FlowVR: A Middleware for Large Scale Virtual Reality Applications

Self Cite

Abstract. Performance of distributed applications largely depends on the mapping of their components on the underlying architecture. On one side, component-based approaches provide an abstraction suitable for development, but on the other side, actual hardware becomes every day more complex and heterogeneous. Despite this increasing gap, mapping components to processors and networks is commonly done manually and is mainly a matter of expertise. Worse, the amount of efforts required for this task rarely allows to further consider optimal hardware use or sensitivity analysis of data scaling. In this paper, we rely on a formal and experimentally sound model of performance and propose a constraint programming based framework to find consistent and efficient mappings of an application onto an architecture. Experiments show that an optimal mapping for a medium-sized application can be found in a few seconds.

“…In this case, the destination module always uses the last available message while older messages are discarded. More details on FlowVR can be found in [1].…”

Section: Performance Of Flowvr Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Heterogeneous Distributed Applications on Clusters

Jubertie

Melin

Vautard

et al. 2008

Self Cite

“…More details on FlowVR can be found in [2]. A FlowVR application is a set of modules which communicate via messages through a data-flow network.…”

Section: The Flowvr Frameworkmentioning

confidence: 99%

“…The FlowVR library [2][4] was created to ease the development of distributed interactive applications and to permit greedy communication. But FlowVR does not offer a way to obtain the best application mapping on a given cluster nor any kind of performance information.…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction for Mappings of Distributed Applications on PC Clusters

Jubertie

Melin

2007

Self Cite

Abstract. Distributed applications running on clusters may be composed of several components with very different performance requirements. The FlowVR middleware allows the developer to deploy such applications and to define communication and synchronization schemes between components without modifying the code. While it eases the creation of mappings, FlowVR does not come with a performance model. Consequently the optimization of mappings is left to the developer's skills. But this task becomes difficult as the number of components and cluster nodes grow and even more complex if the cluster is composed of heterogeneous nodes and networks. In this paper we propose an approach to predict performance of FlowVR distributed applications given a mapping and a cluster. We also give some advice to the developer to create efficient mappings and to avoid configurations which may lead to unexpected performance. Since the FlowVR model is very close to underlying models of lots of distributed codes, our approach can be useful for all designers of such applications.

“…Dedicated environments like FlowVR [8], OpenMask [9] or COVISE [10] propose different approaches to support such features. However, the resulting application code tends to be difficult to maintained when reaching a certain size.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Programming Model for Large Parallel Interactive Applications

Lesage

Raffin

2007

Self Cite

Abstract. This paper focuses on parallel interactive applications ranging from scientific visualization, to virtual reality or computational steering. Interactivity makes them particular on three main aspects: they are endlessly iterative, use advanced I/O devices, and must perform under strong performance constraints (latency, refresh rate). In this paper, we propose an application description language based on a data flow and hierarchical component model to cope with the complexity of parallel interactive applications. It enables us to define highly generic components, enforcing the application maintainability and portability. An implementation on top of the FlowVR middleware is presented.