most citedForecasting GPU Performance for Deep Learning Training and Inference

30 citations · 30 across the 4 of their papers we have counts for

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cs.LG2025

EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence

Ansel Kaplan Erol, Seungjun Lee, Divya Mahajan

Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure monitoring. However, traditional p…

cs.LG2025

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants

Bozhi You, Irene Wang, Zelal Su Mustafaoglu +5

Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example, FlashAttention leverages tiling…

cs.LG2025

CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization

Irene Wang, Newsha Ardalani, Mostafa Elhoushi +6

Machine learning solutions are rapidly adopted to enable a variety of key use cases, from conversational AI assistants to scientific discovery. This growing adoption is expected to…

cs.LG2024

Integrated Hardware Architecture and Device Placement Search

Irene Wang, Jakub Tarnawski, Amar Phanishayee +1

Distributed execution of deep learning training involves a dynamic interplay between hardware accelerator architecture and device placement strategy. This is the first work to expl…

cs.LG2024★ 30 cited

Forecasting GPU Performance for Deep Learning Training and Inference

Seonho Lee, Amar Phanishayee, Divya Mahajan

Deep learning kernels exhibit predictable memory accesses and compute patterns, making GPUs' parallel architecture well-suited for their execution. Software and runtime systems for…