anvl - Accelerated Array Computing and Automatic Differentiation
Accelerated array computing and code transformations for R. Numerical programs operating on multi-dimensional arrays can be just-in-time compiled to optimized executables via 'XLA' -- the same compiler that powers 'JAX' and 'TensorFlow' -- and run on CPU or NVIDIA GPU from the same source. Also provides reverse-mode automatic differentiation, returning the gradient of a function as another R function.
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array-computingautodiffgpu-accelerationxla
8.02 score 64 stars 20 scriptsstablehlo - Write stableHLO programs
The package offers a low level interface to create stableHLO programs. These programs can be compiled and run on different hardware backends (CPU, GPU, ...) using the 'pjrt' package.
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deep-learninggpustablehloxla
4.98 score 8 stars 1 dependents 10 scriptspjrt - R Interface to PJRT
Provides an R interface to PJRT (Pluggable Jit RunTime), which allows you to run XLA or stableHLO programs on a variety of hardware backends including CPU, GPU, and TPU.
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deep-learninggpuxlaprotobufopenblascpp
4.67 score 8 stars 1 dependents 8 scriptstengen - Tensor Generics
Generic functions and common objects for working with tensors.
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4.08 score 3 stars 3 dependents 1 scriptsxlamisc - Helper Functions for the XLA Ecosystem
Helper functions for the XLA ecosystem.
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3.52 score 2 dependents 1 scripts