1 citations · 1 across the 5 of their papers we have counts for
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ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
William Won, Jinsun Yoo, Tuan Ta +16
Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications. As model inference arises as an important use case, faithful model…
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…
Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML
Jinsun Yoo, Meghan Cowan, Zheng Du +3
Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across t…
Towards a Standardized Representation for Deep Learning Collective Algorithms
Jinsun Yoo, William Won, Meghan Cowan +4
The explosion of machine learning model size has led to its execution on distributed clusters at a very large scale. Many works have tried to optimize the process of producing coll…