Malleable parallel computing with Ray: a runtime framework for dynamic resource management in iterative solvers
Yuan, Yue
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https://hdl.handle.net/2142/129273
Description
Title
Malleable parallel computing with Ray: a runtime framework for dynamic resource management in iterative solvers
Author(s)
Yuan, Yue
Issue Date
2025-04-29
Director of Research (if dissertation) or Advisor (if thesis)
Kindratenko, Volodymyr
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Malleable MPI
Ray
Cloud
Language
eng
Abstract
With the increasing demand for scalable parallel computing, frameworks such as MPI have been widely used for scientific research. However, emerging distributed computing frameworks such as Ray provide new opportunities for more flexible and dynamic resource management. This thesis presents an MPI-like framework implemented in Ray which provides one way to migrate the MPI applications to the Ray platform. Our framework features a dynamic process allocation mechanism that empowers process migration by allowing the application’s processes to expand or shrink. This thesis also implements the V-cycle multigrid method, a widely used solver for large-scale linear systems, to evaluate the performance and scalability of our framework. Our experiments demonstrate the trade-offs between static and dynamic resource allocation, highlighting the impact on computational efficiency. We show that while MPI provides strong performance guarantees under fixed workloads, Ray’s flexibility in rank adaptation can significantly improve resource utilization in dynamic environments. Our results provide valuable insights into the feasibility of using Ray as an alternative to MPI for large-scale scientific computing, particularly in scenarios where dynamic load balancing and resource elasticity are crucial.
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