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• W3097576103 abstract "• We present an exhaustive and systematic energy characterization study of six GAP benchmark suite kernels with a variety of input graphs (9 synthetic, 3 real-world) on a dual-socket x86-based system. • We provide an analysis how the algorithms, graph characteristics (like graph scale and degree), and system effects (like parallelism, simultaneous multithreading, and using multiple processors) impact the energy. • Based on our analysis, we derive insights and develop a basic energy model that considers the graph and system characteristics as input features to fit the energy values. The discussions in the paper can enable researchers to advance new models and energy-efficient architectures for graph workloads. Graph algorithms are critical components of the big-data analysis workflow. The graph kernel performance is highly dependent on the input data graphs. The inherently sparse nature of the input graphs often results in irregular memory access patterns, which may not suit the data-locality based cache optimizations featured in current high-performance processors. Much prior research has identified several optimization opportunities by characterizing graph kernels on existing hardware. However, current graph workload characterization studies focus on performance-related observations and optimizations, overlooking the energy implications. In this paper, we address this technology gap by presenting an exhaustive and systematic energy characterization study of graph kernels. We characterize the six GAP benchmark suite kernels with a variety of input graphs on a dual-socket x86-based system. We then analyze how the algorithms, graph characteristics (like graph scale and degree), and system effects (like parallelism, simultaneous multithreading, and multiprocessing) impact the energy. Based on our analysis, we derive observations and insights to develop a basic energy model. The discussions in the paper can enable researchers to advance new models and energy-efficient architectures for graph workloads." @default.
• W3097576103 created "2020-11-09" @default.
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• W3097576103 date "2021-03-01" @default.
• W3097576103 modified "2023-09-24" @default.
• W3097576103 title "Energy characterization of graph workloads" @default.
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• W3097576103 doi "https://doi.org/10.1016/j.suscom.2020.100465" @default.
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