Javier Conejero

EDBT 2026 Demo / reviewers in the wild / expert

dblp:119/4519 · DBLP profile ↗

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14ranked-venue papers

6as first author

5since 2021 · last 2024

0000-0001-6401-6229ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 10 · 3 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author · 1 since 2021Software engineering, systems software and programming languages · 2 · 1 first-author · 1 since 2021

Year	Publication	Venue	Position
2024	GPU Cache System for COMPSs: A Task-Based Distributed Computing Framework Cristian Tatu, Javier Conejero, Fernando Vázquez-Novoa, Rosa M. Badia	Euro-Par (3)	2
2023	Hierarchical Management of Extreme-Scale Task-Based Applications Francesc Lordan, Gabriel Puigdemunt, Pere Vergés, Javier Conejero, Jorge Ejarque, Rosa M. Badia	Euro-Par	4
2023	Scalable Random Forest with Data-Parallel Computing Fernando Vázquez-Novoa, Javier Conejero, Cristian Tatu, Rosa M. Badia	Euro-Par	2
2023	The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase abstract The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026). Arnau Folch, Claudia Abril, Michael Afanasiev, Giorgio Amati, Michael Bader, Rosa M. Badia, Hafize B. Bayraktar, Sara Barsotti, Roberto Basili 0002, Fabrizio Bernardi, Christian Boehm, Beatriz Brizuela, Federico Brogi, Eduardo Cabrera, Emanuele Casarotti, Manuel Jesús Castro Díaz, Matteo Cerminara, Antonella Cirella, Alexey Cheptsov, Javier Conejero, Antonio Costa 0002, Marc de la Asunción, Josep de la Puente, Marco Djuric, Ravil Dorozhinskii, Gabriela Espinosa, Tomaso Esposti Ongaro, Joan Farnós, Nathalie Favretto-Cristini, Andreas Fichtner, Alexandre Fournier, Alice-Agnes Gabriel, Jean-Matthieu Gallard, Steven J. Gibbons, Sylfest Glimsdal, José Manuel González-Vida, José Gracia, Rose Gregorio, Natalia Gutiérrez, Benedikt Halldorsson, Okba Hamitou, Guillaume Houzeaux, Stephan Jaure, Mouloud Kessar, Lukas Krenz, Lion Krischer, Soline Laforet, Piero Lanucara, Bo Li 0147, Maria Concetta Lorenzino, Stefano Lorito, Finn Løvholt, Giovanni Macedonio, Jorge Macías Sánchez, Guillermo Marin, Beatriz Martínez Montesinos, Leonardo Mingari, Geneviève Moguilny, Vadim Montellier, Marisol Monterrubio Velasco, Georges-Emmanuel Moulard, Masaru Nagaso, Massimo Nazaria, Christoph Niethammer, Federica Pardini, Marta Pienkowska, Luca Pizzimenti, Natalia Poiata, Leonhard Rannabauer, Otilio Rojas, Juan Esteban Rodriguez, Fabrizio Romano, Oleksandr Rudyy, Vittorio Ruggiero, Philipp Samfass, Carlos Sánchez-Linares, Sabrina Sanchez, Laura Sandri, Antonio Scala, Nathanaël Schaeffer, Joseph Schuchart, Jacopo Selva, Amadine Sergeant, Angela Stallone, Matteo Taroni, Solvi Thrastarson, Manuel Titos, Nadia Tonelllo, Roberto Tonini, Thomas Ulrich, Jean-Pierre Vilotte, Malte Vöge, Manuela Volpe, Sara Aniko Wirp, Uwe Wössner	Future Gener. Comput. Syst.	20
2022	The BioExcel methodology for developing dynamic, scalable, reliable and portable computational biomolecular workflows abstract Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulation and analysis tools. Moreover, the need to execute the pipelines on distributed systems increases the complexity of these developments. To address these issues, we propose a methodology to simplify the implementation of these workflows on HPC infrastructures. It combines a library, the BioExcel Building Blocks (BioBBs), that allows scientists to implement biomolecular pipelines as Python scripts, and the PyCOMPSs programming framework which allows to easily convert Python scripts into task-based parallel workflows executed in distributed computing systems such as HPC clusters, clouds, containerized platforms, etc. Using this methodology, we have implemented a set of computational molecular workflows and we have performed several experiments to validate its portability, scalability, reliability and malleability. Jorge Ejarque, Pau Andrio, Adam Hospital, Javier Conejero, Daniele Lezzi, Josep Lluís Gelpí, Rosa M. Badia	e-Science	4
2020	Managing Failures in Task-Based Parallel Workflows in Distributed Computing Environments Jorge Ejarque, Marta Bertran, Javier Álvarez Cid-Fuentes, Javier Conejero, Rosa M. Badia	Euro-Par	4
2019	Workflow Environments for Advanced Cyberinfrastructure Platforms abstract Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance Computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it. Rosa M. Badia, Jorge Ejarque, Francesc Lordan, Daniele Lezzi, Javier Conejero, Javier Álvarez Cid-Fuentes, Yolanda Becerra 0001, Anna Queralt	ICDCS	5
2018	Boosting Atmospheric Dust Forecast with PyCOMPSs abstract Task-based programming is becoming a tool of large interest for boosting High-Performance Computing (HPC) and Big Data applications. In particular, COMP Superscalar (COMPSs), is showing to be an effective task-based programming model for distributed computing of Big Data applications within HPC environments. Applications like NMMB-MONARCH, which is a dust forecast application composed by a set of steps (being some of them binaries with or without MPI), are perfect candidates for PyCOMPSs, the Python binding of COMPSs. This paper describes the success story of the adaptation of the NMMB-MONARCH online multi-scale atmospheric dust model to PyCOMPSs in order to exploit its inherent parallelism with the minimal developer effort. The paper also includes an evaluation of this implementation in the Nord3 supercomputer, a scalability analysis and an in-depth behaviour study. The main results presented in this paper are: (1) PyCOMPSs is able to extract the parallelism from the NMMB-MONARCH application; (2) it is able to improve the dust forecasting in terms of performance when compared with previous versions, and (3) PyCOMPSs is able to interact and share the resources with MPI applications when included in the workflow as tasks. Finally, we present the keys for exporting the knowledge of this experience to other applications in order to benefit from using PyCOMPSs. Javier Conejero, Cristian Ramon-Cortes, Kim Serradell, Rosa M. Badia	eScience	1
2016	Analyzing Hadoop power consumption and impact on application QoS Javier Conejero, Omer F. Rana, Pete Burnap, Jeffrey Morgan, María Blanca Caminero, Carmen Carrión 0001	Future Gener. Comput. Syst.	1
2016	Formal performance evaluation of the Map/Reduce framework within cloud computing M. Carmen Ruiz, Diego Cazorla, Diego Pérez Leándrez, Javier Conejero	J. Supercomput.	4
2014	From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments Javier Conejero, María Blanca Caminero, Carmen Carrión 0001, Luis Tomás	Future Gener. Comput. Syst.	1
2013	Scaling Archived Social Media Data Analysis Using a Hadoop Cloud abstract Over recent years, there has been an emerging interest in supporting social media analysis for marketing, opinion analysis and understanding community cohesion. Social media data conforms to many of the categorisations attributed to "big-data" -- i.e. volume, velocity and variety. Generally analysis needs to be undertaken over large volumes of data in an efficient and timely manner. A variety of computational infrastructures have been reported to achieve this. We present the COSMOS platform supporting sentiment and tension analysis on Twitter data, and demonstrate how this platform can be scaled using the OpenNebula Cloud environment with Map/Reduce-based analysis using Hadoop. In particular, we describe the types of system configurations that would be most useful from a performance perspective -- i.e. how virtual machines in the infrastructure should be distributed to reduce variability in the analysis performance. We demonstrate the approach using a data set consisting of several million Twitter messages, analysed over two types of Cloud infrastructure. Javier Conejero, Pete Burnap, Omer F. Rana, Jeffrey Morgan	IEEE CLOUD	1
2013	Characterising the Power Consumption of Hadoop Clouds - A Social Media Analysis Case Study Javier Conejero, Omer F. Rana, Pete Burnap, Jeffrey Morgan, Carmen Carrión 0001, María Blanca Caminero	CLOSER	1
2012	Multilevel SLA-based QoS Support in Grids abstract The need of mechanisms to guarantee the Quality of Service (QoS) in Grid environments has empowered the interest on Service Level Agreements (SLAs), which represent the contract established by negotiation between users and service providers that must be accomplished. Consequently, the SLAs contain numerous terms related to the QoS expected. The aim of this paper is to address different QoS levels in Grid environments by using specific metrics on SLAs. These metrics represent the expected QoS by the user for the given service level terms. Hence, a characterization and classification of these terms, making a differentiation of QoS in levels, is needed in order to improve the number of fulfilled agreements. Our proposal is based on a WS-Agreement compliant architecture developed to provide support for SLAs in Grid environments, also providing a framework prepared for new terms and policy definitions. More precisely, the QoS expected by users is clearly defined in three levels. These levels are used to classify the compromise adquired by each SLA (depending on the expectations of the user that submitted it) and deal with the confidence that Grid resources provide. The evaluation on a real Grid testbed shows the efficiency of the proposal. Javier Conejero, Luis Tomás, María Blanca Caminero, Carmen Carrión 0001	ISPA	1