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Characterizing Performance of Graph Neighborhood Communication Patterns.

, , and . IEEE Trans. Parallel Distributed Syst., 33 (4): 915-928 (2022)

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Characterizing Performance of Graph Neighborhood Communication Patterns., , and . IEEE Trans. Parallel Distributed Syst., 33 (4): 915-928 (2022)Scalable fine-grained call path tracing., , , , and . ICS, page 63-74. ACM, (2011)Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU., , , , , and . IPDPS, page 515-525. IEEE, (2022)Generating Performance Models for Irregular Applications., , , , and . IPDPS, page 317-326. IEEE Computer Society, (2017)Diolkos: improving ethernet throughput through dynamic port selection., , , , , and . CF, page 83-92. ACM, (2021)Modeling the Impact of Silicon Photonics on Graph Analytics., , , , , , , and . NAS, page 1-11. IEEE Computer Society, (2016)Optimizing Distributed Data-Intensive Workflows., , , , and . CLUSTER, page 279-289. IEEE Computer Society, (2018)MemGaze: Rapid and Effective Load-Level Memory Trace Analysis., , , , , and . CLUSTER, page 484-495. IEEE, (2022)Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing., , , , and . Future Gener. Comput. Syst., (2020)HPCVIEW: A Tool for Top-down Analysis of Node Performance., , , and . J. Supercomput., 23 (1): 81-104 (2002)