Viewing profile — ozinenko

I'd be interested to learn about such closed-source important bits and invite them to MLIR workshop / open developer meeting. Having worked on the project essentially since its inc…

MLIR maintainer here, or however close one can be given that we don't have a clear ownership structure. This has been discussed repeatedly in the community, and it is likely that m…

There are very few reasons for a landlord to prematurely terminate the lease in France, and deciding to lent it for more money is specifically not one of them. In winter, you can't…

@chrislattner out of curiosity, can you share on which CPU the numbers in https://docs.modular.com/mojo/notebooks/Matmul.html are obtained and/or the fraction of peak performance o…

Engage with some open-source compiler community or, better, find a way to contribute and then the job will find you. Note, however, that even if you wanted to pursue a purely acade…

Cool, I've seen the PLDI talk a couple of years ago! Would you mind describing your potential use case on https://llvm.discourse.group/c/llvm-project/mlir , there may be more peopl…

(Early disclaimer: I am an author of the paper and of MLIR, but this is a personal opinion) I am somehow surprised by the reaction but at the same time I expected as much. We chose…

It will be as good as the sum of work put into it by the people in the ecosystem. That is the reason why MLIR was open-sourced very early in the development process, instead of thr…

Tensor Comprehensions is mostly targeted at arithmetics operations that appear in DL workloads, and the notation strives to be usable for DL experts. Polyhedral optimizer is orient…

Tensor Comprehensions does not try to own memory allocation and CPU/GPU transfers. ATen is one simple way of getting that, which we used for tests. Anything convertible to DLPack t…

Sure, it is one of the works we cite. It seems to be mostly targeted at sparse computations and does not have GPU support. Tensor Comprehensions does not try to manage memory and t…

Section 7 of the paper ( https://arxiv.org/abs/1802.04730 ) has a couple of examples. In short, yes CuDNN is fast for the cases it was tuned for . It is probably faster on power-of…

The paper has a couple of useful references. Otherwise, we have a site with general information on polyhedral compilation http://polyhedral.info/ and Halide has its own site http:/…

The crucial part is the polyhedral optimizer which does indeed include several GPU-specific heuristics (multilevel parallelization, coalescing, etc) and specialization to tensor si…