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Communication-efficient algorithms for solving pressure Poisson equation for multiphase flows using parallel
Soumyadip Ghosh1, Jiacai Lu2, Vijay Gupta3
1Intel Corporation, University of Notre Dame, Notre Dame, IN, United States of America.
This study introduces new parallel algorithms for solving partial differential equations, reducing synchronization and communication overhead. These methods improve both time and energy efficiency in complex simulations.
Area of Science:
- Computational Science
- Numerical Analysis
- Parallel Computing
Background:
- Domain decomposition for solving partial differential equations on parallel computers often incurs significant synchronization and communication overhead.
- This overhead impacts computational time and energy efficiency, particularly in large-scale simulations.
Purpose of the Study:
- To develop communication-efficient parallel algorithms for solving partial differential equations.
- To reduce the time and energy overhead associated with processor synchronization and communication.
- To maintain solution accuracy while improving computational efficiency.
Main Methods:
- An asynchronous algorithm was developed to eliminate synchronization requirements and enable distributed termination detection.
- An event-triggered communication algorithm was built upon the asynchronous approach, reducing message passing frequency.
- Algorithms were demonstrated using a successive over-relaxation solver for the 3-D pressure Poisson equation in multiphase flows.
Main Results:
- The proposed asynchronous algorithm removes the need for synchronization and allows for distributed termination checks.
- The event-triggered algorithm significantly reduces the number of inter-processor messages by communicating boundary values only at specific iterations.
- Both algorithms demonstrated improvements in time and energy efficiency for the target application.
Conclusions:
- Communication-efficient parallel algorithms can significantly alleviate overhead in solving partial differential equations.
- Asynchronous and event-triggered approaches offer viable strategies for enhancing performance in parallel scientific computing.
- The developed methods show promise for accelerating simulations in fields like computational fluid dynamics.

