Optimizing Geophysical Workloads in High-Performance Computing: Leveraging Machine Learning and Transformer Models for Enhanced Parallelism and Processor Allocation-Reference-Cited by-同舟云学术

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Optimizing Geophysical Workloads in High-Performance Computing: Leveraging Machine Learning and Transformer Models for Enhanced Parallelism and Processor Allocation

Published:2024-05-14 Issue: Volume:abs/1805.11877 Page:1-14
ISSN:
Container-title:2024 Third International Conference on Distributed Computing and High Performance Computing (DCHPC)
language:
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Author:

Wyzykowski André Brasil Vieira¹,Sousa Gabriel Mascarenhas Costa De¹,Coelho Breno Spinelli¹,Santos Lucas Batista¹,Anschau Darlan¹

Affiliation:

1. High Performance Computing Center Senai Cimatec,Salvador,Brazil

Funder

SENAI CIMATEC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10454047/10454024/10454084.pdf?arnumber=10454084

Reference29 articles.

1. An Approach to Performance Prediction for Parallel Applications

2. HPC Cloud for Scientific and Business Applications

3. Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters

4. Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions

5. CloudAdvisor: A Recommendation-as-a-Service Platform for Cloud Configuration and Pricing

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