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Sparse Graph Processing with Soft-Processors

Authors: Nachiket Kapre;

Sparse Graph Processing with Soft-Processors

Abstract

Modern FPGAs can be configured to exploit the large amount of on chip parallelism possible from the distributed SRAM memory blocks for algorithms operating on large sparse graphs. To simplify the programming and configuration of such memory-centric organizations, we can customize an array of soft processors for these graph algorithms. In particular, we can deliver significant performance improvements for bulk synchronous graph algorithms with a custom processor that implements a graph-specific ISA. We develop a C++ API using Vivado High-Level Synthesis to describe graph computations and generate custom soft processors from these high-level descriptions. Our preliminary experiments suggest that our soft processor outperform Micro blaze and NIOS-II/f soft processors by a#x222B;6×. While not the focus of this work, this design can scale out to a cluster of 16 -- 32 low-power, energy-efficient Zed boards and Microzed boards to compete with server-class x86 nodes.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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