Day 41: Advanced Streams & Multi-Stream Concurrency

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1181 字

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8 分钟

Objective:
Learn to manage multiple CUDA streams for parallel kernel execution and overlapping data transfers. By exploiting multiple streams, you can improve GPU utilization and reduce idle time. However, oversubscribing the GPU with too many streams can saturate resources and degrade performance, so it’s crucial to find a balance.

Key References:

• CUDA C Programming Guide – Streams
• CUDA C Best Practices Guide – Streams & Concurrency

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Table of Contents

1. Overview
2. Benefits of Multiple Streams
3. Practical Exercise: Parallel Kernel Execution & Overlapping
   • a) Example Code Snippet
   • b) Observing Concurrency & Pitfalls
4. Best Practices for Multi-Stream Usage
5. Conceptual Diagrams
6. Common Pitfalls & Debugging
7. References & Further Reading
8. Conclusion
9. Next Steps

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1. Overview

CUDA streams are command queues that allow you to run operations asynchronously and concurrently (kernels, memory copies, etc.) on the GPU. By default, operations in the same stream occur in order, but operations in different streams can overlap if resources are available. This leads to better GPU utilization if kernels or data transfers can run concurrently without interfering.

However:

• Creating too many streams or launching many tiny kernels can oversubscribe GPU resources, leading to overhead and scheduling bottlenecks.
• Streams must be used judiciously, ensuring concurrency where beneficial and avoiding fragmentation.

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2. Benefits of Multiple Streams

1. Overlapping: Memory copies can overlap with kernel execution in another stream if they don’t access the same data.
2. Parallel Kernel Execution: Two or more kernels that use different portions of GPU resources can run concurrently.
3. Pipelining: While one stream processes batch k, another stream can transfer data for batch k+1.

Real-world examples:

• In neural networks or real-time signal processing, multiple data sets or tasks can be processed in parallel streams.

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3. Practical Exercise: Parallel Kernel Execution & Overlapping

a) Example Code Snippet

// day41_multiStream.cu
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>

__global__ void vectorAddKernel(const float *A, const float *B, float *C, int N) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if(idx < N){
        C[idx] = A[idx] + B[idx];
    }
}

#define CUDA_CHECK(call) {                                            \
    cudaError_t err = call;                                           \
    if(err != cudaSuccess) {                                          \
        printf("CUDA Error at %s:%d - %s\n", __FILE__, __LINE__,      \
               cudaGetErrorString(err));                              \
        exit(EXIT_FAILURE);                                           \
    }                                                                 \
}

int main(){
    int N = 1 << 20; // 1M
    size_t size = N * sizeof(float);

    // Allocate host memory
    float *h_A = (float*)malloc(size);
    float *h_B = (float*)malloc(size);
    float *h_C = (float*)malloc(size);
    float *h_D = (float*)malloc(size);

    srand(time(NULL));
    for(int i=0; i<N; i++){
        h_A[i] = (float)(rand() % 100);
        h_B[i] = (float)(rand() % 100);
    }

    // Allocate device memory
    float *d_A1, *d_B1, *d_C1;
    float *d_A2, *d_B2, *d_C2;
    CUDA_CHECK(cudaMalloc(&d_A1, size));
    CUDA_CHECK(cudaMalloc(&d_B1, size));
    CUDA_CHECK(cudaMalloc(&d_C1, size));
    CUDA_CHECK(cudaMalloc(&d_A2, size));
    CUDA_CHECK(cudaMalloc(&d_B2, size));
    CUDA_CHECK(cudaMalloc(&d_C2, size));

    // Create two streams
    cudaStream_t stream1, stream2;
    CUDA_CHECK(cudaStreamCreate(&stream1));
    CUDA_CHECK(cudaStreamCreate(&stream2));

    // Asynchronously copy data to device in two streams
    CUDA_CHECK(cudaMemcpyAsync(d_A1, h_A, size, cudaMemcpyHostToDevice, stream1));
    CUDA_CHECK(cudaMemcpyAsync(d_B1, h_B, size, cudaMemcpyHostToDevice, stream1));

    CUDA_CHECK(cudaMemcpyAsync(d_A2, h_A, size, cudaMemcpyHostToDevice, stream2));
    CUDA_CHECK(cudaMemcpyAsync(d_B2, h_B, size, cudaMemcpyHostToDevice, stream2));

    // Kernel launch config
    int threadsPerBlock = 256;
    int blocksPerGrid = (N + threadsPerBlock -1)/threadsPerBlock;

    // Launch two kernels in parallel streams
    vectorAddKernel<<<blocksPerGrid, threadsPerBlock, 0, stream1>>>(d_A1, d_B1, d_C1, N);
    vectorAddKernel<<<blocksPerGrid, threadsPerBlock, 0, stream2>>>(d_A2, d_B2, d_C2, N);

    // Copy results back from each stream asynchronously
    CUDA_CHECK(cudaMemcpyAsync(h_C, d_C1, size, cudaMemcpyDeviceToHost, stream1));
    CUDA_CHECK(cudaMemcpyAsync(h_D, d_C2, size, cudaMemcpyDeviceToHost, stream2));

    // Synchronize all streams
    CUDA_CHECK(cudaStreamSynchronize(stream1));
    CUDA_CHECK(cudaStreamSynchronize(stream2));

    // Check partial results
    printf("h_C[0] = %f, h_D[0] = %f\n", h_C[0], h_D[0]);

    // Cleanup
    CUDA_CHECK(cudaStreamDestroy(stream1));
    CUDA_CHECK(cudaStreamDestroy(stream2));
    cudaFree(d_A1); cudaFree(d_B1); cudaFree(d_C1);
    cudaFree(d_A2); cudaFree(d_B2); cudaFree(d_C2);
    free(h_A); free(h_B); free(h_C); free(h_D);

    return 0;
}

Explanation:

• Allocates and copies data for two separate tasks.
• Streams (stream1 and stream2) handle their own data copies and kernel launches.
• If resources allow, the GPU can run these two vectorAdd kernels concurrently.

b) Observing Concurrency & Pitfalls

• Use Nsight Systems or Nsight Compute to see whether both kernels overlap.
• If the GPU is oversubscribed (e.g., too many small kernels or too many streams), overhead might overshadow concurrency benefits.

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4. Best Practices for Multi-Stream Usage

1. Use Streams for Overlapping: Overlap memory copies with kernel execution or run independent kernels in parallel.
2. Avoid Tiny Kernels: Large overhead may degrade performance if each stream only runs small tasks.
3. Ensure Resource Availability: The GPU can only run so many blocks concurrently. Too many streams might lead to scheduling thrash.
4. Measure: Always measure if concurrency actually speeds up the application.

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5. Conceptual Diagrams

Diagram 1: Multi-Stream Timeline

flowchart TD
    A[CPU Launch Kernel 1 in stream1]
    B[CPU Launch Kernel 2 in stream2]
    C[GPU runs Kernel 1]
    D[GPU runs Kernel 2 concurrently]
    E[GPU memory copies overlap if different streams & data]

    A --> C
    B --> D
    C --> E
    D --> E

Explanation:

• Shows how each kernel and memory copy can overlap if assigned to different streams.

Diagram 2: Potential Over-Subscription

flowchart LR
    subgraph Over-Subscribing GPU
    direction TB
    T1[stream1: kernelA] --> T2[stream2: kernelB] --> T3[stream3: kernelC] --> ...
    end

Explanation:

• If each stream launches many kernels, the GPU sees an explosion of tasks. Some concurrency is beneficial, but too many small tasks might lead to scheduling overhead.

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6. Common Pitfalls & Debugging

1. Data Dependencies
   • If stream1’s output is needed by stream2, use events or cudaStreamWaitEvent() to enforce order.
2. Mismatched Memory
   • Overlapping memory copies for the same buffer can cause corruption if not carefully managed.
3. Host-Side Synchronization
   • If you do a cudaMemcpy() without specifying a stream, it might implicitly sync the default stream.
4. Resource Overload
   • Too many concurrency attempts can degrade performance; measure to confirm speedups.

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7. References & Further Reading

1. CUDA C Programming Guide – Streams
   Stream Documentation
2. CUDA C Best Practices Guide – Asynchronous Transfers
   Best Practices: Streams & Overlapping
3. NVIDIA Developer Blog
   Articles on multi-stream concurrency usage, pitfalls, and examples.

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8. Conclusion

Day 41 focuses on Advanced Streams & Multi-Stream Concurrency:

• Goal: Overlap kernel execution or data transfers in separate streams for improved GPU utilization.
• Key: Each stream operates independently, allowing parallelism if the GPU has resources available.
• Pitfall: Over-subscribing the GPU with too many streams or too many small kernels can lead to overhead rather than performance gains.
• Example: Running two vector-add kernels in parallel in different streams to illustrate concurrency.

Takeaway: Streams can yield significant performance benefits by overlapping tasks, but must be used carefully to avoid resource saturation or scheduling overhead. Always profile and measure real improvements.

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9. Next Steps

1. Add Overlapping Mem Copies: Attempt overlapping large data transfers in one stream with kernel execution in another.
2. Experiment: Increase the number of streams and tasks; measure performance. Identify the sweet spot.
3. Enforce Dependencies: Practice using events (cudaEvent_t) and cudaStreamWaitEvent() to manage data dependencies among streams.
4. Profile: Use Nsight Systems to visualize concurrency timeline and confirm actual overlaps.

Happy CUDA coding, and keep exploring concurrency for more efficient GPU scheduling!

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