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Found 33 papers, 5 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 Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data
A number of ML baselines for tornado detection are developed and compared, including a novel deep learning (DL) architecture capable of processing raw radar imagery without the need for manual feature extraction required for existing ML algorithms.
Lincoln AI Computing Survey (LAICS) Update
Finally, a brief description of each of the new accelerators that have been added in the survey this year is included.
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference
Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art.
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.
KAIROS: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands.
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%.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models
In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models.
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.
Bringing Atomistic Deep Learning to Prime Time
Artificial intelligence has not yet revolutionized the design of materials and molecules.
Scalable Geometric Deep Learning on Molecular Graphs
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing.
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is present.
AI Accelerator Survey and Trends
Over the past several years, new machine learning accelerators were being announced and released every month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications.
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.
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
To help address this problem, we introduce the Storm EVent ImagRy (SEVIR) dataset - a single, rich dataset that combines spatially and temporally aligned data from multiple sensors, along with baseline implementations of deep learning models and evaluation metrics, to accelerate new algorithmic innovations.
Ranked #6 on
Weather Forecasting
on SEVIR
Survey of Machine Learning Accelerators
New machine learning accelerators are being announced and released each month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications.
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.
Compute, Time and Energy Characterization of Encoder-Decoder Networks with Automatic Mixed Precision Training
In this paper we examine the compute, energy and time costs of training a UNet based deep neural network for the problem of predicting short term weather forecasts (called precipitation Nowcasting).
Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid
A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computational kernel execution on GPUs.
GraphChallenge.org Triangle Counting Performance
In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations.
Distributed, Parallel, and Cluster Computing Performance
Survey and Benchmarking of Machine Learning Accelerators
Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications.
Performance B.8; C.4
TapirXLA: Embedding Fork-Join Parallelism into the XLA Compiler in TensorFlow Using Tapir
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.
Distributed Deep Learning for Precipitation Nowcasting
Effective training of Deep Neural Networks requires massive amounts of data and compute.
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
Large Scale Organization and Inference of an Imagery Dataset for Public Safety
Video applications and analytics are routinely projected as a stressing and significant service of the Nationwide Public Safety Broadband Network.
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.
AI Enabling Technologies: A Survey
Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action.
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
Static Graph Challenge: Subgraph Isomorphism
The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
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
