11 papers
TX-Digital Twin: Visualizing Supercomputer GPU Performance Data Stream
Elena Baskakova, William Bergeron, Matthew Hubbell +2
Supercomputers are complex, dynamic systems that serve thousands of users and are built with thousands of compute nodes. Due to the vast amounts of system and performance data need…
Improving the Graph Challenge Reference Implementation
Inna Voloshchuk, Hayden Jananthan, Chansup Byun +1
The MIT/IEEE/Amazon Graph Challenge provides a venue for individuals and teams to showcase new innovations in large-scale graph and sparse data analysis. The Anonymized Network Sen…
Complexity of One-Dimensional ReLU DNNs
Jonathan Kogan, Hayden Jananthan, Jeremy Kepner
We study the expressivity of one-dimensional (1D) ReLU deep neural networks through the lens of their linear regions. For randomly initialized, fully connected 1D ReLU networks (He…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
Performance and Numerical Aspects of Decompositional Factorizations with FP64 Floating-Point Emulation in INT8
Piotr Luszczek, Vijay Gadepally, LaToya Anderson +18
Mixing precisions for performance has been an ongoing trend as the modern hardware accelerators started including new, and mostly lower-precision, data formats. The advantage of us…
GraphBLAS Mathematical Opportunities: Parallel Hypersparse, Matrix Based Graph Streaming, and Complex-Index Matrices
Hayden Jananthan, Jeremy Kepner, Michael Jones +5
The GraphBLAS high performance library standard has yielded capabilities beyond enabling graph algorithms to be readily expressed in the language of linear algebra. These GraphBLAS…