RT info:eu-repo/semantics/conferenceObject
T1 TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities
T2 TuCCompi: A Multi-Layer Programming Model for Heterogeneous Systems with Auto-Tuning Capabilities
A1 Ortega-Arranz, Héctor
A1 Torres de la Sierra, Yuri
A1 González Escribano, Arturo
A1 Llanos Ferraris, Diego Rafael
K1 Informática
K1 APSP
K1 Auto-Tuning
K1 CUDA
K1 GPU
K1 Heterogeneous system
K1 HPC framework
K1 MPI
K1 OpenMP
K1 Parallel model
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
AB During the last decade, parallel processor architectures have become a powerful tool to deal with massively-parallel problems that require High Performance Computing (HPC). The last trend of HPC is the use of heterogeneous environments, that combine different computational power units, such as CPU-cores and GPUs. Performance maximization of any GPU parallel implementation of an algorithm requires an in-depth knowledge about its underlying architecture, becoming a tedious task only suited for experienced programmers. In this paper we present TuCCompi, a multi-layer framework that not only transparently exploits heterogeneous systems, but automatically tunes the GPU capabilities by choosing the optimal values for their configuration parameters, using the kernel characterization provided by the programmer. This model is very useful to tackle problems characterized by independent , high computational-load tasks with none or few communications , such as embarrassingly-parallel problems. We have evaluated TuCCompi in different, real-world heterogeneous environments using the APSP problem as a case study.
YR 2014
FD 2014
LK https://uvadoc.uva.es/handle/10324/70853
UL https://uvadoc.uva.es/handle/10324/70853
LA eng
NO HiPEAC 2014 Workshop on High-Level Parallel Programming for GPUs (HLPGPU), Vienna, Austria.
NO Producción Científica
DS UVaDOC
RD 19-ene-2025