Enhancing HPC with Serverless Computing: Lithops on MareNostrum5
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.
Authors
- Aaron Call (ORCID: https://orcid.org/0000-0002-2770-1662)
- Pedro López (ORCID: https://orcid.org/0000-0003-4544-955X)
- Andres Benavides Arevalo
- Daniel Coll Tejeda
- Ramon Nou Castell
Institutions
- Barcelona Supercomputing Center (ES)
- Universitat Rovira i Virgili (ES)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1109/icnp61940.2024.10858564
- Citations
- 2
- Primary Topic
- Distributed and Parallel Computing Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.70