Concurrency in CUDA multi
I'm running a cuda kernel function on a multiple GPUs system, with 4 GPUs. I've expected them to be launched concurrently, but they are not. I measure the starting time of each kernel, and the second kernel starts after the first one finishes its execution. So launching the kernel on 4 GPUs is not faster than 1 single GPU.
How can I make them work concurrently?
This is my code:
cudaSetDevice(0);
GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_0, parameterA +(0*rateA), parameterB + (0*rateB));
cudaMemcpyAsync(h_result_0, d_result_0, mem_size_result, cudaMemcpyDeviceToHost);
cudaSetDevice(1);
GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_1, parameterA +(1*rateA), parameterB + (1*rateB));
cudaMemcpyAsync(h_result_1, d_result_1, mem_size_result, cudaMemcpyDeviceToHost);
cudaSetDevice(2);
GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_2, parameterA +(2*rateA), parameterB + (2*rateB));
cudaMemcpyAsync(h_result_2, d_result_2, mem_size_result, cudaMemcpyDeviceToHost);
cudaSetDevice(3);
GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_3, parameterA +(3*rateA), parameterB + (3*rateB));
cudaMemcpyAsync(h_result_3, d_result_3, mem_size_result, cudaMemcpyDeviceToHost);
I have done some experiments on achieving concurrent execution on a cluster of 4 Kepler K20c GPUs. I have considered 8 test cases, whose corresponding codes along with the profiler timelines are reported below.
Test case #1 - "Breadth-first" approach - synchronous copy
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
template<class T>
struct plan {
T *d_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
double *inputMatrices = (double *)malloc(N * sizeof(double));
// --- "Breadth-first" approach - no async
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpy(plan[k].d_data, inputMatrices + k * NperGPU, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpy(inputMatrices + k * NperGPU, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline - 
As it can be seen, the use of cudaMemcpy does not enable achieving concurrency in copies, but concurrency is achieved in kernel execution.
Test case #2 - "Depth-first" approach - synchronous copy
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
template<class T>
struct plan {
T *d_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
double *inputMatrices = (double *)malloc(N * sizeof(double));
// --- "Depth-first" approach - no async
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpy(plan[k].d_data, inputMatrices + k * NperGPU, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
gpuErrchk(cudaMemcpy(inputMatrices + k * NperGPU, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
This time, concurrency is not achieved neither within memory copies nor within kernel executions.
Test case #3 - "Depth-first" approach - asynchronous copy with streams
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
template<class T>
struct plan {
T *d_data;
T *h_data;
cudaStream_t stream;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
gpuErrchk(cudaMallocHost((void **)&plan.h_data, NperGPU * sizeof(T)));
gpuErrchk(cudaStreamCreate(&plan.stream));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Depth-first" approach - async
for (int k = 0; k < numGPUs; k++)
{
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, plan[k].h_data, NperGPU * sizeof(double), cudaMemcpyHostToDevice, plan[k].stream));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE, 0, plan[k].stream>>>(plan[k].d_data, NperGPU);
gpuErrchk(cudaMemcpyAsync(plan[k].h_data, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost, plan[k].stream));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Concurrency is achieved, as expected.
Test case #4 - "Depth-first" approach - asynchronous copy within default streams
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
template<class T>
struct plan {
T *d_data;
T *h_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
gpuErrchk(cudaMallocHost((void **)&plan.h_data, NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Depth-first" approach - no stream
for (int k = 0; k < numGPUs; k++)
{
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, plan[k].h_data, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
gpuErrchk(cudaMemcpyAsync(plan[k].h_data, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Despite using the default stream, concurrency is achieved.
Test case #5 - "Depth-first" approach - asynchronous copy within default stream and unique host cudaMallocHost ed vector
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
template<class T>
struct plan {
T *d_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Depth-first" approach - no stream
double *inputMatrices; gpuErrchk(cudaMallocHost(&inputMatrices, N * sizeof(double)));
for (int k = 0; k < numGPUs; k++)
{
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, inputMatrices + k * NperGPU, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
gpuErrchk(cudaMemcpyAsync(inputMatrices + k * NperGPU, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Concurrency is achieved once again.
Test case #6 - "Breadth-first" approach with asynchronous copy with streams
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
// --- Async
template<class T>
struct plan {
T *d_data;
T *h_data;
cudaStream_t stream;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
gpuErrchk(cudaMallocHost((void **)&plan.h_data, NperGPU * sizeof(T)));
gpuErrchk(cudaStreamCreate(&plan.stream));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Breadth-first" approach - async
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, plan[k].h_data, NperGPU * sizeof(double), cudaMemcpyHostToDevice, plan[k].stream));
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE, 0, plan[k].stream>>>(plan[k].d_data, NperGPU);
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].h_data, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost, plan[k].stream));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Concurrency achieved, as in the corresponding "depth-first" approach.
Test case #7 - "Breadth-first" approach - asynchronous copy within default streams
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
// --- Async
template<class T>
struct plan {
T *d_data;
T *h_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
gpuErrchk(cudaMallocHost((void **)&plan.h_data, NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Breadth-first" approach - async
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, plan[k].h_data, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].h_data, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Concurrency is achieved, as in the corresponding "depth-first" approach.
Test case #8 - "Breadth-first" approach - asynchronous copy within the default stream and unique host cudaMallocHost ed vector
- Code -
#include "Utilities.cuh"
#include "InputOutput.cuh"
#define BLOCKSIZE 128
/*******************/
/* KERNEL FUNCTION */
/*******************/
template<class T>
__global__ void kernelFunction(T * __restrict__ d_data, const unsigned int NperGPU) {
const int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < NperGPU) for (int k = 0; k < 1000; k++) d_data[tid] = d_data[tid] * d_data[tid];
}
/******************/
/* PLAN STRUCTURE */
/******************/
// --- Async
template<class T>
struct plan {
T *d_data;
};
/*********************/
/* SVD PLAN CREATION */
/*********************/
template<class T>
void createPlan(plan<T>& plan, unsigned int NperGPU, unsigned int gpuID) {
// --- Device allocation
gpuErrchk(cudaSetDevice(gpuID));
gpuErrchk(cudaMalloc(&(plan.d_data), NperGPU * sizeof(T)));
}
/********/
/* MAIN */
/********/
int main() {
const int numGPUs = 4;
const int NperGPU = 500000;
const int N = NperGPU * numGPUs;
plan<double> plan[numGPUs];
for (int k = 0; k < numGPUs; k++) createPlan(plan[k], NperGPU, k);
// --- "Breadth-first" approach - async
double *inputMatrices; gpuErrchk(cudaMallocHost(&inputMatrices, N * sizeof(double)));
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(plan[k].d_data, inputMatrices + k * NperGPU, NperGPU * sizeof(double), cudaMemcpyHostToDevice));
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
kernelFunction<<<iDivUp(NperGPU, BLOCKSIZE), BLOCKSIZE>>>(plan[k].d_data, NperGPU);
}
for (int k = 0; k < numGPUs; k++) {
gpuErrchk(cudaSetDevice(k));
gpuErrchk(cudaMemcpyAsync(inputMatrices + k * NperGPU, plan[k].d_data, NperGPU * sizeof(double), cudaMemcpyDeviceToHost));
}
gpuErrchk(cudaDeviceReset());
}
- Profiler timeline -
Concurrency is achieved, as in the corresponding "depth-first" approach.
Conclusion Using asynchronous copies guarantees concurrent executions, either using purposely created streams or using the default stream.
Note In all the above examples, I have taken care to provide enough work to do the GPUs, either in terms of copies and of computing tasks. Failing to provide enough work to the cluster may prevent observing concurrent executions.
You might need to use cudaMemcpyAsync . cudaMemcpy is blocking call, so it does not return execution to your code before it finishes, so your code just does not switch GPU before it completes the routine for the current one.
However, kernel calls are asynchronous (for CPU), so the code you posted is likely to cause some race-conditions ( cudaMemcpy might start executing before kernel finishes). As @talonmies pointed out in the comments, since cudaMemcpy / cudaMemcpyAsync goes into the same stream as kernel launch, everything is executed in right order.
I would recommend you to use CUDA Streams; here is a brief introduction to MultiGPU programming using streams. It's not very helpful in your case, but might be very convenient to use in more complex applications, eg if you need to synchronize function calls between different devices.
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