Distributed, object-based programming systems
ACM Computing Surveys (CSUR)
Orca: A Language for Parallel Programming of Distributed Systems
IEEE Transactions on Software Engineering
Cobra: A CORBA-compliant Programming Environment for High-Performance Computing
Euro-Par '98 Proceedings of the 4th International Euro-Par Conference on Parallel Processing
PARDIS: A Parallel Approach to CORBA
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
QoS as Middleware: Bandwidth Reservation System Design
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
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Adaptive utilization of resources in a highly heterogeneous computational environment such as the Grid is a difficult question. In this paper, we address an object-oriented approach to the solution using requirement-driven parallel objects. Each parallel object is a selfdescribed, shareable and passive object that resides in a separate memory address space. The allocation of the parallel object is driven by the constraints on the resource on which the object will live. A new parallel programming paradigm is presented in the context of ParoC++ - a new parallel object-oriented programming environment for high performance distributed computing. ParoC++ extends C++ for supporting requirement-driven parallel objects and a runtime system that provides services to run ParoC++ programs in distributed environments. An industrial application on real-time image processing is used as a test case to the system. The experimental results show that the ParoC++ model is efficient and scalable and that it makes easier to adapt parallel applications to dynamic environments.