WebFeb 19, 2024 · We compare ACTS against Gunrock, a state-of-the-art graph processing accelerator for the GPU, and GraphLily, a recent FPGA-based graph accelerator also utilizing HBM memory. Our results show a geometric mean speedup of 1.5X, with a maximum speedup of 4.6X over Gunrock, and a geometric speedup of 3.6X, with a … WebSparse matrix-vector multiplication (SpMV) multiplies a sparse matrix with a dense vector. SpMV plays a crucial role in many applications, from graph analytics to deep learning. The random memory accesses of the sparse matrix make accelerator design challenging. However, high bandwidth memory (HBM) based FPGAs are a good fit for designing …
GraphBLAS/GraphBLAS-Pointers - Github
WebFrom the evaluation of twelve large-size matrices, Serpens is 1.91x and 1.76x better in terms of geomean throughput than the latest accelerators GraphLiLy and Sextans, … WebNov 24, 2024 · From the evaluation of twelve large-size matrices, Serpens is 1.91x and 1.76x better in terms of geomean throughput than the latest accelerators GraphLiLy and Sextans, respectively. We also evaluate 2,519 SuiteSparse matrices, and Serpens achieves 2.10x higher throughput than a K80 GPU. flow x access
[2111.12555v1] Serpens: A High Bandwidth Memory Based Accelerator …
WebGraphLily effectively utilizes the high bandwidth of HBM to achieve high performance for memory-bound sparse kernels by co-designing the data layout and the accelerator … WebNov 24, 2024 · Sparse matrix-vector multiplication (SpMV) multiplies a sparse matrix with a dense vector. SpMV plays a crucial role in many applications, from graph analytics to … WebGraphBLAS and GraphChallenge Advance Network Frontiers by Jeremy Kepner, David A. Bader, Tim Davis, Roger Pearce, and Michael M. Wolf; Typesetting. The nicematrix LaTeX package can be used to typeset block matrices.. Example TeX code; Related work. graphblas-verif: Formal verification of the GraphBLAS C API implementation by Tim … flowx.ai logo