78 citations · 177 across the 10 of their papers we have counts for
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cs.LG2023★ 2 cited
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
Srinivas Sridharan, Taekyung Heo, Louis Feng +8
Benchmarking and co-design are essential for driving optimizations and innovation around ML models, ML software, and next-generation hardware. Full workload benchmarks, e.g. MLPerf…
cs.DC2023★ 78 cited
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
William Won, Taekyung Heo, Saeed Rashidi +3
As deep learning models and input data are scaling at an unprecedented rate, it is inevitable to move towards distributed training platforms to fit the model and increase training…