Design Method of Heterogeneous Reconfigurable Graph Computing Accelerator System Based on FPGA

Abstract

The invention discloses a design method of an FPGA-based heterogeneous reconfigurable graph computing accelerator system. The entire accelerator system includes two heterogeneous modules, PC and FPGA. Open the PCIe DMA and other devices; select the graph computing accelerator engine according to the number of vertices and edges of the graph data to be processed; preprocess the graph data after selecting the accelerator engine; pass the preprocessed graph data through PCIe DMA transfers to the onboard DDR of the FPGA development board; the startup accelerator starts to read the graph data from the address specified by the onboard DDR; the controller distributes the graph data to different processing units for processing and calculation; after each processing unit is calculated and processed After the data is collected, the result is sent to the calculation result collection module; the collection module writes the result back to the onboard DDR, and after the entire graph data is processed, the PC reads the result from the onboard DDR. The invention has the characteristics of high performance, high energy efficiency, low power consumption and the like.

Description

technical field [0001] The invention relates to the field of computer hardware acceleration, in particular to an FPGA-based graph computing accelerator system design method. Background technique [0002] In the real world, a graph can be used to represent the relationship between different entities. It is an abstraction of the relationship model between entities. A lot of information can be stored in the graph structure, so it has a wide range of applications in practice, such as: social network Analysis, web page map search, product recommendation system establishment, traffic network analysis, and biomedical information analysis, etc. In today's era of big data, the scale of graphs is becoming larger and larger. For example, the number of Facebook users reached 2.2 billion in July 2014, and the number of relationships between users reached hundreds or even hundreds of billions. If the relationship between these users Stored in the form of graph edges, the storage capacity...

AI Technical Summary

Problems solved by technology

In the design and implementation of graph computing systems at the software level, there are often some inefficient and unavoidable problems at the software level, such as low utilization of off-chip bandwidth and low effective computing rate.

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