Deep Learning

Profiling and Debugging GPU-accelerated AI Applications

Presented our research on debugging and profiling of GPU-accelerated AI applications.

Oct 24, 2024 9:41 PM — 9:41 PM Virtual

Presented a talk about Triton and called for contributions to improving the language

Aug 13, 2024 10:56 PM — 10:56 PM Lake Tahoe, California

Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor

For an extended period, graphics processing units (GPUs) have stood as the exclusive choice for training deep neural network (DNN) …

Zhen Xie, Murali Emani, Xiaodong Yu, Dingwen Tao, Xin He, Pengfei Su, Keren Zhou, Venkatram Vishwanath

PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

This paper introduces two extensions to the popular PyTorch machine learning framework, TorchDynamo and TorchInductor, which implement …

Jason Ansel, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael Lazos, Mario Lezcano, Yanbo Liang, Jason Liang, Yinghai Lu, C. K. Luk, Bert Maher, Yunjie Pan, Christian Puhrsch, Matthias Reso, Mark Saroufim, Marcos Yukio Siraichi, Helen Suk, Shunting Zhang, Michael Suo, Phil Tillet, Xu Zhao, Eikan Wang, Keren Zhou, Richard Zou, Xiaodong Wang, Ajit Mathews, William Wen, Gregory Chanan, Peng Wu, Soumith Chintala

Technical Review on PyTorch 2.0 and Triton

High-level overview of PyTorch 2.0 and Triton integration

Aug 7, 2023 10:03 PM — 10:03 PM Virtual

Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing

Advancements in deep learning are often associated with increasing model sizes. Training and deploying large models require …

Aditya Desai, Keren Zhou, Anshumali Shrivastava

Towards Agile Development of Efficient Deep Learning Operators (Hardware Insights)

Presented a talk about Triton and requested feedback from Intel engineers

Jun 29, 2023 10:56 PM — 10:56 PM Virtual

Towards Agile Development of Efficient Deep Learning Operators (Call for Contributions)

Presented a talk about Triton and called for contributions to improving the language

Jun 19, 2023 10:56 PM — 10:56 PM Lake Tahoe, California

Paw-Net: Stacking Ensemble Deep Learning for Segmenting Scanning Electron Microscopy Images of Fine-grained Shale Samples

Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to …

Binqian Yin, Qinhong Hu, Yingying Zhu, Chen Zhao, Keren Zhou

A Performance Analysis Framework for Exploiting GPU Microarchitectural Capability

Presented our work on static performance analysis for GPUs at ICS17

Jul 20, 2017 9:36 PM — 9:36 PM Chicago, IL, USA