Electronic International Standard Serial Number (EISSN)
In recent years, on-line processing of data streams (DaSP) has been established as a major computing paradigm. This is due mainly to two reasons: first, more and more data that are generated in near real-time need to be processed; the second reason is given by the need of efficient parallel applications. However, the above-mentioned areas expose a tough challenge over traditional data-analysis techniques, which have been forced to evolve to a stream perspective. In this work, we apply a novel multiple back end programming framework for stream data and task based parallelism to a multi-staged diffusion magnetic resonance imaging (MRI) toolkit, named pHARDI. The results demonstrate the benefits of using our framework in terms of performance and memory usage. The evaluation carried out also depicts that the speed-up of our parallel framework increases with the problem size.
data stream processing; grppi; task-level parallelism; data-level parallelism; mri reconstruction