cuda , Why does Hyper-Q selectively overlap async HtoD and DtoH transfer on my cc5.2 hardware?

Why does Hyper-Q selectively overlap async HtoD and DtoH transfer on my cc5.2 hardware?


Tag: cuda

There's an old Parallel ForAll blog post that demonstrates using streams and async memcpys to generate overlap between kernels and memcpys, and between HtoD and DtoH memcpys. So I ran the full Async sample given on my GTX Titan X, and here's the result:

As you can see, when the HtoD, Kernel and DtoH are called back to back in a single loop, there's isn't any overlapping between HtoD and DtoH transfers. However, when they are called separately in three loops, there is overlapping between HtoD and DtoH.

If Hyper-Q did what it claims to do, then there should also be HtoD and DtoH overlap in the first version of loop launching (as is the case of Tesla K20c). It was my understanding that in devices with compute capability 3.5 and above that support Hyper-Q, user shouldn't worry about tailoring launch order anymore.

I also ran the CUDA 7.0 simpleHyperQ sample. With CUDA_DEVICE_MAX_CONNECTIONS set to 32, I can get 32 concurrent kernels running, so Hyper-Q is working in this case.

I am under 64-bit Windows 8.1, driver version 353.06 and CUDA 7.0, compiling using Visual Studio 2013, targeting x64 platform release mode, with code generation property being compute_52,sm_52. CUDA_DEVICE_MAX_CONNECTIONS is set to an ample 32.

Since I can't post more links, the full code of the Async sample (with slight modification) is posted below.

// Copyright 2012 NVIDIA Corporation

// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at


// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// See the License for the specific language governing permissions and
// limitations under the License.

#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <curand_kernel.h>

#include <stdio.h>

// Convenience function for checking CUDA runtime API results
// can be wrapped around any runtime API call. No-op in release builds.
cudaError_t checkCuda(cudaError_t result)
#if defined(DEBUG) || defined(_DEBUG)
    if (result != cudaSuccess) {
        fprintf(stderr, "CUDA Runtime Error: %s\n", cudaGetErrorString(result));
        assert(result == cudaSuccess);
    return result;

__global__ void kernel(float *a, int offset)
    int i = offset + threadIdx.x + blockIdx.x*blockDim.x;
    float x = (float)i;
    float s = sinf(x);
    float c = cosf(x);
    a[i] = a[i] + sqrtf(s*s + c*c);

float maxError(float *a, int n)
    float maxE = 0;
    for (int i = 0; i < n; i++) {
        float error = fabs(a[i] - 1.0f);
        if (error > maxE) maxE = error;
    return maxE;

int main(int argc, char **argv)
    _putenv_s("CUDA_DEVICE_MAX_CONNECTIONS", "32");

    const int blockSize = 256, nStreams = 4;
    const int n = 4 * 1024 * blockSize * nStreams;
    const int streamSize = n / nStreams;
    const int streamBytes = streamSize * sizeof(float);
    const int bytes = n * sizeof(float);

    int devId = 0;
    if (argc > 1) devId = atoi(argv[1]);

    cudaDeviceProp prop;
    checkCuda(cudaGetDeviceProperties(&prop, devId));
    printf("Device : %s\n",;

    // allocate pinned host memory and device memory
    float *a, *d_a;
    checkCuda(cudaMallocHost((void**)&a, bytes));      // host pinned
    checkCuda(cudaMalloc((void**)&d_a, bytes)); // device

    float ms; // elapsed time in milliseconds

    // create events and streams
    cudaEvent_t startEvent, stopEvent, dummyEvent;
    cudaStream_t stream[nStreams];
    for (int i = 0; i < nStreams; ++i)

    // baseline case - sequential transfer and execute
    memset(a, 0, bytes);
    checkCuda(cudaEventRecord(startEvent, 0));
    checkCuda(cudaMemcpy(d_a, a, bytes, cudaMemcpyHostToDevice));
    kernel << <n / blockSize, blockSize >> >(d_a, 0);
    checkCuda(cudaMemcpy(a, d_a, bytes, cudaMemcpyDeviceToHost));
    checkCuda(cudaEventRecord(stopEvent, 0));
    checkCuda(cudaEventElapsedTime(&ms, startEvent, stopEvent));
    printf("Time for sequential transfer and execute (ms): %f\n", ms);
    printf("  max error: %e\n", maxError(a, n));

    // asynchronous version 1: loop over {copy, kernel, copy}
    memset(a, 0, bytes);
    checkCuda(cudaEventRecord(startEvent, 0));
    for (int i = 0; i < nStreams; ++i) {
        int offset = i * streamSize;
        checkCuda(cudaMemcpyAsync(&d_a[offset], &a[offset],
            streamBytes, cudaMemcpyHostToDevice,
        kernel << <streamSize / blockSize, blockSize, 0, stream[i] >> >(d_a, offset);
        checkCuda(cudaMemcpyAsync(&a[offset], &d_a[offset],
            streamBytes, cudaMemcpyDeviceToHost,
    checkCuda(cudaEventRecord(stopEvent, 0));
    checkCuda(cudaEventElapsedTime(&ms, startEvent, stopEvent));
    printf("Time for asynchronous V1 transfer and execute (ms): %f\n", ms);
    printf("  max error: %e\n", maxError(a, n));

    // asynchronous version 2: 
    // loop over copy, loop over kernel, loop over copy
    memset(a, 0, bytes);
    checkCuda(cudaEventRecord(startEvent, 0));
    for (int i = 0; i < nStreams; ++i)
        int offset = i * streamSize;
        checkCuda(cudaMemcpyAsync(&d_a[offset], &a[offset],
            streamBytes, cudaMemcpyHostToDevice,
    for (int i = 0; i < nStreams; ++i)
        int offset = i * streamSize;
        kernel << <streamSize / blockSize, blockSize, 0, stream[i] >> >(d_a, offset);
    for (int i = 0; i < nStreams; ++i)
        int offset = i * streamSize;
        checkCuda(cudaMemcpyAsync(&a[offset], &d_a[offset],
            streamBytes, cudaMemcpyDeviceToHost,
    checkCuda(cudaEventRecord(stopEvent, 0));
    checkCuda(cudaEventElapsedTime(&ms, startEvent, stopEvent));
    printf("Time for asynchronous V2 transfer and execute (ms): %f\n", ms);
    printf("  max error: %e\n", maxError(a, n));

    // cleanup
    for (int i = 0; i < nStreams; ++i)


    return 0;


What you are observing is probably an artifact of running the code on a Windows WDDM platform. The WDDM subsystem has a lot of latency which other platforms are not hampered by, so to improve overall performance, the CUDA WDDM driver performs command batching. This can interfere with the expect ordering or timing of concurrent operations and command overlap, and is probably what you are seeing here.

The solution is to either use the Windows TCC driver, which requires a supported Telsa or Quadro card, or change to a non WDDM platform like Linux. The latter seems to have solved the problem in this case.


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