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Found 42 papers, 6 papers with code
LLM Inference Serving: Survey of Recent Advances and Opportunities
This survey offers a comprehensive overview of recent advancements in Large Language Model (LLM) serving systems, focusing on research since the year 2023.
Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference
The rapid advancement of Generative Artificial Intelligence (GenAI) across diverse sectors raises significant environmental concerns, notably the carbon emissions from their cloud and high performance computing (HPC) infrastructure.
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.
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%.
RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
Deep learning model inference is a key service in many businesses and scientific discovery processes.
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.
FastFlows: Flow-Based Models for Molecular Graph Generation
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
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.
Maneuver Identification Challenge
The Maneuver Identification Challenge hosted at maneuver-id. mit. edu provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions of maneuvers, and examples of these maneuvers performed by experienced pilots.
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.
Mathematics of Digital Hyperspace
The GraphBLAS standard currently supports hypergraphs, hypersparse matrices, the mathematics required for semilinks, and seamlessly performs graph, network, and matrix operations.
Technical Report on Data Integration and Preparation
In this report, we highlight a number of tools that can be used to simplify data integration and preparation steps.
Video Action Understanding
Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding.
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.
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 Sparse Deep Neural Network Performance
The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems.
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 of Attacks and Defenses on Edge-Deployed Neural Networks
Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons.
Sparse Deep Neural Network Graph Challenge
The Sparse DNN Challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment.
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
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.
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
TabulaROSA: Tabular Operating System Architecture for Massively Parallel Heterogeneous Compute Engines
In this context, an operating system can be viewed as software that brokers and tracks the resources of the compute engines and is akin to a database management system.
Distributed, Parallel, and Cluster Computing Databases Operating Systems Performance
Sparse Deep Neural Network Exact Solutions
This work uses associative array DNNs to construct exact solutions and corresponding perturbation models to the rectified linear unit (ReLU) DNN equations that can be used to construct test vectors for sparse DNN implementations over various precisions.
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
A Framework for Estimating Long Term Driver Behavior
The long term driver behavior estimation system involves an extended HSS+HMM structure that is capable of including external information in the estimation process.
Large Enforced Sparse Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) is a common method for generating topic models from text data.
