Benchmark computer architecture8/17/2023 ![]() "Exploring Core and Cache Hierarchy Bottlenecks in Graph Processing Workloads", Abanti Basak, Xing Hu, Shuangchen Li, Sang Min Oh, Yuan Xie, Computer Architecture Letters (CAL), 2018.Qureshi, International Symposium on Computer Architecture (ISCA), 2018. "ACCORD: Enabling Associativity for Gigascale DRAM Caches by Coordinating Way-Install and Way-Prediction", Vinson Young, Chia-Chen Chou, Aamer Jaleel, Moinuddin K.Thesis, Georgia Institute of Technology, 2018. "Near-data Processing for Dynamic Graph Analytics", Eric Robert Hein, Ph.D."Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling", Michael Sutton, Tal Ben-Nun, Amnon Barak, International Parallel & Distributed Processing Symposium (IPDPS), 2018.Qureshi, International Symposium on High Performance Computer Architecture (HPCA), 2018. "SYNERGY: Rethinking Secure-Memory Design for Error-Correcting Memories", Gururaj Saileshwar, Prashant Nair, Prakash Ramrakhyani, Wendy Elssaser and Moinuddin K."Julienne: A Framework for Parallel Graph Algorithms using Work-efficient Bucketing", Laxman Dhulipala, Guy Blelloch, and Julian Shun, Symposium on Parallelism in Algorithms and Architectures (SPAA), 2017."GoblinCore-64: A Scalable, Open Architecture for Data Intensive High Performance Computing", John D."Optimizing Indirect Memory References with milk", Vladimir Kiriansky, Yunming Zhang, and Saman Amarasinghe, International Conference on Parallel Architectures and Compilation (PACT), 2016.Burns, Ozcan Ozturk, International Symposium on Computer Architecture (ISCA), 2016. "Energy Efficient Architecture for Graph Analytics Accelerators", Mustafa Ozdal, Serif Yesil, Taemin Kim, Andrey Ayupov, John Greth, Steven M."Whirlpool: Improving Dynamic Cache Management with Static Data Classification", Anurag Mukkara, Nathan Beckmann, and Daniel Sanchez, Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2016.SPEC Kaivalya Dixit Distinguished Dissertation Award Thesis, University of California Berkeley, September 2016. "Understanding and Improving Graph Algorithm Performance", Scott Beamer, Ph.D."Locality Exists in Graph Processing: Workload Characterization on an Ivy Bridge Server", Scott Beamer, Krste Asanović, and David Patterson, International Symposium on Workload Characterization ( IISWC), Atlanta, October 2015.The GAP Benchmark Suite, Scott Beamer, Krste Asanović, and David Patterson, arXiv:1508.03619, 2015.Correct - verifiers to check kernel outputs.Portable - only requires C++11 & OpenMP (tested and supported on: x86/ARM/SPARC/RISC-V and gcc/icc/clang/suncc).Fast - matches or exceeds performance of other shared-memory frameworks.Evaluation best practices - timing methodologies, allowed optimizations.Graph Kernels - including what constitutes a correct solution.Input Graphs - large (billions of edges), real-world & synthetically-generated.Our benchmark was co-designed with our workload characterization, and it has undergone multiple revisions guided by community feedback. We provide both a benchmark specification to standardize the methodology and a high-performance reference implementation to be used as a baseline. Through standardization, we hope to not only make results easier to compare, but to also prevent common evaluation mistakes. We created the GAP Benchmark Suite to standardize evaluations in order to alleviate the methodological issues we observed. In order for the research community to make progress on accelerating graph processing, it is important to be able to properly and reliably compare results. These methodological issues make it difficult for good ideas to stand out and cloud the reasoning behind why these ideas are beneficial. ![]() Common serious mistakes we have observed include: using trivially small input graphs, using only a single input graph topology, or using low-performance implementations as baselines. The unfortunate lack of a widely used graph benchmark suite forces each research publication to create its own evaluation methodology, and this often results in mistakes or unnecessary differences. ![]() GAP Berkeley Benchmark Suite Scott Beamer ![]()
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