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.
Today I would like to describe one way to build a scalable and frictionless benchmarking pipeline for Android native libraries, aiming to support different benchmark and device variants. It is for open source projects, so it composes public services, commonly free under such conditions. The ingredients are cloud virtual machines for building, local single board computers (e.g., Raspberry Pi) for hosting Android devices and executing benchmarks, a Dana server for keeping track of benchmark results of landed changes, and Python scripts for posting benchmark comparisons to pull requests. A Buildkite pipeline chains them together and drives the full flow.
Nowadays GPUs are utilized for both graphics rendering and general-purpose compute (GPGPU). For the latter, CUDA is the indisputable leading solution. Though, with so many other GPU vendors, the quest for a GPGPU standard never stops. OpenCL was a great attempt and is used widely; but still it falls short on many aspects. Given the success of Vulkan in graphics and it being both a graphics and compute API, one would wonder whether it can actually be the next-generation GPGPU standard. I certainly believe so; but the road is not full of roses.
These days if you would like to learn about machine learning, there are abundant great resources on the web discussing model architectures and how to code and train them. Materials about inference, though, are generally much harder to find, especially for edge and mobile. You might ask, inference is just the forward pass of training, so how hard can it be? Actually, it faces lots of unique challenges, to the extent that we are basically solving completely different major problems. I have been working on inference at the edge for a while, so let me capture them in this blog post, by contrasting training and inference in the cloud.
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.
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.