2 citations · 2 across the 4 of their papers we have counts for
5 papers
Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads
Ranganath R. Selagamsetty, Matthew Poremba, Bradford M. Beckmann +2
As distributed AI workloads grow in scale, multi-GPU systems have become essential for training large models. Although techniques like kernel fusion and overlapping communication w…
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…
Optimizing Distributed ML Communication with Fused Computation-Collective Operations
Kishore Punniyamurthy, Khaled Hamidouche, Bradford M. Beckmann
In order to satisfy their ever increasing capacity and compute requirements, machine learning models are distributed across multiple nodes using numerous parallelism strategies. As…
Predict; Do not React for Enabling Efficient Fine Grain DVFS in GPUs
Srikant Bharadwaj, Shomit Das, Kaushik Mazumdar +2
With the continuous improvement of on-chip integrated voltage regulators (IVRs) and fast, adaptive frequency control, dynamic voltage-frequency scaling (DVFS) transition times have…
Optimizing GPU Cache Policies for MI Workloads
Johnathan Alsop, Matthew D. Sinclair, Srikant Bharadwaj +8
In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Optimizing these workloads is important but complicated. As memor…