sync.md

Synchronize all threads

When there are multiple threads running at parallel in a GPU, it is important that all th threads finish their respective job before returning to host. This is achieved via cudaDeviceSynchronize() function. This function blocks CPU host until all GPU operations are completed.

Implementation pattern

Below is the implementation pattern:

myKernel<<<i, j>>>(input_args);
cudaDeviceSynchronize();

Simply put, right after the kernel call, usecudaDeviceSynchronize to hold CPU from execution next instruction until all GPU operations or threads complete their job.

Explanation

Calling cudaDeviceSynchronize() from the main function or the host code is typically done for the purpose of ensuring synchronization between the CPU (host) and the GPU (device) in a CUDA program. When you launch a kernel or perform asynchronous GPU operations, the CPU continues its execution without waiting for the GPU to finish. This can lead to race conditions or incorrect results if the host code relies on the completion of GPU operations.

Here are some common reasons for using cudaDeviceSynchronize() in the main function or host code:

1. Error Handling: After launching a kernel or executing GPU operations, it's a good practice to check for errors. Calling cudaDeviceSynchronize() after the operations ensures that any errors that occurred during GPU execution are reported to the host, allowing for proper error handling. Example code:

// Kernel launch
myKernel<<<1, 1>>>();

// Ensure synchronization and check for errors
cudaDeviceSynchronize();
cudaError_t error = cudaGetLastError();
if (error != cudaSuccess) {
    fprintf(stderr, "CUDA error: %s\n", cudaGetErrorString(error));
    return -1;
}

2. Timing Measurements: If you are measuring the execution time of a kernel, you might use cudaDeviceSynchronize() to ensure accurate timing. Without synchronization, the timing measurement might include the time taken to launch the kernel but not the time it takes to actually execute. Example code:

cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);threads_per_block

// Record start time
cudaEventRecord(start);

// Kernel launch
myKernel<<<1, 1>>>();

// Ensure synchronization before recording stop time
cudaDeviceSynchronize();
cudaEventRecord(stop);

// Calculate and print elapsed time
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("Kernel execution time: %f ms\n", milliseconds);

3. Data Dependencies: If the host code relies on data produced by GPU computations, you can use cudaDeviceSynchronize() to ensure that the GPU has finished its work before accessing the results. Example code:

// Kernel launch
myKernel<<<1, 1>>>();

// Ensure synchronization before accessing results
cudaDeviceSynchronize();

// Continue with host code that relies on GPU results

In summary, calling cudaDeviceSynchronize() in the main function or host code is a way to coordinate the execution of CPU and GPU code, ensuring correct and synchronized behavior in a CUDA program.