y)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
if(i < max_x && j<max_y) {
atomicAdd(tot, x[i] + y[j]);
}
}
int main(void)
{
int Nx = 1<<15;
int Ny = 1<<15;
float *d_x = NULL, *d_y = NULL;
float *d_tot = NULL;
cudaMalloc((void **)&d_x, sizeof(float)*Nx);
cudaMalloc((void **)&d_y, sizeof(float)*Ny);
cudaMalloc((void **)&d_tot, sizeof(float));
// Allocate Unified Memory – accessible from CPU or GPU
vector<float> vx;
vector<float> vy;
// initialize x and y arrays on the host
for (int i = 0; i < Nx; i++)
vx.push_back(i);
for (int i = 0; i < Ny; i++)
vy.push_back(i*10);
//
float tot = 0;
for(int i = 0; i<vx.size(); i++)
for(int j = 0; j<vy.size(); j++)
tot += vx[i] + vy[j];
cout<<"CPU: tot: "<<tot<<endl;
//
cudaMemcpy(d_x, vx.data(), vx.size()*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, vy.data(), vy.size()*sizeof(float), cudaMemcpyHostToDevice);
//
int blockSize; // The launch configurator returned block size
int minGridSize; // The minimum grid size needed to achieve the
cudaOccupancyMaxPotentialBlockSize( &minGridSize, &blockSize, add, 0, Nx+Ny);
//.. bx*by can not go beyond the blockSize, or hardware limit, which is 1024;
//.. bx*bx = blockSize && bx/by=Nx/Ny, solve the equation
int bx = sqrt(blockSize*Nx/(float)Ny);
int by = bx*Ny/(float)Nx;
dim3 blockSize_3D(bx, by);
dim3 gridSize_3D((Nx+bx-1)/bx, (Ny+by+1)/by);
cout<<"blockSize: "<<blockSize<<endl;
cout<<"bx: "<<bx<<" by: "<<by<<" gx: "<<gridSize_3D.x<<" gy: "<<gridSize_3D.y<<endl;
// calculate theoretical occupancy
int maxActiveBlocks;
cudaOccupancyMaxActiveBlocksPerMultiprocessor( &maxActiveBlocks, add, blockSize, 0);
int device;
cudaDeviceProp props;
cudaGetDevice(&device);
cudaGetDeviceProperties(&props, device);
float occupancy = (maxActiveBlocks * blockSize / props.warpSize) /
(float)(props.maxThreadsPerMultiProcessor /
props.warpSize);
printf("Launched blocks of size %d. Theoretical occupancy: %f\n",
blockSize, occupancy);
// Run kernel on 1M elements on the GPU
tot = 0;
add<<<gridSize_3D, blockSize_3D>>>(Nx, Ny, d_tot, d_x, d_y);
// Wait for GPU to finish before accessing on host
//cudaDeviceSynchronize();
//
cudaMemcpy(&tot, d_tot, sizeof(float), cudaMemcpyDeviceToHost);
//
cout<<" GPU: tot: "<<tot<<endl;
// Free memory
cudaFree(d_x);
cudaFree(d_y);
cudaFree(d_tot);
return 0;
}
//Code2.cu has the following output:
//
//CPU: tot: 8.79609e+12
//blockSize: 1024
//bx: 32 by: 32 gx: 1024 gy: 1025
//Launched blocks of size 1024. Theoretical occupancy: 1.000000
//GPU: tot: 0
After remove the comment on cudaDeviceSynchronize(),
GPU: tot: 8.79609e+12

Answer 1

CUDA kernel launches are asynchronous. That means that they execute independently of the CPU thread that launched them.
Because of this asynchronous launch, the CUDA kernel is not guaranteed to be finished (or even started) by the time your CPU thread code begins testing the result.
Therefore it is necessary to wait until the GPU kernel is complete, and cudaDeviceSynchronize() does exactly that. cudaMemcpy also has a synchronizing effect, so when you remove the cudaMemcpy operations, you lose that synchronization, but cudaDeviceSynchronize() restores it.