25079

Using Workload Characterization to Guide High Performance Graph Processing

Mohamed W. Hassan
Virginia Tech
Virginia Tech, 2021

@phdthesis{hassan2021using,

   title={Using Workload Characterization to Guide High Performance Graph Processing},

   author={Hassan, Mohamed Wasfy Abdelfattah},

   year={2021},

   school={Virginia Tech}

}

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Graph analytics represent an important application domain widely used in many fields such as web graphs, social networks, and Bayesian networks. The sheer size of the graph data sets combined with the irregular nature of the underlying problem pose a significant challenge for performance, scalability, and power efficiency of graph processing. With the exponential growth of the size of graph datasets, there is an ever-growing need for faster more power efficient graph solvers. The computational needs of graph processing can take advantage of the FPGAs’ power efficiency and customizable architecture paired with CPUs’ general purpose processing power and sophisticated cache policies. CPU-FPGA hybrid systems have the potential for supporting performant and scalable graph solvers if both devices can work coherently to make up for each other’s deficits. This study aims to optimize graph processing on heterogeneous systems through interdisciplinary research that would impact both the graph processing community, and the FPGA/heterogeneous computing community. On one hand, this research explores how to harness the computational power of FPGAs and how to cooperatively work in a CPUFPGA hybrid system. On the other hand, graph applications have a data-driven execution profile; hence, this study explores how to take advantage of information about the graph input properties to optimize the performance of graph solvers. The introduction of High Level Synthesis (HLS) tools allowed FPGAs to be accessible to the masses but they are yet to be performant and efficient, especially in the case of irregular graph applications. Therefore, this dissertation proposes automated frameworks to help integrate FPGAs into mainstream computing. This is achieved by first exploring the optimization space of HLS-FPGA designs, then devising a domain-specific performance model that is used to build an automated framework to guide the optimization process. Moreover, the architectural strengths of both CPUs and FPGAs are exploited to maximize graph processing performance via an automated framework for workload distribution on the available hardware resources.
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