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Ramon Nou Castell

dblp:397/3299 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-0464-1473ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 61% Cloud and datacenter computing · 39%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
HPC system software
0.812024
Enhancing HPC with Serverless Computing: Lithops on MareNostrum5 · ICNP 2024
Cloud and datacenter computing
serverless computing
0.812024
Enhancing HPC with Serverless Computing: Lithops on MareNostrum5 · ICNP 2024
High-performance computing › distributed computing infrastructure
serverless HPC deployment
0.812024
Enhancing HPC with Serverless Computing: Lithops on MareNostrum5 · ICNP 2024

Methods — techniques the papers use, named apart from their topics

benchmarking · 0.8
YearPublicationVenuePosition
2024 Enhancing HPC with Serverless Computing: Lithops on MareNostrum5
abstract
Serverless computing offers a novel alternative for developing and deploying applications. By abstracting backend architecture from the user, developers are encouraged to write code without worrying about server management, scaling, or maintenance. The Function-as-a-Service (FaaS) model optimizes this by allowing developers to deploy discrete functions that scale automatically in response to demand without deep concerns about the scalability of the execution infrastructure or platform. Lithops, a multi-cloud serverless computing framework, follows this trend and enables developers to execute Python code across thousands of cloud cores without modifying local scripts. Despite its potential to deploy parallel jobs, Lithops has been designed for running big data jobs over cloud environments, and its applicability to High-Performance Computing (HPC) systems has been unexplored. This paper introduces a novel architecture enabling Lithops deployment in HPC systems like the MareNostrum 5 supercomputer. By leveraging the immense computational power of the HPC-MN5 supercomputer and the FaaS model of Lithops, the architecture aims to offer high performance and scalability while simplifying application coding and deployment. Our evaluations display Lithops' benchmarks over the MareNostrum 5 HPC scale with the number of nodes, outperforming other commercial cloud platforms in terms of Floating Point Operations Per Second (FLOPS) and read-write bandwidth, and avoiding CPU wastage.
Andres Benavides Arevalo, Daniel Coll Tejeda, Aaron Call, Pedro García López, Ramon Nou Castell
ICNP5