12 citations · 24 across the 6 of their papers we have counts for
6 papers
Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid
Andrew C. Kirby, Siddharth Samsi, Michael Jones +3
A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computatio…
Accuracy and Performance Comparison of Video Action Recognition Approaches
Matthew Hutchinson, Siddharth Samsi, William Arcand +16
Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance re…
Benchmarking network fabrics for data distributed training of deep neural networks
Siddharth Samsi, Andrew Prout, Michael Jones +16
Artificial Intelligence/Machine Learning applications require the training of complex models on large amounts of labelled data. The large computational requirements for training de…
Best of Both Worlds: High Performance Interactive and Batch Launching
Chansup Byun, Jeremy Kepner, William Arcand +16
Rapid launch of thousands of jobs is essential for effective interactive supercomputing, big data analysis, and AI algorithm development. Achieving thousands of launches per second…
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets
Jeremy Kepner, Chad Meiners, Chansup Byun +23
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network tr…
TapirXLA: Embedding Fork-Join Parallelism into the XLA Compiler in TensorFlow Using Tapir
Tao B. Schardl, Siddharth Samsi
This work introduces TapirXLA, a replacement for TensorFlow's XLA compiler that embeds recursive fork-join parallelism into XLA's low-level representation of code. Machine-learning…