What is the difference between CUDA's cudaMemcpyAsync and cudaMemcpy?

Understanding the Difference Between cudaMemcpy and cudaMemcpyAsync

When programming with NVIDIA's CUDA, transferring data between host (CPU) memory and device (GPU) memory is a common operation. CUDA provides two primary functions for handling these transfers: cudaMemcpy and cudaMemcpyAsync. Below, we'll explore the key differences between these two functions, along with their practical use cases.

What is cudaMemcpy?

cudaMemcpy is a synchronous memory copy function provided by CUDA. Being synchronous means that the CPU execution halts until the memory transfer is complete.

Key Characteristics of cudaMemcpy:

• Synchronous: CPU waits until the memory transfer operation is fully completed.
• Simple and straightforward: Typically used for simple, non-overlapping operations.
• Easier debugging: Since the CPU waits, debugging and error handling can be simpler.

Example of cudaMemcpy usage:

// Allocate memory on host and device
float *h_array, *d_array;
size_t size = 1024 * sizeof(float);

h_array = (float*)malloc(size);
cudaMalloc(&d_array, size);

// Copy data from host to device synchronously
cudaMemcpy(d_array, h_array, size, cudaMemcpyHostToDevice);

What is cudaMemcpyAsync?

cudaMemcpyAsync is the asynchronous counterpart to cudaMemcpy. It allows the CPU to continue executing further instructions without waiting for the memory transfer to complete. This function is typically used in scenarios where overlapping computation and data transfer can significantly enhance performance.

Key Characteristics of cudaMemcpyAsync:

• Asynchronous: CPU execution continues immediately after initiating the transfer.
• Stream-Based: Requires the specification of a CUDA stream for execution.
• Performance Optimization: Enables overlapping of data transfer with GPU computation, improving throughput and reducing latency.

Example of cudaMemcpyAsync usage:

// Allocate memory on host and device
float *h_array, *d_array;
size_t size = 1024 * sizeof(float);

h_array = (float*)malloc(size);
cudaMalloc(&d_array, size);

// Create CUDA stream
cudaStream_t stream;
cudaStreamCreate(&stream);

// Asynchronously copy data from host to device
cudaMemcpyAsync(d_array, h_array, size, cudaMemcpyHostToDevice, stream);

// Perform kernel execution or other computations here
myKernel<<<blocks, threads, 0, stream>>>(d_array);

// Synchronize stream before accessing results
cudaStreamSynchronize(stream);

// Clean up
cudaStreamDestroy(stream);

When to Use cudaMemcpy vs cudaMemcpyAsync

Use cudaMemcpy when:

• You prefer simpler, easier-to-debug code.
• Your application does not benefit significantly from overlapping transfers with computations.
• You're performing quick testing or prototyping.

Use cudaMemcpyAsync when:

• You want to maximize GPU utilization by overlapping data transfers and GPU computations.
• Your application has large or frequent data transfers that could benefit from parallelism.
• You are working with advanced, performance-critical workloads.

Performance Considerations

For best performance, especially in production or computationally intensive applications, prefer using cudaMemcpyAsync along with CUDA streams to achieve concurrent data transfers and computations. Effective use of asynchronous memory transfers can dramatically improve overall application throughput and reduce latency.

Summary: Quick Comparison Table

By clearly understanding these differences and leveraging asynchronous memory operations where suitable, you can significantly optimize your CUDA applications' performance and responsiveness.