arXiv:2311.12180math.OC
cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia
Abstract
In this paper, we provide an affirmative answer to the long-standing question: Are GPUs useful in solving linear programming? We present cuPDLP.jl, a GPU implementation of restarted primal-dual hybrid gradient (PDHG) for solving linear programming (LP). We show that this prototype implementation in Julia has comparable numerical performance on standard LP benchmark sets to Gurobi, a highly optimized implementation of the simplex and interior-point methods. This demonstrates the power of using GPUs in linear programming, which, for the first time, showcases that GPUs and first-order methods can lead to performance comparable to state-of-the-art commercial optimization LP solvers on standard benchmark sets.
The three linked papers seem to be old, but the broader impact section mentioned cupdlp, which is more recent and has interesting numerical comparisons with commercial solvers: https://arxiv.org/abs/2311.12180, https://arxiv.org/pdf/2312.14832. It is CPU vs GPU, though, not sure…
optcoderView on HNParent context
Their numerical results with GPUs, compared to Gurobi, are quite impressive [1]. In my opinion (unless I'm missing something), the key benefits of their algorithms lie in the ability to leverage GPUs and the fact that there’s no need to store factorization in memory. However, if…
dsfcxvView on HNParent context