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.
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?
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.
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.
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.
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.
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.
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.