Summary Demystifying Memory Access Patterns of FPGA-Based Graph Processing Accelerators arxiv.org
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One Line
Advancements in reprogrammable hardware and memory technology can enhance graph processing performance, but comparing accelerators is difficult due to limited open source implementations and varying efforts.
Slides
Slide Presentation (8 slides)
Key Points
- Advances in reprogrammable hardware and memory technology can address performance challenges in graph processing.
- Limited open source implementations and varying implementation efforts make it difficult to assess and compare the performance of different graph accelerators.
- Different FPGA-based graph processing accelerators have different memory access patterns, such as AccuGraph, HitGraph, ForeGraph, and ThunderGP.
- Performance of FPGA-based graph processing accelerators is influenced by factors like graph size, density, and degree distribution skewness.
- Comparing the performance of different graph processing systems can be done using a DRAM-based simulation environment.
Summaries
25 word summary
Advancements in reprogrammable hardware and memory technology can improve graph processing performance. Comparing accelerators is challenging due to limited open source implementations and varying efforts.
42 word summary
Advances in reprogrammable hardware and memory technology have the potential to address performance challenges in graph processing. However, comparing the performance of different graph accelerators is difficult due to limited open source implementations and varying implementation efforts. The article discusses various approaches
438 word summary
Advances in reprogrammable hardware and memory technology have the potential to address performance challenges in graph processing. However, it is difficult to assess and compare the performance of different graph accelerators due to limited open source implementations and varying implementation efforts. In
This article discusses various approaches to graph processing accelerators, including AccuGraph, HitGraph, ForeGraph, and ThunderGP, each with different memory access patterns. The authors make several contributions, including a classification of existing graph processing accelerators, a
The text excerpt discusses various aspects of memory access patterns in FPGA-based graph processing accelerators. It begins by mentioning that request type modeling is straightforward, as it is clear when requests read and write data. Request addressing assumes that different data structures are adjacent in
AccuGraph proposes a flexible accumulator for FPGA-based graph processing accelerators. The controller triggers iterations over the graph, processing all partitions. The prefetch request producer fetches vertex values sequentially and merges adjacent requests into one. Values and pointers of destination vertices are
The HitGraph accelerator uses a controller to trigger edge-centric processing iterations until there are no value changes. In each iteration, partitions are scheduled for the scatter phase (producing updates) and then the gather phase (applying updates). Partitions are assigned
This summary examines the memory access patterns of FPGA-based graph processing accelerators. The document compares different accelerators based on their design decisions, graph problems, data set characteristics, memory technology, and memory access optimizations. The selection of graph data sets used for
The performance of FPGA-based graph processing accelerators is affected by various factors such as graph size, density, and degree distribution skewness. AccuGraph and ForeGraph show decreased performance compared to HitGraph and ThunderGP for large graphs due to the need
The excerpt discusses the performance and memory access patterns of four FPGA-based graph processing accelerators: AccuGraph, HitGraph, ForeGraph, and ThunderGP. The performance improvements vary for each accelerator when increasing the number of channels, with HitGraph showing
This excerpt discusses the performance of different graph processing systems, specifically focusing on the comparability of graph processing performance. The authors extend a DRAM-based simulation environment and compare the performance of four well-known graph accelerators: AccuGraph, ForeGraph,
This text excerpt includes a list of references to various research papers and publications related to graph processing accelerators and memory access patterns. The references cover a range of topics including non-relational databases on FPGAs, workload and DRAM interactions, high-performance
The summary is organized into separate paragraphs to distinguish distinct ideas and maintain the original order of the ideas presented in the excerpt.
Several studies have been conducted on efficient FPGA-based graph processing, including works by Yang et al. [24], Yao et al.