Rust Learning Project: Building a Neural Network

I recently read a few chapters of The Rust Programming Language and wanted to put my newly gained knowledge to use. Naturally, the first thing that came to mind was building a neural network in Rust. I’d already tried this in Python and failed, so I figured maybe, for some reason, it would go better this time around.

Matmul

The first step was implementing matrices and their operations in Rust, the most important of course being matrix multiplication. Since this would likely become the biggest bottleneck for the whole model, I got curious about its performance and benchmarked multiplying two 1024x1024 matrices.

The naive implementation took about 20 seconds. One simple optimization brought that down to 14 seconds.

Then I built with compiler optimizations enabled, and the naive implementation suddenly ran in 63 nanoseconds. I was floored, briefly convinced I’d stumbled onto some incredible compiler magic, before realizing what actually happened: the compiler had simply skipped the calculation entirely, since the result was never used. Classic dead code elimination.

After forcing the compiler to keep the computation, the naive implementation came in at a more believable 2 seconds, and the optimized version dropped to 200ms.

For comparison, Numpy does the same multiplication in about 4ms. Whether 200ms is fast enough for training neural nets in reasonable time remains to be seen.