4 citations · 9 across the 5 of their papers we have counts for
6 papers
Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs
Haoyu Zhang, Arshad Rahman, Adnan Aziz +55
Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive history o…
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…
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Arya Tschand, Arun Tejusve Raghunath Rajan, Sachin Idgunji +23
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmark…
MTrainS: Improving DLRM training efficiency using heterogeneous memories
Hiwot Tadese Kassa, Paul Johnson, Jason Akers +7
Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires…
HetSched: Quality-of-Mission Aware Scheduling for Autonomous Vehicle SoCs
Aporva Amarnath, Subhankar Pal, Hiwot Kassa +6
Systems-on-Chips (SoCs) that power autonomous vehicles (AVs) must meet stringent performance and safety requirements prior to deployment. With increasing complexity in AV applicati…
STOMP: A Tool for Evaluation of Scheduling Policies in Heterogeneous Multi-Processors
Augusto Vega, Aporva Amarnath, John-David Wellman +6
The proliferation of heterogeneous chip multiprocessors in recent years has reached unprecedented levels. Traditional homogeneous platforms have shown fundamental limitations when…