/*
 * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
 *
 * Please refer to the NVIDIA end user license agreement (EULA) associated
 * with this source code for terms and conditions that govern your use of
 * this software. Any use, reproduction, disclosure, or distribution of
 * this software and related documentation outside the terms of the EULA
 * is strictly prohibited.
 *
 */

//
// This sample demonstrates how HyperQ allows supporting devices to avoid false
// dependencies between kernels in different streams.
//
// - Devices without HyperQ will run a maximum of two kernels at a time (one
//   kernel_A and one kernel_B).
// - Devices with HyperQ will run up to 32 kernels simultaneously.

#include <stdio.h>
#include <helper_functions.h>
#include <helper_cuda.h>

const char *sSDKsample = "hyperQ";

// This subroutine does no real work but runs for at least the specified number
// of clock ticks.
__device__ void clock_block(clock_t *d_o, clock_t clock_count)
{
     unsigned int start_clock = (unsigned int) clock();

    clock_t clock_offset = 0;

    while (clock_offset < clock_count)
    {
        unsigned int end_clock = (unsigned int) clock();

        // The code below should work like 
        // this (thanks to modular arithmetics): 
        // 
        // clock_offset = (clock_t) (end_clock > start_clock ? 
        //                           end_clock - start_clock : 
        //                           end_clock + (0xffffffffu - start_clock));
        //
        // Indeed, let m = 2^32 then
        // end - start = end + m - start (mod m).

        clock_offset = (clock_t) (end_clock - start_clock);
    }

    d_o[0] = clock_offset;
}

// We create two identical kernels calling clock_block(), we create two so that
// we can identify dependencies in the profile timeline ("kernel_B" is always
// dependent on "kernel_A" in the same stream).
__global__ void kernel_A(clock_t *d_o, clock_t clock_count)
{
    clock_block(d_o, clock_count);
}
__global__ void kernel_B(clock_t *d_o, clock_t clock_count)
{
    clock_block(d_o, clock_count);
}

// Single-warp reduction kernel (note: this is not optimized for simplicity)
__global__ void sum(clock_t *d_clocks, int N)
{
    __shared__ clock_t s_clocks[32];

    clock_t my_sum = 0;

    for (int i = threadIdx.x ; i < N ; i += blockDim.x)
    {
        my_sum += d_clocks[i];
    }

    s_clocks[threadIdx.x] = my_sum;
    __syncthreads();

    for (int i = warpSize / 2 ; i > 0 ; i /= 2)
    {
        if (threadIdx.x < i)
        {
            s_clocks[threadIdx.x] += s_clocks[threadIdx.x + i];
        }

        __syncthreads();
    }

    if (threadIdx.x == 0)
    {
        d_clocks[0] = s_clocks[0];
    }
}


int main(int argc, char **argv)
{
    int nstreams = 32;          // One stream for each pair of kernels
    float kernel_time = 10;     // Time each kernel should run in ms
    float elapsed_time;
    int cuda_device = 0;

    printf("starting %s...\n", sSDKsample);

    // Get number of streams (if overridden on the command line)
    if (checkCmdLineFlag(argc, (const char **)argv, "nstreams"))
    {
        nstreams = getCmdLineArgumentInt(argc, (const char **)argv, "nstreams");
    }

    // Use command-line specified CUDA device, otherwise use device with
    // highest Gflops/s
    cuda_device = findCudaDevice(argc, (const char **)argv);

    // Get device properties
    cudaDeviceProp deviceProp;
    checkCudaErrors(cudaGetDevice(&cuda_device));
    checkCudaErrors(cudaGetDeviceProperties(&deviceProp, cuda_device));

    // HyperQ is available in devices of Compute Capability 3.5 and higher
    if (deviceProp.major < 3 || (deviceProp.major == 3 && deviceProp.minor < 5))
    {
        if (deviceProp.concurrentKernels == 0)
        {
            printf("> GPU does not support concurrent kernel execution (SM 3.5 or higher required)\n");
            printf("  CUDA kernel runs will be serialized\n");
        }
        else
        {
            printf("> GPU does not support HyperQ\n");
            printf("  CUDA kernel runs will have limited concurrency\n");
        }
    }

    printf("> Detected Compute SM %d.%d hardware with %d multi-processors\n",
           deviceProp.major, deviceProp.minor, deviceProp.multiProcessorCount);

    // Allocate host memory for the output (reduced to a single value)
    clock_t *a = 0;
    checkCudaErrors(cudaMallocHost((void **)&a, sizeof(clock_t)));

    // Allocate device memory for the output (one value for each kernel)
    clock_t *d_a = 0;
    checkCudaErrors(cudaMalloc((void **)&d_a, 2 * nstreams * sizeof(clock_t)));

    // Allocate and initialize an array of stream handles
    cudaStream_t *streams = (cudaStream_t *) malloc(nstreams * sizeof(cudaStream_t));

    for (int i = 0 ; i < nstreams ; i++)
    {
        checkCudaErrors(cudaStreamCreate(&(streams[i])));
    }

    // Create CUDA event handles
    cudaEvent_t start_event, stop_event;
    checkCudaErrors(cudaEventCreate(&start_event));
    checkCudaErrors(cudaEventCreate(&stop_event));

    // Target time per kernel is kernel_time ms, clockRate is in KHz
    // Target number of clocks = target time * clock frequency
#if defined(__arm__) || defined(__aarch64__)
    // the kernel takes more time than the channel reset time on arm archs, so to prevent hangs reduce time_clocks.
    clock_t time_clocks = (clock_t)(kernel_time * (deviceProp.clockRate / 1000));
#else
    clock_t time_clocks = (clock_t)(kernel_time * deviceProp.clockRate);
#endif
    clock_t total_clocks = 0;

    // Start the clock
    checkCudaErrors(cudaEventRecord(start_event, 0));

    // Queue pairs of {kernel_A, kernel_B} in separate streams
    for (int i = 0 ; i < nstreams ; ++i)
    {
        kernel_A<<<1,1,0,streams[i]>>>(&d_a[2*i], time_clocks);
        total_clocks += time_clocks;
        kernel_B<<<1,1,0,streams[i]>>>(&d_a[2*i+1], time_clocks);
        total_clocks += time_clocks;
    }

    // Stop the clock in stream 0 (i.e. all previous kernels will be complete)
    checkCudaErrors(cudaEventRecord(stop_event, 0));

    // At this point the CPU has dispatched all work for the GPU and can
    // continue processing other tasks in parallel. In this sample we just want
    // to wait until all work is done so we use a blocking cudaMemcpy below.

    // Run the sum kernel and copy the result back to host
    sum<<<1,32>>>(d_a, 2 * nstreams);
    checkCudaErrors(cudaMemcpy(a, d_a, sizeof(clock_t), cudaMemcpyDeviceToHost));

    // stop_event will have been recorded but including the synchronize here to
    // prevent copy/paste errors!
    checkCudaErrors(cudaEventSynchronize(stop_event));
    checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event));

    printf("Expected time for serial execution of %d sets of kernels is between approx. %.3fs and %.3fs\n", nstreams, (nstreams + 1) * kernel_time / 1000.0f, 2 * nstreams *kernel_time / 1000.0f);
    printf("Expected time for fully concurrent execution of %d sets of kernels is approx. %.3fs\n", nstreams, 2 * kernel_time / 1000.0f);
    printf("Measured time for sample = %.3fs\n", elapsed_time / 1000.0f);

    bool bTestResult  = (a[0] >= total_clocks);

    // Release resources
    for (int i = 0 ; i < nstreams ; i++)
    {
        cudaStreamDestroy(streams[i]);
    }

    free(streams);
    cudaEventDestroy(start_event);
    cudaEventDestroy(stop_event);
    cudaFreeHost(a);
    cudaFree(d_a);

    exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE);
}
