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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)QuaL2 M: Learning Quantitative Performance of Latency-Sensitive Code., , , , , , and . IPDPS Workshops, page 913-923. IEEE, (2022)Evaluating On-Node GPU Interconnects for Deep Learning Workloads., , , , and . PMBS@SC, volume 10724 of Lecture Notes in Computer Science, page 3-21. Springer, (2017)Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows., , , and . IEEE BigData, page 3905-3914. IEEE, (2018)Towards efficient scheduling of data intensive high energy physics workflows., , , , , and . WORKS@SC, page 3:1-3:9. ACM, (2015)Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation., , , , and . IISWC, page 240-251. IEEE, (2020)Assessing Advanced Technology in CENATE., , , , , , , , and . NAS, page 1-2. IEEE Computer Society, (2016)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)