Rust ML WG Meeting 00005

Meeting Info

Date: 20200623

Start time: 1600ET

Zoom (Meeting ID: 940 7467 1258)

Discuss collaborative library for doing ndarray-based convolution
- Seems like an extremely common use-case that most people would probably otherwise need to re-implement from scratch
- ocl-convolution does this with OpenCL-accelerated code, although examples for CPU-only aren't great
Updates on individual approaches and any new libraries
- Has anyone looked at the autograd crate? It's pretty impressive, although the examples aren't completely working at the moment (bug report has been filed)

Discussion about approaches for doing computation on GPU
- Low-level w/Vulkan vs higher-level OpenCL
- Pre-allocating space for all operations ahead of time can lead to benefits
- Generally, the overhead between passing information between GPU and CPU is fairly high
- cuBLAS and cuDNN performance considerations, with the work being done on accel
Discussion of high-level vs. low-level GPU API for linear algebra vs custom code approaches
- JWL is using Vulkan
- Performance differences between WebGPU and Vulkan based on command buffer allocation?
Saw autograd crate, which is a little more similar to Tensorflow
Interest in MKL operations and BLAS
- ndarray already has mkl (?)
Comments on cross-platform opportunities on WebGPU, but lack of documentation makes it a difficult consideration
TbF's library with matrix multiplication, which can move into new shaders
- https://github.com/tiberiusferreira/gpu_compute
Geordan has some existing convolutional work in the mli library