Enrique S. Quintana–Ort́ı

Universitat Politècnica de València

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Co-Authors

Rafael Mayo

Universitat Jaume I

115

Peter Benner

Max Planck Institute for Dynamics of Complex Technical Systems

José I. Aliaga

Universitat Jaume I

Francisco D. Igual

Universidad Complutense de Madrid

Gregorio Quintana‐Ortí

Universitat Jaume I

Pablo Ezzatti

Universidad de la República

Alfredo Remón

Biomechanics Institute of Valencia

Adrián Castelló

Universitat Politècnica de València

Sandra Catalán

Universitat Jaume I

Rafael Rodríguez‐Sánchez

Universidad Complutense de Madrid

Cooperative Institutions

Universidad Católica Andrés Bello

233

Geospatial Research (United Kingdom)

Universitat Jaume I

Universitat Politècnica de València

Universitat Politècnica de Catalunya

Barcelona Supercomputing Center

Universitat de València

Universidad de la República

Argonne National Laboratory

University of Illinois Urbana-Champaign

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Cáncer colorrectal: concepto de neoplasias sincrónicas y metacrónicas

Revista de la Sociedad Valenciana de Patología Digestiva (2000)

Joan Tosca Enrique S. Quintana–Ort́ı P Canelles Francisco Quiles S Pamos

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Citations (2)

Partial stabilization of large-scale discrete-time linear control systems

Peter Benner Maribel Castillo Enrique S. Quintana–Ort́ı

We propose a parallel algorithm for stabilizing large discrete-time linear control systems on a Beowulf cluster. Our algorithm first separates the Schur stable part of the linear control system using an inverse-free iteration for the matrix disc function, and then computes a stabilizing feedback matrix for the unstable part. This stage requires the numerical solution of a Stein equation. This linear matrix equation is solved using the sign function method after applying a Cayley transformation to the original equation. The experimental results on a cluster composed of Intel PII processors and a Myrinet interconnection network show the parallelism and scalability of our approach.

Matrix (chemical analysis)

Myrinet

10.1109/icppw.2001.951856

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Citations (15)

Improved Accuracy and Parallelism for MRRR-based Eigensolvers -- A Mixed Precision Approach

arXiv (Cornell University) (2013)

Matthias Petschow Enrique S. Quintana–Ort́ı Paolo Bientinesi

The real symmetric tridiagonal eigenproblem is of outstanding importance in numerical computations; it arises frequently as part of eigensolvers for standard and generalized dense Hermitian eigenproblems that are based on a reduction to tridiagonal form. For its solution, the algorithm of Multiple Relatively Robust Representations (MRRR) is among the fastest methods. Although fast, the solvers based on MRRR do not deliver the same accuracy as competing methods like Divide & Conquer or the QR algorithm. In this paper, we demonstrate that the use of mixed precisions leads to improved accuracy of MRRR-based eigensolvers with limited or no performance penalty. As a result, we obtain eigensolvers that are not only equally or more accurate than the best available methods, but also -in most circumstances- faster and more scalable than the competition.

Double-precision floating-point format

10.48550/arxiv.1304.1864

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Citations (0)

Real-time Sound Source Localization on an Embedded GPU Using a Spherical Microphone Array

Procedia Computer Science (2015)

Jose A. Belloch Máximo Cobos Alberto González Enrique S. Quintana–Ort́ı

Spherical microphone arrays are becoming increasingly important in acoustic signal processing systems for their applications in sound field analysis, beamforming, spatial audio, etc. The positioning of target and interfering sound sources is a crucial step in many of the above applications. Therefore, 3D sound source localization is a highly relevant topic in the acoustic signal processing field. However, spherical microphone arrays are usually composed of many microphones and running signal processing localization methods in real time is an important issue. Some works have already shown the potential of Graphic Processing Units (GPUs) for developing high-end real-time signal processing systems. New embedded systems with integrated GPU accelerators providing low power consumption are becoming increasingly relevant. These novel systems play a very important role in the new era of smartphones and tablets, opening further possibilities to the design of high-performance compact processing systems. This paper presents a 3D source localization system using a spherical microphone array fully implemented on an embedded GPU. The real-time capabilities of these platforms are analyzed, providing also a performance analysis of the localization system under different acoustic conditions.

SIGNAL (programming language)

Array Processing

10.1016/j.procs.2015.05.226

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Citations (5)

Towards a Distributed GPU-Accelerated Matrix Inversion

Gerardo Ares Pablo Ezzatti Enrique S. Quintana–Ort́ı

Matrix (chemical analysis)

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Citations (0)

Automatic generation of ARM NEON micro-kernels for matrix multiplication

The Journal of Supercomputing (2024)

Guillermo Alaejos Héctor Martínez Adrián Castelló Manuel F. Dolz Francisco D. Igual

Abstract General matrix multiplication ( gemm ) is a fundamental kernel in scientific computing and current frameworks for deep learning. Modern realisations of gemm are mostly written in C, on top of a small, highly tuned micro-kernel that is usually encoded in assembly. The high performance realisation of gemm in linear algebra libraries in general include a single micro-kernel per architecture, usually implemented by an expert. In this paper, we explore a couple of paths to automatically generate gemm micro-kernels, either using C++ templates with vector intrinsics or high-level Python scripts that directly produce assembly code. Both solutions can integrate high performance software techniques, such as loop unrolling and software pipelining, accommodate any data type, and easily generate micro-kernels of any requested dimension. The performance of this solution is tested on three ARM-based cores and compared with state-of-the-art libraries for these processors: BLIS, OpenBLAS and ArmPL. The experimental results show that the auto-generation approach is highly competitive, mainly due to the possibility of adapting the micro-kernel to the problem dimensions.

Matrix (chemical analysis)

10.1007/s11227-024-05955-8

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Citations (1)

Architecture-Aware Optimization of an HEVC decoder on Asymmetric Multicore Processors

arXiv (Cornell University) (2016)

Rafael Rodríguez‐Sánchez Enrique S. Quintana–Ort́ı

Low-power asymmetric multicore processors (AMPs) attract considerable attention due to their appealing performance-power ratio for energy-constrained environments. However, these processors pose a significant programming challenge due to the integration of cores with different performance capabilities, asking for an asymmetry-aware scheduling solution that carefully distributes the workload. The recent HEVC standard, which offers several high-level parallelization strategies, is an important application that can benefit from an implementation tailored for the low-power AMPs present in many current mobile or hand-held devices. In this scenario, we present an architecture-aware implementation of an HEVC decoder that embeds a criticality-aware scheduling strategy tuned for a Samsung Exynos 5422 system-on-chip furnished with an ARM big.LITTLE AMP. The performance and energy efficiency of our solution is further enhanced by exploiting the NEON vector engine available in the ARM big.LITTLE architecture. Experimental results expose a 1080p real-time HEVC decoding at 24 frames/sec, and a reduction of energy consumption over 20%.

Multi-core processor

ARM architecture

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Extending ILUPACK with a Task-Parallel Version of BiCG for Dual-GPU Servers

José I. Aliaga Matthias Bollhöfer Ernesto Dufrechou Pablo Ezzatti Enrique S. Quintana–Ort́ı

We target the solution of sparse linear systems via iterative Krylov subspace-based methods enhanced with the ILUPACK preconditioner on graphics processing units (GPUs). Concretely, in this work we extend ILUPACK with an implementation of the BiCG solver capable of exploiting dual-GPU systems. We leverage the structure of the BiCG to execute the main stages of the solver in a concurrent manner, and take advantage of the extended memory space to improve the data access patterns. The experimental results on a server with two NVIDIA K40 GPUs show important acceleration factors with respect to a previous single GPU variant.

Krylov subspace

Solver

Leverage (statistics)

10.1145/3178442.3178450

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Citations (1)

Table of content

Gabriel Lema Luis Martino Sebastián Berchesi Alicia Fernández Federico Lecumberry

Table (database)

Content (measure theory)

10.1109/clei.2014.6965201

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