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202 results on '"Meneses, Esteban"'

1. A Study of Performance Portability in Plasma Physics Simulations

Author

Ruzicka, Josef, Asch, Christian, Meneses, Esteban, Rampp, Markus, and Laure, Erwin

Subjects
Physics - Plasma Physics, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance, Physics - Computational Physics, 68Q85, D.1.3
Abstract

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it is fundamental to consider performance portability frameworks, i.e., software tools that allow programmers to write code once and run it on different computer architectures without sacrificing performance. We report here on the benefits and challenges of performance portability using a field-line tracing simulation and a particle-in-cell code, two relevant applications in computational plasma physics with applications to magnetically-confined nuclear-fusion energy research. For these applications we report performance results obtained on four HPC platforms with server-class CPUs from Intel (Xeon) and AMD (EPYC), and high-end GPUs from Nvidia and AMD, including the latest Nvidia H100 GPU and the novel AMD Instinct MI300A APU. Our results show that both Kokkos and OpenMP are powerful tools to achieve performance portability and decent "out-of-the-box" performance, even for the very latest hardware platforms. For our applications, Kokkos provided performance portability to the broadest range of hardware architectures from different vendors., Comment: 15 pages, 8 figures, this is a pre-print to be published in the Latin America High Performance Computing Conference (CARLA) 2024 proceedings

Published
2024

2. Evaluation of Alternatives to Accelerate Scientific Numerical Calculations on Graphics Processing Units Using Python

Author

Villalobos, Johansell, Meneses, Esteban, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barrios H., Carlos J., editor, Rizzi, Silvio, editor, Meneses, Esteban, editor, Mocskos, Esteban, editor, Monsalve Diaz, Jose M., editor, and Montoya, Javier, editor

Published
2024
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3. OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoringin Costa Rica

Author

van der Laat, Leonardo, Baldares, Ronald J. L., Chaves, Esteban J., and Meneses, Esteban

Subjects
Physics - Geophysics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern DL algorithms along with the increasing computational power are exploiting the continuously growing seismological databases, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention. In this work, we introduce OKSP, a novel automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabre supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.

Published
2021

6. A Study of Checkpointing in Large Scale Training of Deep Neural Networks

Author

Rojas, Elvis, Kahira, Albert Njoroge, Meneses, Esteban, Gomez, Leonardo Bautista, and Badia, Rosa M

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Deep learning (DL) applications are increasingly being deployed on HPC systems, to leverage the massive parallelism and computing power of those systems for DL model training. While significant effort has been put to facilitate distributed training by DL frameworks, fault tolerance has been largely ignored. In this work, we evaluate checkpoint-restart, a common fault tolerance technique in HPC workloads. We perform experiments with three state-of-the-art DL frameworks common in HPC Chainer, PyTorch, and TensorFlow). We evaluate the computational cost of checkpointing, file formats and file sizes, the impact of scale, and deterministic checkpointing. Our evaluation shows some critical differences in checkpoint mechanisms and exposes several bottlenecks in existing checkpointing implementations. We provide discussion points that can aid users in selecting a fault-tolerant framework to use in HPC. We also provide takeaway points that framework developers can use to facilitate better checkpointing of DL workloads in HPC.

Published
2020

7. The impact of school reopening strategies during COVID-19: A case study of Sao Paulo, Brazil

Author

Cruz, Eduardo H. M., Maciel, João M., Clozato, Camila, Serpa, Matheus S., Navaux, Philippe O. A., Meneses, Esteban, Abdalah, Mariela, and Diener, Matthias

Subjects
Quantitative Biology - Populations and Evolution
Abstract

During the COVID-19 pandemic, many countries opted for strict public health measures, including closing schools. They have now started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using computer simulation, we analyze different strategies to reopen schools in the Sao Paulo Metropolitan Area, including the official reopening plan. Our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, a controlled school reopening avoids the collapse of the health system, with a maximum ICU occupancy between 48.8% and 97.8%, depending on how people attending school follow sanitary measures. Postponing the schools' return date for after a vaccine becomes available can save up to 37,753 lives just in the Sao Paulo Metropolitan Area.

Published
2020

8. Understanding COVID-19 Epidemic in Costa Rica Through Network-Based Modeling

Author

Abdalah, Mariela, Soto, Cristina, Arce, Melissa, Cruz, Eduardo, Maciel, Jöao, Clozato, Camila, Meneses, Esteban, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gitler, Isidoro, editor, Barrios Hernández, Carlos Jaime, editor, and Meneses, Esteban, editor

Published
2022
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10. Implementing a GPU-Portable Field Line Tracing Application with OpenMP Offload

Author

Jiménez, Diego, Herrera-Mora, Javier, Rampp, Markus, Laure, Erwin, Meneses, Esteban, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Navaux, Philippe, editor, Barrios H., Carlos J., editor, Osthoff, Carla, editor, and Guerrero, Ginés, editor

Published
2022
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12. Calibration of Traffic Simulations Using Simulated Annealing and GPS Navigation Records

Author

Gamboa-Venegas, Carlos, Gómez-Campos, Steffan, Meneses, Esteban, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lossio-Ventura, Juan Antonio, editor, Valverde-Rebaza, Jorge, editor, Díaz, Eduardo, editor, Muñante, Denisse, editor, Gavidia-Calderon, Carlos, editor, Valejo, Alan Demétrius Baria, editor, and Alatrista-Salas, Hugo, editor

Published
2022
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14. On the Detection of Silent Data Corruptions in HPC Applications Using Redundant Multi-threading

Author

Pérez, Diego, Ropars, Thomas, Meneses, Esteban, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Balis, Bartosz, editor, B. Heras, Dora, editor, Antonelli, Laura, editor, Bracciali, Andrea, editor, Gruber, Thomas, editor, Hyun-Wook, Jin, editor, Kuhn, Michael, editor, Scott, Stephen L., editor, Unat, Didem, editor, and Wyrzykowski, Roman, editor

Published
2021
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16. Analyzing Communication Features and Community Structure of HPC Applications

Author

Calvo, Manfred, Jiménez, Diego, Meneses, Esteban, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Washio, Takashi, Founding Editor, Meneses, Esteban, editor, Castro, Harold, editor, Barrios Hernández, Carlos Jaime, editor, and Ramos-Pollan, Raul, editor

Published
2019
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17. Modelling Road Saturation Dynamics on a Complex Transportation Network Based on GPS Navigation Software Data

Author

Cubero-Corella, Mariana, Durán-Monge, Esteban, Díaz, Warner, Meneses, Esteban, Gómez-Campos, Steffan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Crespo-Mariño, Juan Luis, editor, and Meneses-Rojas, Esteban, editor

Published
2020
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18. BS-SOLCTRA: Towards a Parallel Magnetic Plasma Confinement Simulation Framework for Modular Stellarator Devices

Author

Jiménez, Diego, Campos-Duarte, Luis, Solano-Piedra, Ricardo, Araya-Solano, Luis Alonso, Meneses, Esteban, Vargas, Iván, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Crespo-Mariño, Juan Luis, editor, and Meneses-Rojas, Esteban, editor

Published
2020
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19. Towards a Model to Estimate the Reliability of Large-Scale Hybrid Supercomputers

Author

Rojas, Elvis, Meneses, Esteban, Jones, Terry, Maxwell, Don, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Malawski, Maciej, editor, and Rzadca, Krzysztof, editor

Published
2020
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22. A Large-Scale Epidemic Simulation Framework for Realistic Social Contact Networks

Author

Kitson, Joy, Costello, Ian, Chen, Jiangzhuo, Jiménez, Diego, Hoops, Stefan, Mortveit, Henning, Meneses, Esteban, Yeom, Jae-Seung, Marathe, Madhav V., Bhatele, Abhinav, Kitson, Joy, Costello, Ian, Chen, Jiangzhuo, Jiménez, Diego, Hoops, Stefan, Mortveit, Henning, Meneses, Esteban, Yeom, Jae-Seung, Marathe, Madhav V., and Bhatele, Abhinav

Abstract

Global pandemics can wreak havoc and lead to significant social, economic, and personal losses. Preventing the spread of infectious diseases requires implementing interventions at different levels of government, and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of epidemic diffusion and possible interventions. We present Loimos, a highly parallel simulation of epidemic diffusion written on top of Charm++, an asynchronous task-based parallel runtime. Loimos uses a hybrid of time-stepping and discrete-event simulation to model disease spread. We demonstrate that our implementation of Loimos is able to scale to large core counts on an HPC system. In particular, Loimos is able to simulate a US-scale synthetic interaction network in an average of 1.497 seconds per simulation day when executed on 16 nodes on Rivanna at the University of Virginia, processing around 428 billion interactions (person-person edges) in under five minutes for an average of 1.4 billion traversed edges per second (TEPS)., Comment: 13 pages (including references), 9 figures

Published
2024

23. Parallel Computing for Processing Data from Intelligent Transportation Systems

Author

Denis, Jonathan, Massobrio, Renzo, Nesmachnow, Sergio, Cristóbal, Alfredo, Tchernykh, Andrei, Meneses, Esteban, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Torres, Moisés, editor, and Klapp, Jaime, editor

Published
2019
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24. Exploring Application-Level Message-Logging in Scalable HPC Programs

Author

Meneses, Esteban, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Mocskos, Esteban, editor, and Nesmachnow, Sergio, editor

Published
2018
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29. Reducing the Overhead of Message Logging in Fault-Tolerant HPC Applications

Author

Meneses, Esteban, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Barrios Hernández, Carlos Jaime, editor, Gitler, Isidoro, editor, and Klapp, Jaime, editor

Published
2017
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30. From Knights Corner to Landing: A Case Study Based on a Hodgkin-Huxley Neuron Simulator

Author

Chatzikonstantis, George, Jiménez, Diego, Meneses, Esteban, Strydis, Christos, Sidiropoulos, Harry, Soudris, Dimitrios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kunkel, Julian M., editor, Yokota, Rio, editor, Taufer, Michela, editor, and Shalf, John, editor

Published
2017
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36. On the Use of Cluster-Based Partial Message Logging to Improve Fault Tolerance for MPI HPC Applications

Author

Ropars, Thomas, Guermouche, Amina, Uçar, Bora, Meneses, Esteban, Kalé, Laxmikant V., Cappello, Franck, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Jeannot, Emmanuel, editor, Namyst, Raymond, editor, and Roman, Jean, editor

Published
2011
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37. Early Experiences of Noise-Sensitivity Performance Analysis of a Distributed Deep Learning Framework

Author

Rojas, Elvis, Knobloch, Michael, Daoud, Nour, Meneses, Esteban, and Mohr, Bernd

Abstract

Deep Learning (DL) applications are used to solve complex problems efficiently. These applications require complex neural network models composed of millions of parameters and huge amounts of data for proper training. This is only possible by parallelizing the necessary computations by so-called distributed deep learning (DDL) frameworks over many GPUs distributed over multiple nodes of a HPC cluster. These frameworks mostly utilize the compute power of the GPUs and use only a small portion of the available compute power of the CPUs in the nodes for I/O and inter-process communication, leaving many CPU cores idle and unused. The more powerful the base CPU in the cluster nodes, the more compute resources are wasted. In this paper, we investigate how much of this unutilized compute resources could be used for executing other applications without lowering the performance of the DDL frameworks. In our experiments, we executed a noise-generation application, which generates a very-high memory, network or I/O load, in parallel with DDL frameworks, and use HPC profiling and tracing techniques to determine whether and how the generated noise is affecting the performance of the DDL frameworks. Early results indicate that it might be possible to utilize the idle cores for jobs of other users without affecting the performance of the DDL applications in a negative way.

Published
2022

42. Desde el sur del Cesar a la capital de Santander: La experiencia del desplazamiento forzado

Author

Castillo Niño, Joseph Vicent, Almeida Quintero, Claudia Paola, Meneses Esteban, Nathalia, Castillo Niño, Joseph Vicent, Almeida Quintero, Claudia Paola, and Meneses Esteban, Nathalia

Abstract

This article presents the findings of a qualitative research with a life history approach on the damage caused to victims of forced displacement from the south of the department of Cesar who reside in the Metropolitan Area of ​​Bucaramanga. For this, five people participated: three men from San Alberto and two women from Aguachica. The results show the emotional and psychological damage caused by feelings of fear and danger in the face of threats from armed groups. Within moral damages, the dignity of people is violated through stigmatization and the impossibility of performing religious rituals to commemorate their dead. Finally, in the socio-cultural damages, the reconfiguration of the family nucleus is mentioned, as well as the roles within it, passing to women's heads and a rupture of the social fabric of the victims., El presente artículo tiene como objetivo evidenciar las afectaciones en víctimas de desplazamiento forzado en el marco del conflicto social armado. La metodología fue de carácter cualitativa empleando la historia de vida como enfoque investigativo con personas víctimas de desplazamiento forzado en el marco del conflicto armado interno en Colombia provenientes desde sur del Cesar que actualmente viven en el Área Metropolitana de Bucaramanga. Los hallazgos son múltiples, se evidencian daños emocionales y psicológicos donde prevalecen sentimientos de peligro y zozobra; los daños morales se presentan como ataques a la dignidad de los sujetos, denigrando su vida siendo acusados y estigmatizados por parte de los grupos armados y en los daños socioculturales se manifiestan como la fragmentación de las estructuras familiares y del tejido social.

Published
2022

50. Understanding soft error sensitivity of deep learning models and frameworks through checkpoint alteration

Author

Barcelona Supercomputing Center, Rojas, Elvis, Pérez, Diego, Calhoun, Jon C., Bautista Gomez, Leonardo, Jones, Terry, Meneses, Esteban, Barcelona Supercomputing Center, Rojas, Elvis, Pérez, Diego, Calhoun, Jon C., Bautista Gomez, Leonardo, Jones, Terry, and Meneses, Esteban

Abstract

The convergence of artificial intelligence, high-performance computing (HPC), and data science brings unique opportunities for marked advance discoveries and that leverage synergies across scientific domains. Recently, deep learning (DL) models have been successfully applied to a wide spectrum of fields, from social network analysis to climate modeling. Such advances greatly benefit from already available HPC infrastructure, mainly GPU-enabled supercomputers. However, those powerful computing systems are exposed to failures, particularly silent data corruption (SDC) in which bit-flips occur without the program crashing. Consequently, exploring the impact of SDCs in DL models is vital for maintaining progress in many scientific domains. This paper uses a distinctive methodology to inject faults into training phases of DL models. We use checkpoint file alteration to study the effect of having bit-flips in different places of a model and at different moments of the training. Our strategy is general enough to allow the analysis of any combination of DL model and framework—so long as they produce a Hierarchical Data Format 5 checkpoint file. The experimental results confirm that popular DL models are often able to absorb dozens of bit-flips with a minimal impact on accuracy convergence, Peer Reviewed, Postprint (author's final draft)

Published
2021

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