3,878 research outputs found
Companion for "Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources"
No full text<p>This is the companion data for the VECPAR2018 submission paper entitled: Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources by Lucas Leandro Nesi, Lucas Mello Schnorr, and Philippe Olivier Alexandre Navaux. All the data, source code, and images generation scripts used in the paper are present here.</p>
Companion data of Summarizing task-based applications behavior over many nodes through progression clustering
No full textThis is the companion data for the paper *Summarizing task-based applications behavior over many nodes through progression clustering* by Lucas Leandro Nesi, Vinícius Garcia Pinto, Lucas Mello Schnorr, and Arnaud Legrand accept for publication in 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2023). The remaining of the companion is at: https://gitlab.com/lnesi/companion-pdp-2023
Companion data of Multi-Phase Task-Based HPC Applications: Quickly Learning how to Run Fast
No full textThis is the companion data repository for the paper entitled Multi-Phase Task-Based HPC Applications: Quickly Learning how to Run Fast by Lucas Leandro Nesi, Lucas Mello Schnorr, and Arnaud Legrand. The manuscript has been accepted for publication in the IPDPS 2022
Companion for "Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements"
No full textThis is the Companion Material for the paper “Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements”, by Ana Luisa Veroneze Solórzano and Lucas Mello Schnorr. The manuscript was approved for publication in the ISC High Performance 2022 for the Research Papers session. A public companion is also availabl in GitLab: https://gitlab.com/anaveroneze/isc2022-companion
Companion for "Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements"
No full textThis is the Companion Material for the paper “Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements”, by Ana Luisa Veroneze Solórzano and Lucas Mello Schnorr. The manuscript was approved for publication in the ISC High Performance 2022 for the Research Papers session. A public companion is also availabl in GitLab: https://gitlab.com/anaveroneze/isc2022-companion
Companion for "OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance"
No full textThis is the companion website for the paper entitled *OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance* by Marcelo Cogo Miletto and Lucas Mello Schnorr that has been submited to Simpósio de Sistemas
Computacionais de Alto Desempenho (WSCAD) - 2019. This repository provides all the data and the code snippets used to generate the figures that are discussed inside the article, in a way that you can use them to reproduce our steps.</p
Companion for "Providing In-depth Performance Analysis for Task-based Applications with StarVZ"
No full textThis is the software and dataset companion for the paper entitled "Providing In-depth Performance Analysis for Task-based Applications with StarVZ" by Vinícius Garcia Pinto, Lucas Leandro Nesi,
Marcelo Cogo Miletto, Lucas Mello Schnorr. The manuscript has been
submitted in February 8th for publication in the Heterogeneity in
Computing Workshop at IPDPS 2021 and accept in March. Further instructions can be found in the README.org file
<i>No se sabe</i>: entrevista a Lucas Gagliardi
No full textEntrevista al Licenciado y Profesor en Letras (UNLP) Lucas Gagliardi. Se especializa en literatura en lengua inglesa y en crítica genética. Se desempeña como profesor en la Universidad Pedagógica (UNIPE), en institutos de formación docente y escuelas secundarias. Ha participado en proyectos de investigación sobre archivos de escritores, publicaciones impresas. Participa en el programa de voluntariado universitario de la Facultad de Trabajo Social (UNLP) en articulación con la Biblioteca Ambulante del Hospital de Niños dictando talleres de lectura y escritura.Al hacer clic en el enlace que figura en "Documentos relacionados", pueden accederse a todos los trabajos de Lucas Gagliardi presentes en el repositorio.Radio Universidad Nacional de La Plat
Companion for "Temporal Load Imbalance on Ondes3D Seismic Simulator for Different Multicore Architectures"
No full text<p>This is the companion code/data repository for the paper entitled "Temporal Load Imbalance on Ondes3D Seismic Simulator for Different Multicore Architectures" by Ana Luisa Veroneze Solórzano, Philippe Olivier Alexandre Navaux, and Lucas Mello Schnorr. The manuscript has been submitted in September 2020 for publication in the HPCS Conference and accepted in February 2021.</p>
Gas volume fraction and velocity profiles: vertical and inclined bubbly air-water flows
No full textUpward inclined gas-liquid flows are frequently encountered in the oil industry and data relating to the local gas volume fraction distribution and the local gas velocity distribution is important, for example, in pressure gradient prediction and in modeling oil well 'blowouts'. In this paper measurements are presented of the local gas volume fraction distribution and the local axial gas velocity distribution which were taken in bubbly air-water flows in an 80 mm diameter pipe which was inclined at angles of 0°, 15° and 30° to the vertical. Qualitative arguments are presented to explain the influence of the liquid superficial velocity on the local gas volume fraction distribution in inclined flow and also to explain the very high axial gas velocities observed towards the upper side of the inclined pipe
- …
