Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library

TL;DR

This paper enhances the ChASE eigensolver library for multi-GPU systems by optimizing algorithms and leveraging NCCL, enabling efficient large-scale dense eigenproblem solutions on supercomputers with thousands of GPUs.

Contribution

The authors redesigned ChASE with new memory layout, parallelization, communication-avoiding algorithms, and NCCL integration, significantly improving scalability and performance for large dense eigenproblems.

Findings

Supports problem sizes up to 10^6

Scales to 900 GPU nodes

Achieves efficient computation on supercomputers

Abstract

As supercomputers become larger with powerful Graphics Processing Unit (GPU), traditional direct eigensolvers struggle to keep up with the hardware evolution and scale efficiently due to communication and synchronization demands. Conversely, subspace eigensolvers, like the Chebyshev Accelerated Subspace Eigensolver (ChASE), have a simpler structure and can overcome communication and synchronization bottlenecks. ChASE is a modern subspace eigensolver that uses Chebyshev polynomials to accelerate the computation of extremal eigenpairs of dense Hermitian eigenproblems. In this work we show how we have modified ChASE by rethinking its memory layout, introducing a novel parallelization scheme, switching to a more performing communication-avoiding algorithm for one of its inner modules, and substituting the MPI library by the vendor-optimized NCCL library. The resulting library can tackle dense problems with size up to $N=\mathcal{O}(10^6)$, and scales effortlessly up to the full 900 nodes -- each one powered by 4$\times$A100 NVIDIA GPUs -- of the JUWELS Booster hosted at the J\"ulich Supercomputing Centre.