Modern graphics processing units (GPUs) have evolved from a configurable graphics processor to massively parallel many-core multiprocessors for rapidly solving data and time intensive [24].
The dataflow programming model has shown to be a relevant approach to efficiently run massively parallel applications over many-core architectures.
Transistor density has made possible the design of massively parallel architectures with hundreds of cores on a single chip.
We develop a Lattice Boltzmann code for computational fluid-dynamics and optimize it for massively parallel systems based on multi-core processors.
P-EFIT is based on the EFIT framework, but built with the CUDA™ architecture to take advantage of massively parallel Graphical Processing Unit (GPU) cores to significantly accelerate the computation.
System architectures are also of interest as long as they are supporting physics-related calculations, such as: massively parallel systems, GPUs, many-integrated-cores, distributed (cluster, grid/cloud) computing, and hybrid systems.
System architectures are also of interest as long as they are supporting physics related calcula- tions, such as: massively parallel systems, GPUs, many-integrated-cores, distributed (cluster, grid/cloud) computing, and hybrid systems.
We expect to achieve about 10 tera FLOPS (TFLOPS) for a self-gravitating collisional system with N ∼ 105 on massively parallel systems with at most 800 cores with Sandy Bridge micro-architecture.
This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters.
GPU is a massively parallel device, with a significant number of processing cores, whose processing ability can be exploited for general purpose use in high arithmetic intensity algorithms, where the parallel nature of its architecture can be exploited to maximum benefit.
