A Mixed Precision, Multi-GPU Design for Large-scale Top-K Sparse Eigenproblems

01/19/2022

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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 and is 12 times more accurate than single-precision floating-point arithmetic.

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A Mixed Precision, Multi-GPU Design for Large-scale Top-K Sparse Eigenproblems

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A Mixed Precision, Multi-GPU Design for Large-scale Top-K Sparse Eigenproblems

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