This blog post talks about how to generate performant code for convolution ops using MLIR’s multiple levels of abstractions and transformations. I initially created it for targeting ARM Mali GPUs in IREE. But given it is just direct tiling and vectorization, it should be widely applicable. I will walk through the lowering steps, so if you are interested to know how to organize MLIR’s various dialects/patterns together to achieve similar tasks, this blog post might also be useful.
In a previous blog post I gave a general introduction to GPU driver internals in Android/Linux systems. Following up with it, today I will explain how a specific functionality, hardware performance counter (perf counter) queries, is handled in both Qualcomm Adreno and ARM Mali drivers, by walking through the kernel driver source code.
10 min read
Recently I have been working on a library that needs to directly interact with GPU kernel drivers from various vendors on Android/Linux systems. Compared to various GPU APIs, information at this level is quite sparse; so it is not a straightforward task, to say the least, and ends up requiring me to piece multiple sources together to figure out the details. So I am logging these driver internals and resources down in case it can be useful to others that are interested in these low-level bits.
12 min read
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