With the support of the high-level abstractions provided by the proposed GOP environment, programming of parallel applications on various parallel architectures can be greatly simplified.
A procedure for the analysis of the transient response and for the evaluation of the accuracy of the proposed method, based on the application of parallel RC circuits, is also given.
Finally, we show a real application of parallel computing on genome-enabled prediction in beef cattle.
To this end, we build a programming environment dedicated to the fast prototyping of embedded vision applications based on parallel algorithmic skeletons.
We have measured the performance of five applications on two parallel architectures.
We present experimental results highlighting the performance improvements obtained by our methods using real scientific data-analysis applications on multiple parallel and distributed processing configurations (e.g., single symmetric multiprocessor (SMP) machine, cluster of SMP nodes, and a Grid computing configuration).
This manuscript presents a general conceptual model for easily porting environmental applications on different parallel and distributed infrastructures.
As specified before, the proposed conceptual model is a key component in a general methodology for easily and efficiently porting environmental applications on different parallel and distributed infrastructures.
The main goal of this manuscript is to fill that gap and to propose a conceptual model of environmental applications, which will be a key component in a general methodology for porting these applications on different parallel and distributed infrastructures.
The methodology consists in proposing solutions to easily and efficiently port and execute environmental applications on different parallel and distributed infrastructures, using the conceptual model proposed in this manuscript.
We argue it from the following aspects: (1) Distributed data partitioning, message communication and task organization are given to support transparent application execution on parallel hardware.
