Optimizing Matrix Multiplication for Swift in LLM Training

The author is working on improving the speed of a handwritten matrix multiplication code in Swift for training a Large Language Model (LLM).

The focus is on optimizing mathematical operations in Swift, particularly matrix multiplication, to achieve performance comparable to C implementations.

The initial Swift implementation was significantly slower than the C version, prompting the author to explore various optimization strategies, including using MutableSpan and Relaxed operations from Swift-Numerics.

The author notes that while some optimizations improved performance, the Swift implementation still lags behind C, leading to further investigations into loop unrolling and compiler optimization flags.

Future articles in the series will cover additional frameworks for machine learning on Apple Silicon and provide insights into training neural networks in Swift.