Performance evaluation of a Ray-based MPI runtime for hybrid HPC-cloud systems

In this work, I present two contributions to the development and benchmarking of the Ray Collective Communication Library (CCL) and its integration with MPI workflows. First, I implemented examples demonstrating the adaptation of mpi4py code to Ray CCL backend. This includes translation and validation of critical mpi4py test cases, specifically focusing on fundamental operations such as send, receive, broadcast, and MPI Group functionalities. I demonstrated that the translated code produces identical outputs to the original mpi4py tests, ensuring reliability and correctness. The adaptation of mpi4py code to Ray CCL backend enables the migration of existing MPI applications to HPC cloud environments where mpi4py may not be suitable. By mapping MPI communication semantics to Ray CCL, we are able to fully leverage Ray’s advantages, including elastic scheduling, fault tolerance, resource-aware execution, and cloud-native support, making it possible to run traditionally MPI-bound HPC applications more effectively on the modern cloud infrastructure. Second, I designed and implemented a comprehensive benchmarking suite to evaluate the performance of the Ray-based MPI communication backend in comparison with mpi4py. This benchmarking framework provides valuable insights into the relative performance characteristics of both communication paradigms, particularly in hybrid HPC-Cloud scenarios where traditional MPI backends may face challenges. Through my validation work and comprehensive benchmarking analysis, I helped evaluate the feasibility of bridging traditional HPC communication patterns with cloud-native distributed computing frameworks. My testing contributions support the broader goal of enabling workload migration across diverse computing platforms.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Song, Yifei

Contributors dc:contributor

• Kindratenko, Volodymyr

• Ray
• MPI
• HPC

Statement dc:rights

• Copyright 2025 Yifei Song

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129588

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129588