Graph analytics techniques based on spectral methods process extremely large sparse matrices with millions or even billions of non-zero values. Behind these algorithms lies the Top-K sparse eigenproblem, the computation of the largest eigenvalues and their associated eigenvectors. In this work, we leverage GPUs to scale the Top-K sparse eigenproblem to bigger matrices than previously achieved while also providing state-of-the-art execution times. We can transparently partition the computation across multiple GPUs, process out-of-core matrices, and tune precision and execution time using mixed-precision floating-point arithmetic. Overall, we are 67 times faster than the highly optimized ARPACK library running on a 104-thread CPU and 1.9 times than a recent FPGA hardware design. We also determine how mixed-precision floating-point arithmetic improves execution time by 50% over double-precision, and is 12 times more accurate than single-precision floating-point arithmetic.
@article{arxiv.2201.07498,
title = {A Mixed Precision, Multi-GPU Design for Large-scale Top-K Sparse Eigenproblems},
author = {Francesco Sgherzi and Alberto Parravicini and Marco Domenico Santambrogio},
journal= {arXiv preprint arXiv:2201.07498},
year = {2022}
}