Rocío Carratalá-SáezUniversidad de Valladolid | UVA · Escuela Técnica Superior de Ingeniería Informática
Rocío Carratalá-Sáez
PhD in Computer Science
Assistant Professor at Universidad de Valladolid
About
12
Publications
562
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36
Citations
Citations since 2017
Introduction
I am currently an assistant professor at Universidad de Valladolid. I obtained my Ph.D. in Computer Science by Universitat Jaume I (UJI) in 2021 (cum laude), M.Sc. in Parallel and Distributed Computing by Universitat Politècnica de València (UPV) in 2016, and B.Sc. in Computational Mathematics by UJI in 2015.
My main research interests are HPC, HTC, and optimization of linear algebra algorithms and fluid dynamic applications. Moreover, I am also interested in HPC education innovative strategies
Publications
Publications (12)
The determination of Lagrangian Coherent Structures (LCS) is becoming very important in several disciplines, including cardiovascular engineering, aerodynamics, and geophysical fluid dynamics. From the computational point of view, the extraction of LCS consists of two main steps: The flowmap computation and the resolution of Finite Time Lyapunov Ex...
In this work we present an extension of our previous experience with the course “Build your own supercomputer with Raspberry Pi”, presented in [1]. It is offered as a non-mandatory workshop with the purpose of bringing High Performance Computing (HPC) closer to bachelor students of Jaume I University (UJI, Spain). The intention of the course is two...
We address the parallelization of the LU factorization of hierarchical matrices ($\mathcal{H}$-matrices) arising from boundary element methods. Our approach exploits task-parallelism via the OmpSs programming model and runtime, which discovers the data-flow parallelism intrinsic to the operation at execution time, via the analysis of data dependenc...
We address the parallelization of the LU factorization of hierarchical matrices (-matrices) arising from boundary element methods. Our approach exploits task-parallelism via the OmpSs programming model and runtime, which discovers the data-flow parallelism intrinsic to the operation at execution time, via the analysis of data dependencies based on...
We investigate the introduction of look-ahead in two-stage algorithms for the singular value decomposition (SVD). Our approach relies on a specialized reduction for the first stage that produces a band matrix with the same upper and lower bandwidth instead of the conventional upper triangular-band matrix. In the case of a CPU-GPU server, this alter...
We present a prototype task-parallel algorithm for the solution of hierarchical symmetric positive definite linear systems via the ℋ -Cholesky factorization that builds upon the parallel programming standards and associated runtimes for OpenMP and OmpSs. In contrast with previous efforts, our proposal decouples the numerical aspects of the linear a...
