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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 exa

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.

sillysaurusx 6y ago
For one TensorFlow is not a generic framework like cuda is, so you lose a whole bunch of the configurability you have with cuda Why make generalizations like this? It's not true, and we've devolved back into the "nu uh" we originally started with. This is trivial to do on a GPU, and is built into the library Yes, I'm sure there are hardwired operations that are trivial to do on GPUs. That's not exactly a +1 in favor of generic programmability. There are also operations that are trivial to do on TPUs, such as CrossReplicaSum across a massive cluster of cores, or the various special-case Adam operations. This doesn't seem related to the claim that TPUs are less flexible. The raw functions it provides is not direct access to the hardware and memory subsystem. Not true. https://www.tensorflow.org/api_docs/python/tf/raw_ops/InplaceUpdate https://www.tensorflow.org/api_docs/python/tf/raw_ops/Inplac... Jax is also going to be giving even lower-level access than TF, which may interest 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. Well yeah, I care about achieving goals in my specific case, as you do yours. And simply getting together a VM that can feed 500 examples/sec to a set of GPUs is a massive undertaking in and of itself. TPUs make it more or less "easy" in comparison. (I won't say effortless, since it does take some effort to get yourself into the TPU programming mindset.)
- shaklee3 6y ago
  I gave you an example of something you can't do, which is an overlap-save FFT, and you ignored that completely. Please implement it, or show me any example of someone implementing any custom FFT that's not a simple, standard, batched FFT. I'll take any example of implementing any type of signal processing pipeline on TPU, such as a 5G radio. Your last sentence is pretty funny: a GPU can't do certain workloads because one it can do is too slow for you. Yet it remains a fact that TPU cannot do certain workloads without offloading to the CPU (making it orders of magnitude slower), and that's somehow okay? It seems where this discussion is going is you pointed to a TensorFlow library that may or may not offload to a TPU, and it probably doesn't. But even that library is incomplete to implement things like a 5G LDPC decoder.
  - sillysaurusx 6y ago
    Which part of this can't be done on TPUs? https://en.wikipedia.org/wiki/Overlap%E2%80%93save_method#Pseudocode https://en.wikipedia.org/wiki/Overlap%E2%80%93save_method#Ps... As far as I can tell, all of those operations can be done on TPUs. In fact, I linked to the operation list that shows they can be. You'll need to link me to some specific implementation that you want me to port over, not just namedrop some random algorithm. Got a link to a github? If your point is "There isn't a preexisting operation for overlap-save FFT" then... yes, sure, that's true. There's also not a preexisting operation for any of the hundreds of other algorithms that you'd like to do with signal processing. But they can all be implemented efficiently. Yet it remains a fact that TPU cannot do certain workloads without offloading to the CPU (making it orders of magnitude slower), and that's somehow okay? I think this is the crux of the issue: you're saying X can't be done, I'm saying X can be done, so please link to a specific code example. Emphasis on "specific" and "code".
    - shaklee3 6y ago
      Let's just leave this one alone then. I can't argue with someone who claims anything is possible, yet absolutely nobody seems to be doing what you're referring to (except you). A100 now tops all MLPerf benchmarks, and the unavailable TPUv4 may not even keep up. Trust me, I would love if TPUs could do what you're saying, but they simply can't. There's no direct DMA from the NIC to where I can do a streaming application at 40+Gbps to it. Even if TPU could do all the things you claim, if it's not as fast as the A100, what's the point? To go through undocumented pain to prove something?

sillysaurusx 6y ago

For one TensorFlow is not a generic framework like cuda is, so you lose a whole bunch of the configurability you have with cuda Why make generalizations like this? It's not true, and we've devolved back into the "nu uh" we originally started with. This is trivial to do on a GPU, and is built into the library Yes, I'm sure there are hardwired operations that are trivial to do on GPUs. That's not exactly a +1 in favor of generic programmability. There are also operations that are trivial to do on TPUs, such as CrossReplicaSum across a massive cluster of cores, or the various special-case Adam operations. This doesn't seem related to the claim that TPUs are less flexible. The raw functions it provides is not direct access to the hardware and memory subsystem. Not true. https://www.tensorflow.org/api_docs/python/tf/raw_ops/InplaceUpdate https://www.tensorflow.org/api_docs/python/tf/raw_ops/Inplac... Jax is also going to be giving even lower-level access than TF, which may interest 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. Well yeah, I care about achieving goals in my specific case, as you do yours. And simply getting together a VM that can feed 500 examples/sec to a set of GPUs is a massive undertaking in and of itself. TPUs make it more or less "easy" in comparison. (I won't say effortless, since it does take some effort to get yourself into the TPU programming mindset.)

shaklee3 6y ago
I gave you an example of something you can't do, which is an overlap-save FFT, and you ignored that completely. Please implement it, or show me any example of someone implementing any custom FFT that's not a simple, standard, batched FFT. I'll take any example of implementing any type of signal processing pipeline on TPU, such as a 5G radio. Your last sentence is pretty funny: a GPU can't do certain workloads because one it can do is too slow for you. Yet it remains a fact that TPU cannot do certain workloads without offloading to the CPU (making it orders of magnitude slower), and that's somehow okay? It seems where this discussion is going is you pointed to a TensorFlow library that may or may not offload to a TPU, and it probably doesn't. But even that library is incomplete to implement things like a 5G LDPC decoder.
- sillysaurusx 6y ago
  Which part of this can't be done on TPUs? https://en.wikipedia.org/wiki/Overlap%E2%80%93save_method#Pseudocode https://en.wikipedia.org/wiki/Overlap%E2%80%93save_method#Ps... As far as I can tell, all of those operations can be done on TPUs. In fact, I linked to the operation list that shows they can be. You'll need to link me to some specific implementation that you want me to port over, not just namedrop some random algorithm. Got a link to a github? If your point is "There isn't a preexisting operation for overlap-save FFT" then... yes, sure, that's true. There's also not a preexisting operation for any of the hundreds of other algorithms that you'd like to do with signal processing. But they can all be implemented efficiently. Yet it remains a fact that TPU cannot do certain workloads without offloading to the CPU (making it orders of magnitude slower), and that's somehow okay? I think this is the crux of the issue: you're saying X can't be done, I'm saying X can be done, so please link to a specific code example. Emphasis on "specific" and "code".
  - shaklee3 6y ago
    Let's just leave this one alone then. I can't argue with someone who claims anything is possible, yet absolutely nobody seems to be doing what you're referring to (except you). A100 now tops all MLPerf benchmarks, and the unavailable TPUv4 may not even keep up. Trust me, I would love if TPUs could do what you're saying, but they simply can't. There's no direct DMA from the NIC to where I can do a streaming application at 40+Gbps to it. Even if TPU could do all the things you claim, if it's not as fast as the A100, what's the point? To go through undocumented pain to prove something?