Time spent invoking a CUDA kernel

The time spent invoking a CUDA kernel (i.e., launching it) is typically very small—on the order of microseconds (µs)—but depends on several factors:

Factors Affecting Kernel Launch Time:

1. Driver Overhead:

   • The CUDA driver must perform checks, set up kernel parameters, and schedule the kernel on the GPU.
   • This usually takes 5–50 µs on modern systems.

2. Kernel Configuration:

   • The number of thread blocks and grid dimensions can slightly affect launch overhead, but the impact is usually minimal.

3. Synchronization:

   • By default, kernel launches are asynchronous (the CPU continues execution without waiting).
   • If you explicitly synchronize (e.g., with cudaDeviceSynchronize()), the total time includes kernel execution, not just launch overhead.

4. CUDA Context Initialization:

   • The first kernel launch in a program may take longer (up to 100–500 ms) due to CUDA context setup.
   • Subsequent launches are much faster.

5. Hardware & CUDA Version:

   • Newer GPUs and CUDA versions tend to have lower launch overhead.

How to Measure Kernel Launch Time:

You can use CUDA events to measure the launch time:

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

cudaEventRecord(start);
myKernel<<<grid, block>>>(...); // Kernel launch
cudaEventRecord(stop);

cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);

printf("Kernel launch time: %f µs\n", milliseconds * 1000);

(Note: This measures launch + minimal GPU execution; pure launch overhead is hard to isolate.)

Summary:

• Typical kernel launch time: ~5–50 µs (after context setup).
• First launch in a program: Much slower (~100–500 ms) due to CUDA initialization.
• Kernel execution time: Separate from launch time (depends on the kernel’s workload).

If you need ultra-low-latency launches, consider:

• Avoiding frequent small kernel launches (use larger kernels or dynamic parallelism).
• Using CUDA Graphs to reduce launch overhead for repetitive workloads.

Would you like help optimizing a specific scenario?