Hello,
I’ve been exploring methods to detect and assess the impact of synchronization between thread blocks in CUDA programming. I understand the importance of minimizing global synchronization due to its potential performance impact, though it may be necessary in scenarios involving data transfer to host memory.
I attempted to use NVIDIA’s Nsight system/compute for profiling but did not find the specific synchronization-related information I sought. I aim to understand and quantify the frequency and impact of thread block synchronization.
One approach I considered was counting the occurrences of synchronization methods like cudaDeviceSynchronize(). However, I am concerned that this alone may not capture all synchronization dependencies, as there could be hidden instructions or implicit dependencies between thread blocks.
Could anyone provide insights or suggestions on how to measure and analyze thread block synchronization overhead in CUDA applications effectively? I am particularly interested in understanding when and how thread blocks synchronize and the implications for overall application performance.
Thank you for any guidance or recommendations you can offer.
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Greetings,
Synchronization is a big topic, and CUDA offers multiple paradigms for how you define and schedule your work – streams, dynamic parallelism, cooperative groups, events, and graphs all different features and abilities.
It isn’t clear to me what sort of synchronization you are looking to evaluate. Are you looking to measure synchronization between the host and the device, or within the device? There are, broadly, a few types of device-level sync:
- Intra-block synchronization, where threads of a block synchronize (via
__syncthreads())
- Inter-block synchronization, where blocks wait on blocks
- grid/kernel level, like dynamic parallelism or graph where something is waiting on multiple blocks
I’ll refer you to CUDA guide on Asynchronous Concurrent Execution and the Guidelines for Maximizing Utilization for much better discussions on these topics.
Nsight Systems will give you the ability to trace API calls such as cudaDeviceSynchronize() or cudaStreamSynchronize() for evaluating the host-to-device synchronization time. It will help to identify when the GPU is busy or idle, and if memory transfers are happening concurrently with kernel execution such that you are effectively “hiding the latency” of the host-to-device transfers. It also provides a rich amount of data about what your computer is doing, to help you diagnose performance issues that are happening off-GPU.
Nsight Compute is the best tool if you are looking to closely examine the performance of any one kernel. Nsys can also make it easy for you to go from one app to the other by providing you the command line for ncu.
Hope this provides a little clarity.
Thank you for your response, @mhallock.
I am particularly interested in evaluating the effects of inter-block synchronization. I understand that this type of synchronization should generally be avoided. One approach is to use multiple kernel launches, with each kernel writing to global memory at the end, making the data available to all threads.
However, I am curious about how to quantify the effect of inter-block synchronization if it could be achieved without multiple kernel launches. For example, I am trying to evaluate a reduction problem that requires different thread blocks to synchronize.
Any advice or hints you could provide on this matter would be greatly appreciated.
Thank you in advance.
Thanks for the clarification.
I have an inelegant solution for you: ignore your data dependencies required for algorithmic correctness, and remove your inter-block synchronization, and ideally express the whole thing as a single kernel. Now, you aren’t getting the right answer, but you would be able to measure the runtime of the kernel if you were magically able to reduce the synchronization cost to zero. This gives you your “best possible” result you could hope for.
From there, you can determine what possible performance you are leaving on the table in order to get the right answer. Examine a multi-kernel approach, and see how much better you can do with other methods. Also, if you haven’t already, please look into CUB because they have a lot of great tricks up their sleeves for performing reductions.
There may be a more elegant solution in Nsight Compute, but that is outside of my wheelhouse. Let me confer with their developers and see what else we could recommend.
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Thank you for your insightful idea, @mhallock. It’s a really interesting approach, and I will explore it further while learning more about CUB.