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Parallel anisotropic mesh refinement with dynamic load balancing for transonic flow simulations

Gepner S., Majewski J., Rokicki J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2017

Vol. 65, nr 2

195--207

The present paper discusses an effective adaptive methods suited for use in parallel environment. An in-house, parallel flow solver based on the residual distribution method is used for the solution of flow problems. Simulation is parallelized based on the domain decomposition approach. Adaptive changes to the mesh are achieved by two distinctive techniques. Mesh refinement is performed by dividing element edges and a subsequent application of pre defined splitting templates. Mesh regularization and derefinement is achieved through topology conserving node movement (r-adaptivity). Parallel implementations of an adaptive use the dynamic load balancing technique.

ADAJ : a Java Distributed Environment for Easy Programming Design and Efficient Execution

Felea V., Olejnik R., Toursel B.

Schedae Informaticae

2004

Vol. 13

9-36

In this paper we present a platform, ADAJ (Adaptive Distributed Applications in Java), that we have developed for distributed applications in Java over a cluster of computers. The objective. The objective of this platform is to facilitate the application design and to efficiently use the computing power. ADAJ offers both a programming and execution environment. The programming environment simplifies the user programming effort and offers a MIND/SPMD programming model style. Our approach relies on the concept of distributed collections, grouping fragmented objects and on asynchronous calls. The ADAJ execution environment supports irregularities during the evolution of the application and in the availabilities of resources. We present the architecture and the tools we have developed: object observation and load balancing mechanisms. The observation mechanism allows to estimate the JVM load. The load balancing mechanism dynamically adapts the execution according to this information. We present estimation measures on two different applications, so that to evaluate the benefits and the overhead of ADAJ.