no code implementations • 14 Oct 2022 • Sanghyun Son, Yi-Ling Qiao, Jason Sewall, Ming C. Lin
To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well.
no code implementations • 14 Aug 2018 • Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee
Deep learning is a promising tool to determine the physical model that describes our universe.
1 code implementation • 24 Oct 2017 • Jason Sewall, Simon J. Pennycook
We present a technique for automatically transforming kernel-based computations in disparate, nested loops into a fused, vectorized form that can reduce intermediate storage needs and lead to improved performance on contemporary hardware.
Performance Distributed, Parallel, and Cluster Computing
