Section: Application Domains
Code Coupling and Grid Programming
Participants : François Broquedis, Thierry Gautier, Jean-Louis Roch, Vincent Danjean, Frédéric Wagner.
Code coupling aim is to assemble component to build distributed applications by reusing legacy code. The objective here is to build high performance applications for cluster and grid infrastructures.
Grid programming model and runtime support. Programming the grid is a challenging problem. The MOAIS Team has a strong knowledge in parallel algorithms and develop a runtime support for scheduling grid program written in a very high level interface. The parallelism from recursive divide and conquer applications and those from iterative simulation are studied. Scheduling heuristics are based on online work stealing for the former class of applications, and on hierarchical partitioning for the latter. The runtime support provides capabilities to hide latency by computation thanks to a non-blocking one-side communication protocol and by re-ordering computational tasks.
Grid application deployment. To test grid applications, we need to deploy and start programs on all used computers. This can become difficult if the real topology involves several clusters with firewall, different runtime environments, etc. The MOAIS Team designed and implemented a new tool called karun that allows a user to easily deploy a parallel application wrote with the Kaapi software. This Kaapi tool relies on the TakTuk software to quickly launch programs on all nodes. The user only needs to describe the hierarchical networks/clusters involved in the experiment with their firewall if any.
Visualization of grid applications execution. The analysis of applications execution on the grid is challenging both because of the large scale of the platform and because of the heterogeneous topology of the interconnections. To help users to understand their application behavior and to detect potential bottleneck or load unbalance, the MOAIS team designed and implemented a tool named Triva. This tool proposes a new three dimensional visualization model that combines topological information to space time data collected during the execution. It also proposes an aggregation mechanism that eases the detection of application load unbalance.