148 citations · 533 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
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…
Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks
Mingyu Liang, Wenyin Fu, Louis Feng +5
Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role…