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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.
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Given a set of candidate entities (e. g. movie titles), the ability to identify similar entities is a core capability of many recommender systems.
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.
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.
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.
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.
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.
