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I think it does?: (the comment is in the original source)

    print("Adding matrices using GPU...")
    start_time = time.time()
    gpu_result = add_matrices(gpu_matrices)
    cp.cuda.get_current_stream().synchronize() # Not 100% sure what this does
    elapsed_time = time.time() - start_time

I was going to ask, any CUDA professionals who want to give a crash course on what us python guys will need to know?

▲ apbytes 14 days ago | parent [-]

When you call a cuda method, it is launched asynchronously. That is the function queues it up for execution on gpu and returns.

So if you need to wait for an op to finish, you need to `synchronize` as shown above.

`get_current_stream` because the queue mentioned above is actually called stream in cuda.

If you want to run many independent ops concurrently, you can use several streams.

Benchmarking is one use case for synchronize. Another would be if you let's say run two independent ops in different streams and need to combine their results.

Btw, if you work with pytorch, when ops are run on gpu, they are launched in background. If you want to bench torch models on gpu, they also provide a sync api.

▲ claytonjy 14 days ago | parent | next [-]

I’ve always thought it was weird GPU stuff in python doesn’t use asyncio, and mostly assumed it was because python-on-GPU predates asyncio. But I was hoping a new lib like this might right that wrong, but it doesn’t. Maybe for interop reasons?

Do other languages surface the asynchronous nature of GPUs in language-level async, avoiding silly stuff like synchronize?

▲ ImprobableTruth 14 days ago | parent | next [-]

The reason is that the usage is completely different from coroutine based async. With GPUs you want to queue _as many async operations as possible_ and only then synchronize. That is, you would have a program like this (pseudocode):

  b = foo(a)
  c = bar(b)
  d = baz(c)
  synchronize()

With coroutines/async await, something like this

  b = await foo(a)
  c = await bar(b)
  d = await baz(c)

would synchronize after every step, being much more inefficient.

▲

hackernudes 14 days ago | parent | next [-]

Pretty sure you want it to do it the first way in all cases (not just with GPUs)!

	▲	halter73 14 days ago \| parent [-]
		It really depends on if you're dealing with an async stream or a single async result as the input to the next function. If a is an access token needed to access resource b, you cannot access a and b at the same time. You have to serialize your operations.

▲

alanfranz 14 days ago | parent | prev [-]

Well you can and should create multiple coroutine/tasks and then gather them. If you replace cuda with network calls, it’s exactly the same problem. Nothing to do with asyncio.

	▲	ImprobableTruth 14 days ago \| parent [-]
		No, that's a different scenario. In the one I gave there's explicitly a dependency between requests. If you use gather, the network requests would be executed in parallel. If you have dependencies they're sequential by nature because later ones depend on values of former ones. The 'trick' for CUDA is that you declare all this using buffers as inputs/outputs rather than values and that there's automatic ordering enforcement through CUDA's stream mechanism. Marrying that with the coroutine mechanism just doesn't really make sense.

▲ apbytes 14 days ago | parent | prev [-]

Might have to look at specific lib implementations, but I'd guess that mostly gpu calls from python are actually happening in c++ land. And internally a lib might be using synchronize calls where needed.

▲ hnuser123456 14 days ago | parent | prev [-]

Thank you kindly!