Hybrid Computing Platform to Enable Complex Multi-Physics DNS/LES from Desktop to Exascale
Abstract
We propose to develop the hybrid CPU-GPU paradigm for unstructured overset grid large-eddy simulation, that promises transformational speedups in computing time. Our goal is to enable exascale simulations on the largest platforms as well as highly affordable simulations on desktop machines. We will develop the capability for our in-house solver MPCUGLES and demonstrate its impact on our current ONR projects. The speedup enabled by the proposed research will significantly impact the utilization of LES for practical problems. Developing a GPU extension for a code as complex as MPCUGLES is not straightforward and involves significant algorithm and data-handling development, and hardware-aware programming. The proposed machine is essential to (i) develop the hybrid accelerated methodology, (ii) evaluate it for relevant problems like crashback, cavitation and hull maneuvering, (iii) explore the potential in detail in terms of what is feasible on machines ranging from desktop machines to leadership class machines. If the promise of the capability being developed is realized, it will enable LES of practical Navy relevant problems to be performed on desktops. Approved for public release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 13, 2023
- Source ID
- N000142312894
Entities
People
- Krishnan Mahesh
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy