2 citations · 2 across the 4 of their papers we have counts for
4 papers
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
Srinivas Sridharan, Theodor-Adrian Badea, Andy Balogh +26
The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed machine learning~(ML) workload b…
Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training
Mingyu Liang, Hiwot Tadese Kassa, Wenyin Fu +3
Training LLMs in distributed environments presents significant challenges due to the complexity of model execution, deployment systems, and the vast space of configurable strategie…
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…
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…