Cuda Programming 6: Advanced CUDA Programming – Streams and Overlapping Computation

Introduction:

CUDA allows you to take full advantage of your GPU’s parallel processing power. One powerful feature of CUDA is streams, which enable the concurrent execution of multiple operations, such as memory transfers and kernel launches. By overlapping data transfers with computation, you can avoid idle times and fully utilize your GPU’s processing capabilities.

In this blog, we will explore how to use streams to optimize your CUDA programs, as well as how to overlap computation with memory transfers to improve the performance of your applications.

1. What are CUDA Streams?

CUDA streams allow you to execute operations asynchronously. Normally, when a CUDA program executes, it follows a strict sequence of instructions—first, it executes memory transfers, then it runs kernels. This can lead to idle time on the GPU, as data is being transferred while computation could be taking place.

With streams, you can overlap memory operations (like cudaMemcpy()) and kernel launches to make better use of the GPU. Multiple streams can run concurrently on the GPU, allowing memory copies and kernel execution to happen simultaneously.

How Streams Work:

• By default, all CUDA operations are placed into stream 0, which is the default stream. Operations in the default stream execute in order.
• When you create a new stream, you allow CUDA to execute operations in parallel, as long as they are independent.

Example:

cppCopyEditcudaStream_t stream1, stream2;
cudaStreamCreate(&stream1);
cudaStreamCreate(&stream2);

// Launching kernel in stream 1
kernel<<<grid, block, 0, stream1>>>(d_data);

// Launching kernel in stream 2
kernel<<<grid, block, 0, stream2>>>(d_data);

// Memory transfer in stream 1
cudaMemcpyAsync(d_data, h_data, size, cudaMemcpyHostToDevice, stream1);

// Memory transfer in stream 2
cudaMemcpyAsync(h_data, d_data, size, cudaMemcpyDeviceToHost, stream2);

// Synchronizing streams
cudaStreamSynchronize(stream1);
cudaStreamSynchronize(stream2);

cudaStreamDestroy(stream1);
cudaStreamDestroy(stream2);

In this example, we use two separate streams (stream1 and stream2). Memory transfers and kernel executions in different streams can run concurrently. The cudaMemcpyAsync calls will not block the kernel launches, enabling better GPU utilization.

2. Overlapping Memory Transfers with Computation

One of the most effective ways to improve the performance of CUDA applications is to overlap memory transfers with kernel execution. This means that while the GPU is performing a computation, you can also transfer data to and from the host (CPU) and device (GPU) without waiting for one operation to finish before starting the next.

Example of Overlapping Memory Transfers and Kernels:

cppCopyEdit#include <iostream>
#include <cuda_runtime.h>

#define N 1024  // Size of data

__global__ void simpleKernel(int *data) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    data[idx] = data[idx] * 2;  // Simple operation
}

int main() {
    int *h_data, *d_data;
    size_t size = N * sizeof(int);
    
    // Allocate memory on host and device
    h_data = (int*)malloc(size);
    cudaMalloc(&d_data, size);
    
    // Initialize data on host
    for (int i = 0; i < N; i++) {
        h_data[i] = i;
    }

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

    // Start memory transfer in stream1 (Host to Device)
    cudaMemcpyAsync(d_data, h_data, size, cudaMemcpyHostToDevice, stream1);
    
    // Launch the kernel in stream2 (Device computation)
    simpleKernel<<<(N + 255) / 256, 256, 0, stream2>>>(d_data);

    // Start memory transfer in stream2 (Device to Host)
    cudaMemcpyAsync(h_data, d_data, size, cudaMemcpyDeviceToHost, stream2);

    // Synchronize streams to ensure all operations are completed
    cudaStreamSynchronize(stream1);
    cudaStreamSynchronize(stream2);

    // Display results
    for (int i = 0; i < N; i++) {
        std::cout << h_data[i] << " ";
    }

    // Clean up
    free(h_data);
    cudaFree(d_data);
    cudaStreamDestroy(stream1);
    cudaStreamDestroy(stream2);

    return 0;
}

Explanation:

1. Memory Transfer (Host to Device): The memory transfer from the host to the device is initiated in stream1 using cudaMemcpyAsync().
2. Kernel Execution (Device): The kernel (simpleKernel) runs concurrently in stream2 while the memory transfer continues in stream1.
3. Memory Transfer (Device to Host): Another memory transfer happens from the device to the host concurrently with the kernel execution, using cudaMemcpyAsync() in stream2.
4. Synchronization: We call cudaStreamSynchronize() to wait for all operations in each stream to finish before proceeding.

By overlapping these operations, we minimize the idle time on both the CPU and the GPU.

3. Launching Multiple Kernels Simultaneously

CUDA streams not only allow for overlapping memory transfers with computation, but they also enable launching multiple kernels at the same time, as long as the kernels don’t depend on each other.

You can use multiple streams to launch independent kernels concurrently on the GPU, which can significantly speed up execution when multiple tasks need to be performed in parallel.

Example:

cppCopyEditcudaStream_t stream1, stream2;
cudaStreamCreate(&stream1);
cudaStreamCreate(&stream2);

// Kernel 1 launched in stream 1
kernel1<<<grid1, block1, 0, stream1>>>(d_data1);

// Kernel 2 launched in stream 2
kernel2<<<grid2, block2, 0, stream2>>>(d_data2);

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

// Cleanup
cudaStreamDestroy(stream1);
cudaStreamDestroy(stream2);

By launching kernel1 and kernel2 in different streams, they will run concurrently (assuming there are enough resources on the GPU).

4. Practical Considerations

• Stream Synchronization: When launching multiple kernels or memory transfers in different streams, synchronization becomes crucial. If one stream depends on the result of another, use cudaStreamSynchronize() or cudaDeviceSynchronize() to ensure proper execution order.
• Hardware Limitations: Although streams enable concurrent execution, the actual level of concurrency depends on the GPU’s hardware. Some GPUs may support more concurrent operations than others.
• Maximizing Concurrency: The maximum amount of concurrency you can achieve depends on the number of multiprocessors (SMs) on your GPU and the resources each kernel requires. Profiling tools like NVIDIA Nsight can help you identify bottlenecks and adjust kernel launch parameters.

5. Conclusion

CUDA streams are a powerful feature that can significantly improve the performance of your CUDA programs by allowing the overlapping of memory transfers and kernel execution. By using multiple streams, you can execute kernels concurrently, making better use of GPU resources and reducing idle times. This technique is essential for optimizing complex programs that require high throughput, such as simulations or large-scale data processing.

In the next blog, we will explore how to use CUDA events to measure the timing of operations and analyze performance more effectively.

Call to Action:

• Have you used CUDA streams in your projects? Share your experiences in the comments!
• Next up: We’ll dive into CUDA events and timing techniques to analyze and optimize your CUDA applications.