Platform for Research with GPUs

The project: Plataforma para la Investigación con GPUs (PIG) is an inter-institutional initiative by the Supercomputing Community within the Corporación Universitaria para el Desarrollo de Internet (CUDI) that aims to aggregate GPU infrastructure from various Mexican institutions into a single, container-based platform that allows for secure and easy resource sharing. PIG’s main objective is to provide students and researchers access to advanced computing tools while promoting collaboration and optimizing resource utilization. The platform enables users to experiment with different GPU models and capacities, offering an ideal environment for developing, debugging and testing parallel applications with GPU support. Once ready to scale up, users can then take their applications to HPC environments, thus making a better use of these resources. In its pilot phase, PIG has successfully deployed a Kubernetes cluster that gathers infrastructure from several institutions, including nodes from UNAM, UDG, and UAEMEX. With 10+ GPUs, 80+ CPUs, 250GB+ RAM and 5 TB of storage capacity PIG is ready to deliver tangible benefits. The next steps involve formalizing the project, expanding its capacity, and incorporating more institutions and users. We believe that PIG has the potential to become a strategic platform that strengthens Mexico’s HPC infrastructure by promoting inter-institution cooperation and facilitating the access to HPC-like resources to users, helping the development of highly skilled talent and promoting cutting-edge scientific research.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-02
DOI
https://doi.org/10.5281/zenodo.15320880
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
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article

Platform for Research with GPUs

Zenodo (CERN European Organization for Nuclear Research)
Distributed and Parallel Computing Systems
article

Platform for Research with GPUs

article en

Abstract

The project: Plataforma para la Investigación con GPUs (PIG) is an inter-institutional initiative by the Supercomputing Community within the Corporación Universitaria para el Desarrollo de Internet (CUDI) that aims to aggregate GPU infrastructure from various Mexican institutions into a single, container-based platform that allows for secure and easy resource sharing. PIG’s main objective is to provide students and researchers access to advanced computing tools while promoting collaboration and optimizing resource utilization. The platform enables users to experiment with different GPU models and capacities, offering an ideal environment for developing, debugging and testing parallel applications with GPU support. Once ready to scale up, users can then take their applications to HPC environments, thus making a better use of these resources. In its pilot phase, PIG has successfully deployed a Kubernetes cluster that gathers infrastructure from several institutions, including nodes from UNAM, UDG, and UAEMEX. With 10+ GPUs, 80+ CPUs, 250GB+ RAM and 5 TB of storage capacity PIG is ready to deliver tangible benefits. The next steps involve formalizing the project, expanding its capacity, and incorporating more institutions and users. We believe that PIG has the potential to become a strategic platform that strengthens Mexico’s HPC infrastructure by promoting inter-institution cooperation and facilitating the access to HPC-like resources to users, helping the development of highly skilled talent and promoting cutting-edge scientific research.

Zenodo (CERN European Organization for Nuclear Research)
Universidad de Guadalajara (MX), Autonomous University of San Luis Potosí (MX), University of California San Diego (US), Corporación para el Desarrollo Sostenible del Urabá (CO), Universidad Nacional Autónoma de México (MX)
Industry, innovation and infrastructure
Openalex Percentile: Top 60%
Distributed and Parallel Computing Systems
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Platform for Research with GPUs · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS