Soleil-X: turbulence, particles, and radiation in the Regent programming language

The Predictive Science Academic Alliance Program (PSAAP) II at Stanford University is developing an exascale-ready multi-physics solver to investigate particle-laden turbulent flows in a radiation environment for solar energy receiver applications. In order to simulate the proposed concentrated particle-based receiver design three distinct but coupled physical phenomena must be modeled: fluid flows, Lagrangian particle dynamics, and the transport of thermal radiation. Therefore, three different physics solvers (fluid, particles, and radiation) must run concurrently with significant cross-communication in an integrated multi-physics simulation. However, each solver uses substantially different algorithms and data access patterns. Coordinating the overall data communication, computational load balancing, and scaling these different physics solvers together on modern massively parallel, heterogeneous high performance computing systems presents several major challenges. We have adopted the Legion programming system, via the Regent programming language, and its task parallel programming model to address these challenges. Our multi-physics solver Soleil-X is written entirely in the high level Regent programming language and is one of the largest and most complex applications written in Regent to date. At this workshop we will give an overview of the software architecture of Soleil-X as well as discuss how our multi-physics solver was designed to use the task parallel programming model provided by Legion. We will also discuss the development experience, scaling, performance, portability, and multi-physics simulation results.Postprint (published version

High performance conjugate heat transfer with the openpalm coupler

Optimizing gas turbines is a complex multi-physical and multi-component problem that has long been based on expensive experiments. Today, computer simulations can reduce design process costs and are acknowledged as a promising path for optimization. Although the simulations of specific components of gas turbines become accessible, these stand-alone simulations face a new challenge: to improve the quality of the results, new physics must be introduced. Based on the simulation of conjugate heat transfer within an industrial combustor to predict the temperature of its walls, the current work aims at studying the scalability of code coupling on HPC architectures. Coupling accurately solvers on massively parallel architectures while maintaining their scalability is challenging. The strategy investigated relies on recent developments made in a generic parallel coupler. Performance tests have been carried out until 12,288 cores on the CURIE supercomputer (TGCC / CEA). Results show a good behavior and advanced analyzes are carried out in order to draw new paths for future developments in coupled simulations

A parallel compact-TVD method for compressible fluid dynamics employing shared and distributed-memory paradigms

A novel multi-block compact-TVD finite difference method for the simulation of compressible flows is presented. The method combines distributed and shared-memory paradigms to take advantage of the configuration of modern supercomputers that host many cores per shared-memory node. In our approach a domain decomposition technique is applied to a compact scheme using explicit flux formulas at block interfaces. This method offers great improvement in performance over earlier parallel compact methods that rely on the parallel solution of a linear system. A test case is presented to assess the accuracy and parallel performance of the new method