GraphMMU: Memory Management Unit for Sparse Graph Accelerators

Nachiket Kapre, Han Jianglei, Andrew Bean, P. Moorthy, Siddhartha
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引用次数: 5

Abstract

Memory management units that use low-level AXI descriptor chains to hold irregular graph-oriented access sequences can help improve DRAM memory throughput of graph algorithms by almost an order of magnitude. For the Xilinx Zed board, we explore and compare the memory throughputs achievable when using (1) cache-enabled CPUs with an OS, (2) cache-enabled CPUs running bare metal code, (2) CPU-based control of FPGA-based AXI DMAs, and finally (3) local FPGA-based control of AXI DMA transfers. For short-burst irregular traffic generated from sparse graph access patterns, we observe a performance penalty of almost 10× due to DRAM row activations when compared to cache-friendly sequential access. When using an AXI DMA engine configured in FPGA logic and programmed in AXI register mode from the CPU, we can improve DRAM performance by as much as 2.4× over naïve random access on the CPU. In this mode, we use the host CPU to trigger DMA transfer by writing appropriate control information in the internal register of the DMA engine. We also encode the sparse graph access patterns as locally-stored BRAM-hosted AXI descriptor chains to drive the AXI DMA engines with minimal CPU involvement under Scatter Gather mode. In this configuration, we deliver an additional 3× speedup, for a cumulative throughput improvement of 7× over a CPU-based approach using caches while running an OS to manage irregular access.
GraphMMU:稀疏图形加速器的内存管理单元
使用低级AXI描述符链来保存不规则的面向图形的访问序列的内存管理单元可以帮助将图形算法的DRAM内存吞吐量提高几乎一个数量级。对于Xilinx Zed板,我们探索并比较了使用(1)支持缓存的cpu与操作系统,(2)支持缓存的cpu运行裸机代码,(2)基于cpu的基于fpga的AXI DMA控制,以及(3)基于本地fpga的AXI DMA传输控制时可实现的内存吞吐量。对于稀疏图访问模式生成的短突发不规则流量,我们观察到,与缓存友好的顺序访问相比,由于DRAM行激活,性能损失几乎是10倍。当使用在FPGA逻辑中配置并从CPU以AXI寄存器模式编程的AXI DMA引擎时,我们可以通过naïve对CPU的随机访问将DRAM性能提高多达2.4倍。在这种模式下,我们使用主机CPU通过在DMA引擎的内部寄存器中写入适当的控制信息来触发DMA传输。我们还将稀疏图访问模式编码为本地存储的bram托管的AXI描述符链,以便在Scatter Gather模式下以最小的CPU占用来驱动AXI DMA引擎。在此配置中,我们提供了额外的3倍加速,在运行操作系统管理不规则访问时,使用缓存的基于cpu的方法累计吞吐量提高了7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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