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0 pointssillysaurusx5y ago0 comments

I don't know where all of this misinformation is coming from or why, but, as someone who has spent the last year programming TPUs to do all kinds of things that a GPU can't do, this isn't true.

Are we going to simply say "Nu uh" at each other, or do you want to throw down some specific examples so I can show you how mistaken they are?

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7 comments · 3 top-level

sorenbouma5y ago· 2 in thread

I'm a TPU user and I'd be interested to see a specific example of something that can be done on TPU but not GPU.

Perhaps I'm just not experienced enough with the programming model, but I've found them to be strictly less flexible/more tricky than GPUs, especially for things like conditional execution, multiple graphs, variable size inputs and custom ops.

sillysaurusxOP5y ago

Sure! I'd love to chat TPUs. There's a #tpu discord channel on the MLPerf discord: https://github.com/shawwn/tpunicorn#ml-community

The central reason that TPUs feel less flexible is Google's awful mistake in encouraging everyone to use TPUEstimator as the One True API For Doing TPU Programming. Getting off that API was the single biggest boost to my TPU skills.

You can see an example of how to do that here: https://github.com/shawwn/ml-notes/blob/master/train_runner.... This is a repo that can train GPT-2 1.5B at 10 examples/sec on a TPUv3-8 (aka around 10k tokens/sec).

Happy to answer any specific questions or peek at codebases you're hoping to run on TPUs.

slaymaker19075y ago

That doesn't answer the question of what a TPU can do that a GPU can't. I think the OP means impossible for the GPU, not just slower.

shaklee35y ago· 2 in thread

Please show me the API where I can write a generic function on a TPU. I'm talking about writing something like a custom reduction or a peak search, not offloading a tensor flow model.

I'll make it easier for you, directly from Google's website:

TPUs Cloud TPUs are optimized for specific workloads. In some situations, you might want to use GPUs or CPUs on Compute Engine instances to run your machine learning workloads.

Please tell me a workload a gpu can't do that a TPU can.

sillysaurusxOP5y ago

Sure, here you go: https://www.tensorflow.org/api_docs/python/tf/raw_ops

In my experience, well over 80% of these operations are implemented on TPU CPUs, and at least 60% are implemented on TPU cores.

Again, if you give a specific example, I can simply write a program demonstrating that it works. What kind of custom reduction do you want? What's a peak search?

As for workloads that GPUs can't do, we regularly train GANs at 500+ examples/sec across a total dataset size of >3M photos. Rather hard to do that with GPUs.

shaklee35y ago

Well, there you go. For one TensorFlow is not a generic framework like cuda is, so you lose a whole bunch of the configurability you have with cuda. So, for example, even though there is an FFT raw function, there doesn't appear to be a way to do more complicated FFTs, such as an overlap-save. This is trivial to do on a GPU, and is built into the library. The raw functions it provides is not direct access to the hardware and memory subsystem. It's a set of raw functions that is a small subset of the total problem space. And certainly if you are saying that running something on a TPU's CPU cores are in any way going to compete with a gpu, then I don't know what to tell you.

You did not give an example of something GPUs can't do. all you said was that TPUs are faster for a specific function in your case.

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slaymaker19075y ago

You can run basically any C program on a CUDA core even those requiring malloc. It may not be efficient but you can do it. Google themselves call GPUs general purpose and TPUs domain specific. https://cloud.google.com/blog/products/ai-machine-learning/w...

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7 comments · 3 top-level

sorenbouma5y ago· 2 in thread

I'm a TPU user and I'd be interested to see a specific example of something that can be done on TPU but not GPU.

sillysaurusxOP5y ago

Sure! I'd love to chat TPUs. There's a #tpu discord channel on the MLPerf discord: https://github.com/shawwn/tpunicorn#ml-community

Happy to answer any specific questions or peek at codebases you're hoping to run on TPUs.

slaymaker19075y ago

That doesn't answer the question of what a TPU can do that a GPU can't. I think the OP means impossible for the GPU, not just slower.

shaklee35y ago· 2 in thread

Please show me the API where I can write a generic function on a TPU. I'm talking about writing something like a custom reduction or a peak search, not offloading a tensor flow model.

I'll make it easier for you, directly from Google's website:

TPUs Cloud TPUs are optimized for specific workloads. In some situations, you might want to use GPUs or CPUs on Compute Engine instances to run your machine learning workloads.

Please tell me a workload a gpu can't do that a TPU can.

sillysaurusxOP5y ago

Sure, here you go: https://www.tensorflow.org/api_docs/python/tf/raw_ops

In my experience, well over 80% of these operations are implemented on TPU CPUs, and at least 60% are implemented on TPU cores.

Again, if you give a specific example, I can simply write a program demonstrating that it works. What kind of custom reduction do you want? What's a peak search?

As for workloads that GPUs can't do, we regularly train GANs at 500+ examples/sec across a total dataset size of >3M photos. Rather hard to do that with GPUs.

shaklee35y ago

You did not give an example of something GPUs can't do. all you said was that TPUs are faster for a specific function in your case.

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slaymaker19075y ago

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