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Large-scale eigenvalue computations on sparse matrices are a key component ofgraph analytics techniques based on spectral methods. In such applications, anexhaustive computation of all eigenvalues and eigenvectors is impractical andunnecessary, as spectral methods can retrieve the relevant properties ofenormous graphs using just the eigenvectors associated with the Top-K largesteigenvalues.  In this work, we propose a hardware-optimized algorithm to approximate asolution to the Top-K eigenproblem on sparse matrices representing large graphtopologies. We prototype our algorithm through a custom FPGA hardware designthat exploits HBM, Systolic Architectures, and mixed-precision arithmetic. Weachieve a speedup of 6.22x compared to the highly optimized ARPACK libraryrunning on an 80-thread CPU, while keeping high accuracy and 49x better powerefficiency.

Solving Large Top-K Graph Eigenproblems with a Memory and Compute-optimized FPGA Design