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Found 12 papers, 0 papers with code
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
In this paper, we study the aggregate effect of power-capping GPUs on GPU temperature and power draw at a research supercomputing center.
A Green(er) World for A.I
applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more.
An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning
Our best performing models achieve a classification accuracy greater than 95%, outperforming previous approaches to multi-channel time series classification with the MIT SuperCloud Dataset by 5%.
The MIT Supercloud Workload Classification Challenge
We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches.
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains.
The MIT Supercloud Dataset
In this paper we introduce the MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations.
Accuracy and Performance Comparison of Video Action Recognition Approaches
Over the past few years, there has been significant interest in video action recognition systems and models.
Benchmarking network fabrics for data distributed training of deep neural networks
The large computational requirements for training deep models have necessitated the development of new methods for faster training.
Securing HPC using Federated Authentication
Federated authentication can drastically reduce the overhead of basic account maintenance while simultaneously improving overall system security.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
This work describes the design and performance optimization of an implementation of hierarchical associative arrays that reduces memory pressure and dramatically increases the update rate into an associative array.
A Billion Updates per Second Using 30,000 Hierarchical In-Memory D4M Databases
Streaming updates to a large associative array requires a hierarchical implementation to optimize the performance of the memory hierarchy.
Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Networking and Internet Architecture
Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor
Thus, the performance of these applications on KNL systems is of high interest to LLSC users and the broader data analysis and machine learning communities.
Performance Instrumentation and Methods for Astrophysics Distributed, Parallel, and Cluster Computing Computational Physics
