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- W4386711145 abstract "Recent advances in graph processing on FPGAs promise to alleviate performance bottlenecks with irregular memory access patterns. Such bottlenecks challenge performance for a growing number of important application areas like machine learning and data analytics. While FPGAs denote a promising solution through flexible memory hierarchies and massive parallelism, we argue that current graph processing accelerators either use the off-chip memory bandwidth inefficiently or do not scale well across memory channels. In this work, we propose GraphScale, a scalable graph processing framework for FPGAs. GraphScale combines multi-channel memory with asynchronous graph processing (i. e., for fast convergence on results) and a compressed graph representation (i. e., for efficient usage of memory bandwidth and reduced memory footprint). GraphScale solves common graph problems like breadth-first search, PageRank, and weakly-connected components through modular user-defined functions, a novel two-dimensional partitioning scheme, and a high-performance two-level crossbar design. Additionally, we extend GraphScale to scale to modern high-bandwidth memory (HBM) and reduce partitioning overhead of large graphs with binary packing." @default.
- W4386711145 created "2023-09-14" @default.
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- W4386711145 date "2023-09-13" @default.
- W4386711145 modified "2023-10-18" @default.
- W4386711145 title "GraphScale: Scalable Processing on FPGAs for HBM and Large Graphs" @default.
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- W4386711145 doi "https://doi.org/10.1145/3616497" @default.
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