JuliaGPU/CUDA.jl

Support general eltypes in matrix factorizations (LU, QR, Cholesky)

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#1,458 opened on Mar 29, 2022

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Description

Matrices with eltypes other than T <: CublasFloat should be supported in matrix factorizations (LU, QR, Cholesky) by promotion to a supported eltype during the copy that the non-mutating functions have to make anyway. This already works in many cases by reusing non-mutating methods in Base.LinearAlgebra and only specializing the mutating methods; however, Base tends to promote to Float64 when cuda would be better served by Float32, so it would be better to systematically handle this within CUDA.CUSOLVER.

Already implemented for matrix division and SVD in #1453. Submitting this issue to make sure it's not forgotten for the other factorizations.

Contributor guide

Research direction
Review the implementation in PR #1453 for matrix division and SVD, and extend the pattern to LU, QR, and Cholesky factorizations.
Tech stack
None
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
JuliaGitLinear Algebra
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70