AbstractIn this work we propose a high performance parallelization of the software package COMPSYN, devoted to the production of syntethic seismograms, on a cluster of multicore processors with multiple GPUs. To design and implement the proposed high performance version, we started from a naıve parallel version of COMPSYN. The naıve version consists in a simple parallelization on both device side, obtained by exploiting CUDA, and host side, obtained by exploiting the MPI paradigm and OpenMP API. The proposed high performance version implements several practical techniques of CUDA programming and deeply exploits the GPU architecture, thus achieving a much better performance with respect to the naıve version. We compare the performance of the...
Map-Reduce is a framework for processing parallelizable problem across huge datasets using a large c...
International audienceThis paper presents two parallel Simplified PN (SPN) solver implementations fo...
Spherical Harmonic Transforms (SHT) are at the heart of many scientific and practical applications r...
In this work we propose a high performance parallelization of the software package COMPSYN, devoted ...
In this work we propose two different parallel versions of the software package COMPSYN, devoted to ...
In this work we propose two different parallel versions of the software package COMPSYN, devoted to ...
Using two full applications with different characteristics, this thesis explores the performance and...
We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA para...
The introduction and rise of General Purpose Graphics Computing has significantly impacted parallel ...
We present our new parallel GPU clusters in Beijing and Heidelberg and demonstrate the nearly optima...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
AbstractThe computational performance of a smoothed particle hydrodynamics (SPH) simulation is inves...
The rate of scientific discovery depends on the speed at which accurate results and analysis can be...
A new trend in computing is the use of multi-core processors and the use of Graphics Processing Unit...
Map-Reduce is a framework for processing parallelizable problem across huge datasets using a large c...
International audienceThis paper presents two parallel Simplified PN (SPN) solver implementations fo...
Spherical Harmonic Transforms (SHT) are at the heart of many scientific and practical applications r...
In this work we propose a high performance parallelization of the software package COMPSYN, devoted ...
In this work we propose two different parallel versions of the software package COMPSYN, devoted to ...
In this work we propose two different parallel versions of the software package COMPSYN, devoted to ...
Using two full applications with different characteristics, this thesis explores the performance and...
We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA para...
The introduction and rise of General Purpose Graphics Computing has significantly impacted parallel ...
We present our new parallel GPU clusters in Beijing and Heidelberg and demonstrate the nearly optima...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
AbstractThe computational performance of a smoothed particle hydrodynamics (SPH) simulation is inves...
The rate of scientific discovery depends on the speed at which accurate results and analysis can be...
A new trend in computing is the use of multi-core processors and the use of Graphics Processing Unit...
Map-Reduce is a framework for processing parallelizable problem across huge datasets using a large c...
International audienceThis paper presents two parallel Simplified PN (SPN) solver implementations fo...
Spherical Harmonic Transforms (SHT) are at the heart of many scientific and practical applications r...