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3 results on '"Albert Farrés"'

1. Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors

Author

Albert Farrés, Mauricio Hanzich, Charles R. Yount, Claudia Rosas, Alejandro Duran, S. Fernández, and Barcelona Supercomputing Center

Subjects
medicine.medical_specialty, Wave propagation, Intel Xeon Phi x200 processor family, business.industry, Earth science, 3D full-wave field modelling-based applications, Enginyeria electrònica [Àrees temàtiques de la UPC], OpenMP, Permission, Metamorphic petrology, Mecànica ondulatòria, Telmatology, Wave mechanics, medicine, media_common.cataloged_instance, Christian ministry, European union, Telecommunications, business, Geology, Research center, Xeon Phi, media_common
Abstract

This work shows several optimization strategies evaluated and applied to an elastic wave propagation engine, based on a Fully Staggered Grid, running on the latest Intel Xeon Phi processors, the second generation of the product (code-named Knights Landing). Our fully optimized code shows a speed-up of about 4x when compared with the same algorithm optimized for the previous generation processor. Authors also thank Repsol for the permission to publish the present research, carried out at the Repsol-BSC Research Center. This work has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement n.◦ 689772. * Other brands and names are the property of their respective owners.

Published
2017
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2. On the Use of Probabilistic Worst-Case Execution Time Estimation for Parallel Applications in High Performance Systems

Author

Matteo Fusi, Fabio Mazzocchetti, Albert Farres, Leonidas Kosmidis, Ramon Canal, Francisco J. Cazorla, and Jaume Abella

Subjects
wcet, probabilistic timing analysis, randomization, measurement-based, hpc applications, Mathematics, QA1-939
Abstract

Some high performance computing (HPC) applications exhibit increasing real-time requirements, which call for effective means to predict their high execution times distribution. This is a new challenge for HPC applications but a well-known problem for real-time embedded applications where solutions already exist, although they target low-performance systems running single-threaded applications. In this paper, we show how some performance validation and measurement-based practices for real-time execution time prediction can be leveraged in the context of HPC applications on high-performance platforms, thus enabling reliable means to obtain real-time guarantees for those applications. In particular, the proposed methodology uses coordinately techniques that randomly explore potential timing behavior of the application together with Extreme Value Theory (EVT) to predict rare (and high) execution times to, eventually, derive probabilistic Worst-Case Execution Time (pWCET) curves. We demonstrate the effectiveness of this approach for an acoustic wave inversion application used for geophysical exploration.

Published
2020
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3. Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems

Author

William Fornaciari, Carlo Brandolese, Alessandro Cilardo, Carles Hernandez, José María Torralba Martínez, Luca Cremona, Michal Kulchewski, Simone Libutti, Giovanni Agosta, Jose Flich, Ariel Oleksiak, Marina Zapater, Giuseppe Massari, Albert Farrés, Rafael Tornero, David Atienza, Michele Zanella, Federico Reghenzani, Anna Pupykina, Leila Cammoun, and Davide Zoni

Subjects
Exploit, Computer science, Distributed computing, Cloud computing, 02 engineering and technology, Multi many cores, run time management, models, 0202 electrical engineering, electronic engineering, information engineering, power optimizaton, Massively parallel, 020203 distributed computing, business.industry, mpi/openmp, simulation, Exascale computing, 020202 computer hardware & architecture, Power (physics), Range (mathematics), multi, HPC, Central processing unit, business, Efficient energy use, Multi many cores, run time management, power optimizaton, HPC, energy
Abstract

The transition to Exascale computing is going to be characterised by an increased range of application classes. In addition to traditional massively parallel "number crunching" applications, new classes are emerging such as real-time HPC and data-intensive scalable computing. Furthermore, Exascale computing is characterised by a "democratisation" of HPC: to fully exploit the capabilities of Exascale-level facilities, HPC is moving towards enabling access to its resources to a wider range of new players, including SMEs, through cloud-based approaches [1]. Finally, the need for much higher energy efficiency is pushing towards deep heterogeneity, widening the range of options for acceleration, moving from the traditional CPU-only organization, to the CPU plus GPU which currently dominates the Green5001, to more complex options including programmable accelerators and even (reconfigurable) hardware accelerators [2].

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