272 citations · 2.5k across the 136 of their papers we have counts for
48 papers · 1 filter
Lincoln AI Computing Survey (LAICS) and Trends
Albert Reuther, Peter Michaleas, Michael Jones +2
In the past year, generative AI (GenAI) models have received a tremendous amount of attention, which in turn has increased attention to computing systems for training and inference…
Easy Acceleration with Distributed Arrays
Jeremy Kepner, Chansup Byun, LaToya Anderson +20
High level programming languages and GPU accelerators are powerful enablers for a wide range of applications. Achieving scalable vertical (within a compute node), horizontal (acros…
GPU Sharing with Triples Mode
Chansup Byun, Albert Reuther, LaToya Anderson +19
There is a tremendous amount of interest in AI/ML technologies due to the proliferation of generative AI applications such as ChatGPT. This trend has significantly increased demand…
Supercomputer 3D Digital Twin for User Focused Real-Time Monitoring
William Bergeron, Matthew Hubbell, Daniel Mojica +17
Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers…
HPC with Enhanced User Separation
Andrew Prout, Albert Reuther, Michael Houle +19
HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and ra…
LLM Inference Serving: Survey of Recent Advances and Opportunities
Baolin Li, Yankai Jiang, Vijay Gadepally +1
This survey offers a comprehensive overview of recent advancements in Large Language Model (LLM) serving systems, focusing on research since the year 2023. We specifically examine…
LLload: Simplifying Real-Time Job Monitoring for HPC Users
Chansup Byun, Julia Mullen, Albert Reuther +16
One of the more complex tasks for researchers using HPC systems is performance monitoring and tuning of their applications. Developing a practice of continuous performance improvem…
Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference
Baolin Li, Yankai Jiang, Vijay Gadepally +1
The rapid advancement of Generative Artificial Intelligence (GenAI) across diverse sectors raises significant environmental concerns, notably the carbon emissions from their cloud…
pPython Performance Study
Chansup Byun, William Arcand, David Bestor +17
pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics…
Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
Baolin Li, Rohan Basu Roy, Daniel Wang +3
The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which requires urgent intervention. In this work, we present a comprehensive analysis of the carbo…
Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service
Baolin Li, Siddharth Samsi, Vijay Gadepally +1
This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern…
KAIROS: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
Baolin Li, Siddharth Samsi, Vijay Gadepally +1
Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands. Despite their revenue-generation capability, these ser…
Python Implementation of the Dynamic Distributed Dimensional Data Model
Hayden Jananthan, Lauren Milechin, Michael Jones +17
Python has become a standard scientific computing language with fast-growing support of machine learning and data analysis modules, as well as an increasing usage of big data. The…
pPython for Parallel Python Programming
Chansup Byun, William Arcand, David Bestor +18
pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics…
RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
Baolin Li, Rohan Basu Roy, Tirthak Patel +3
Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces RIBBON, a novel deep learning inference serving system t…
MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning
Baolin Li, Tirthak Patel, Siddarth Samsi +2
GPU technology has been improving at an expedited pace in terms of size and performance, empowering HPC and AI/ML researchers to advance the scientific discovery process. However,…
3D Real-Time Supercomputer Monitoring
Bill Bergeron, Matthew Hubbell, Dylan Sequeira +17
Supercomputers are complex systems producing vast quantities of performance data from multiple sources and of varying types. Performance data from each of the thousands of nodes in…
Node-Based Job Scheduling for Large Scale Simulations of Short Running Jobs
Chansup Byun, William Arcand, David Bestor +15
Diverse workloads such as interactive supercomputing, big data analysis, and large-scale AI algorithm development, requires a high-performance scheduler. This paper presents a nove…
Vertical, Temporal, and Horizontal Scaling of Hierarchical Hypersparse GraphBLAS Matrices
Jeremy Kepner, Tim Davis, Chansup Byun +16
Hypersparse matrices are a powerful enabler for a variety of network, health, finance, and social applications. Hierarchical hypersparse GraphBLAS matrices enable rapid streaming u…
The MIT Supercloud Dataset
Siddharth Samsi, Matthew L Weiss, David Bestor +24
Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers a…
Survey of Machine Learning Accelerators
Albert Reuther, Peter Michaleas, Michael Jones +3
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 m…
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…
Fast Mapping onto Census Blocks
Jeremy Kepner, Andreas Kipf, Darren Engwirda +21
Pandemic measures such as social distancing and contact tracing can be enhanced by rapidly integrating dynamic location data and demographic data. Projecting billions of longitude…