This work analyzes the role of graphic processing units (GPUs) in the framework of traditional parallel architectures (MIMD, SIMD, . . .) to find some effective parameters for the prediction of algorithms performance in a GPUenhanced environment. We consider a medical imaging application, namely the deconvolution of 3D Fluorescence Microscopy images, implemented in C with CUDA extension on a NVIDIA Tesla C1060: describing its design choices we intend to show how those parameters can affect actual algorithms performance.
Performance Parameters for Parallel Algorithms in GPU-Enhanced Environments / D'Amore, Luisa; Mele, Valeria; Romano, Diego; Murli, Almerico. - (2012), pp. 62-62. (Intervento presentato al convegno SIAM Conference on Parallel Processing for Scientific Computing tenutosi a Georgia (USA) nel febbraio 2012).
Performance Parameters for Parallel Algorithms in GPU-Enhanced Environments
D'AMORE, LUISA;MELE, VALERIA;ROMANO, DIEGO;MURLI, ALMERICO
2012
Abstract
This work analyzes the role of graphic processing units (GPUs) in the framework of traditional parallel architectures (MIMD, SIMD, . . .) to find some effective parameters for the prediction of algorithms performance in a GPUenhanced environment. We consider a medical imaging application, namely the deconvolution of 3D Fluorescence Microscopy images, implemented in C with CUDA extension on a NVIDIA Tesla C1060: describing its design choices we intend to show how those parameters can affect actual algorithms performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
