Search Results for author:
Found 8 papers, 2 papers with code
Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations
Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples.
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators
However, the comparative performance of these AI accelerators on large language models has not been previously studied.
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions.
Distributed Neural Representation for Reactive in situ Visualization
In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization.
BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
We describe how the task of rescaling suitable DNN training tasks to fit dynamically changing holes can be formulated as a deterministic mixed integer linear programming (MILP)-based resource allocation algorithm, and show that this MILP problem can be solved efficiently at run time.
Deep Reinforcement Agent for Scheduling in HPC
Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads.
