Rmlx: R Interface to Apple's MLX Arrays

This package provides an R interface to Apple's MLX (Machine Learning eXchange) library for GPU-accelerated array operations on Apple Silicon.

Key Features

• Lazy evaluation: Operations are not computed until explicitly evaluated

• GPU acceleration: Leverage Metal on Apple Silicon

• Familiar syntax: S3 methods for standard R operations

• Unified memory: Efficient data sharing between CPU and GPU

Lazy Evaluation

MLX arrays use lazy evaluation by default. Operations are recorded but not executed until:

• You call mlx_eval()

• You convert to R with e.g. as.array() or as.vector()

• The result is needed for another computation

The package implements most of the C++ API via calls with the mlx_ prefix, but it also ships S3 methods for many base generics, so common R matrix operations continue to work on MLX arrays. R conventions are used throughout: for example, indexing is 1-based.

See also

Useful links:

• https://hughjonesd.github.io/Rmlx/

• https://github.com/hughjonesd/Rmlx

• Report bugs at https://github.com/hughjonesd/Rmlx/issues

Author

Maintainer: David Hugh-Jones david@hughjones.com

Other contributors:

• Apple Inc. (MLX library downloaded at install time) [copyright holder]