PROMESPAR: A High Performance Computing Implementation of the Regional Atmospheric Model PROMES

Garrido, Juan Enrique; Arias, Enrique; Cazorla, Diego; Cuartero, Fernando; Fernández, Irene; Gallardo, Clemente

doi:10.1007/978-90-481-8776-8_45

Cited by 2 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Garrido et al [8] present a parallel solution for a forecast model which works with several atmospheric phenomenons named PROMES. The amount of parameters, the need for spatial resolution, accuracy and reasonable run times make it necessary to use parallel platforms.…”

Section: Related Workmentioning

confidence: 99%

“…Garrido et al [9] recognize that vectorial multiprocessors are perhaps the best parallel platform to solve weather models, as a consequence of how these can handle the data domain. GPUs are a return to vectorial machines and thus seem to be the natural choice for solving these kinds of problems.…”

Section: Related Workmentioning

confidence: 99%

“…The usage of GPU in WR applications is recent and focuses mostly on the implementation of meteorological models [9] or visualization [18]. The structure of raw WR data suits the GPU architecture well, since each radar cell maps to a number in the radar data cube [14], and processing is usually decoupled.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weather radar data processing on graphic cards

2017

View full text Add to dashboard Cite

Weather radar operation generates data at a high rate that requires prompt processing. The operations performed on data for weather product generation are repeated in each resolution cell and thus are naturally prone to parallelization. Parallel processing using graphic cards is an emerging technology that allows for implementation of high-throughput algorithms at a low cost. In this paper, the parallel implementation of the main product of a polarimetric weather radar using GPU is presented, focusing on its optimization. A speedup exceeding 20× is obtained when compared to the serial implementation. Also processing is found to be memory bound, which results in a counter-intuitive performance improvement when the number of threads per job is reduced.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Weather radar data processing on graphic cards

2017

View full text Add to dashboard Cite

show abstract

“…This results in unacceptably high simulation execution times which increase even more depending on the temporal and spatial resolution or time interval required. This calls for parallelization and although an atmospheric model using parallelization already exists (Garrido et al 2009), its accompanying ocean model was still sequential.…”

Section: Introductionmentioning

confidence: 99%

Efficient parallelization of a regional ocean model for the western Mediterranean Sea

Córdoba

García-Dopico

García

et al. 2013

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

This paper focuses on the parallelization of an ocean model applying current multicore processor-based cluster architectures to an irregular computational mesh. The aim is to maximize the efficiency of the computational resources used. To make the best use of the resources offered by these architectures, this parallelization has been addressed at all the hardware levels of modern supercomputers: firstly, exploiting the internal parallelism of the CPU through vectorization; secondly, taking advantage of the multiple cores of each node using OpenMP; and finally, using the cluster nodes to distribute the computational mesh, using MPI for communication within the nodes. The speedup obtained with each parallelization technique as well as the combined overall speedup have been measured for the western Mediterranean Sea for different cluster configurations, achieving a speedup factor of 73.3 using 256 processors. The results also show the efficiency achieved in the different cluster nodes and the advantages obtained by combining OpenMP and MPI versus using only OpenMP or MPI. Finally, the scalability of the model has been analysed by examining computation and communication times as well as the communication and synchronization overhead due to parallelization.

show abstract

PROMESPAR: A High Performance Computing Implementation of the Regional Atmospheric Model PROMES

Cited by 2 publications

References 18 publications

Weather radar data processing on graphic cards

Weather radar data processing on graphic cards

Efficient parallelization of a regional ocean model for the western Mediterranean Sea

Contact Info

Product

Resources

About