Suchergebnisse

The Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation (TANGO) project's goal is to characterise factors which affect power consumption in software development and operation for Heterogeneous Parallel Hardware (HPA) environments. Its main contribution is the combination of requirements engineering and design modelling for self-adaptive software systems, with power consumption awareness in relation to these environments. The energy efficiency and application quality factors are integrated into the application lifecycle (design, implementation and operation). To support this, the key novelty of the project is a reference architecture and its implementation. Moreover, a programming model with built-in support for various hardware architectures including heterogeneous clusters, heterogeneous chips and programmable logic devices is provided. This leads to a new cross-layer programming approach for heterogeneous parallel hardware architectures featuring software and hardware modelling. Application power consumption and performance, data location and time-criticality optimization, as well as security and dependability requirements on the target hardware architecture are supported by the architecture. ; This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014- SGR-1051 and 2014-SGR-1272. ; Peer Reviewed ; Postprint (author's final draft)

This document aims to spearhead a Europe-wide discussion on HPC system resilience and to help the European HPC community define best practices for resilience. We analyse a wide range of state-of-the-art resilience mechanisms and recommend the most effective approaches to employ in large-scale HPC systems. Our guidelines will be useful in the allocation of available resources, as well as guiding researchers and research funding towards the enhancement of resilience approaches with the highest priority and utility. Although our work is focused on the needs of next generation HPC systems in Europe, the principles and evaluations are applicable globally. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the projects ECOSCALE (grant agreement No 671632), EPI (grant agreement No 826647), EuroEXA (grant agreement No 754337), Eurolab4HPC (grant agreement No 800962), EVOLVE (grant agreement No 825061), EXA2PRO (grant agreement No 801015), ExaNest (grant agreement No 671553), ExaNoDe (grant agreement No 671578), EXDCI-2 (grant agreement No 800957), LEGaTO (grant agreement No 780681), MB2020 (grant agreement No 779877), RECIPE (grant agreement No 801137) and SDK4ED (grant agreement No 780572). The work was also supported by the European Commission's Seventh Framework Programme under the projects CLERECO (grant agreement No 611404), the NCSA-Inria-ANL-BSC-JSCRiken-UTK Joint-Laboratory for Extreme Scale Computing – JLESC (https://jlesc.github.io/), OMPI-X project (No ECP-2.3.1.17) and the Spanish Government through Severo Ochoa programme (SEV-2015-0493). This work was sponsored in part by the U.S. Department of Energy's Office of Advanced Scientific Computing Research, program managers Robinson Pino and Lucy Nowell. This manuscript has been authored by UT-Battelle, LLC under Contract No DE-AC05-00OR22725 with the U.S. Department of Energy. ; Preprint

The Performance Monitoring Unit (PMU) in MPSoCs is at the heart of the latest measurement-based timing analysis techniques in Critical Embedded Systems. In particular, hardware event monitors (HEMs) in the PMU are used as building blocks in the process of budgeting and verifying software timing by tracking and controlling access counts to shared resources. While the number of HEMs in current MPSoCs reaches hundreds, they are read via Performance Monitoring Counters whose number is usually limited to 4-8, thus requiring multiple runs of each experiment in order to collect all desired HEMs. Despite the effort of engineers in controlling the execution conditions of each experiment, the complexity of current MPSoCs makes it arguably impossible to completely remove the noise affecting each run. As a result, HEMs read in different runs are subject to different variability, and hence, those HEMs captured in different runs cannot be 'blindly' merged. In this work, we focus on the NXP T2080 platform where we observed up to 59% variability across different runs of the same experiment for some relevant HEMs (e.g. processor cycles). We develop a HEM reading and merging (HRM) approach to join reliably HEMs across different runs as a fundamental element of any measurement-based timing budgeting and verification technique. Our method builds on order statistics and the selection of an anchor HEM read in all runs to derive the most plausible combination of HEM readings that keep the distribution of each HEM and their relationship with the anchor HEM intact. ; This work has been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773) and the HiPEAC Network of Excellence. ; Peer Reviewed ; Postprint (author's final draft)

High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes (i) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; (ii) Lemonade, a data mining and analysis tool; and (iii) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs's use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction. ; Funding This work was partially funded by Fapemig, CAPES, CNPq, MCT/CNPq-InWeb, FAPEMIG-PRONEX-MASWeb (APQ-01400-14), and by the collaboration between Brazilian MCT/RNP and the European Union Horizon 2020 research and innovation programme under grants 690116 (EUBra-BIGSEA) and 777154 (EUBra Atmosphere). ; Peer Reviewed ; Postprint (published version)

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; Current trends and projections show that faults in computer systems become increasingly common. Such errors may be detected, and possibly corrected transparently, e.g. by Error Correcting Codes (ECC). For a program to be fault-tolerant, it needs to also handle the Errors that are Detected and Uncorrected (DUE), such as an ECC encountering too many bit flips in a codeword. While correcting an error has an overhead in itself, it can also affect the progress of a program. The most generic technique, rolling back the program state to a previously taken checkpoint, sets back any progress done since then. Alternately, application specific techniques exist, such as restarting an iterative program with its latest iteration's values as initial guess. ; This manuscript is the journal extension of a previously published conference paper [25]. This work has been partially supported by the European Research Council under the European Union's 7th FP, ERC Advanced Grant 321253, and by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P. L. Jaulmes has been partially supported by the Spanish Ministry of Education, Culture and Sports under grant FPU2013/06982. M. Moretó has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship JCI-2012-15047. M. Casas has been partially supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Co-fund programme of the Marie Curie Actions of the European Union's 7th FP (contract 2013 BP B 00243). We would like to thank Nicolas Vidal for his contribution on using huge pages natively. ; Peer Reviewed ; Postprint (author's final draft)

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; Current trends and projections show that faults in computer systems become increasingly common. Such errors may be detected, and possibly corrected transparently, e.g. by Error Correcting Codes (ECC). For a program to be fault-tolerant, it needs to also handle the Errors that are Detected and Uncorrected (DUE), such as an ECC encountering too many bit flips in a codeword. While correcting an error has an overhead in itself, it can also affect the progress of a program. The most generic technique, rolling back the program state to a previously taken checkpoint, sets back any progress done since then. Alternately, application specific techniques exist, such as restarting an iterative program with its latest iteration's values as initial guess. ; This manuscript is the journal extension of a previously published conference paper [25]. This work has been partially supported by the European Research Council under the European Union's 7th FP, ERC Advanced Grant 321253, and by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P. L. Jaulmes has been partially supported by the Spanish Ministry of Education, Culture and Sports under grant FPU2013/06982. M. Moretó has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship JCI-2012-15047. M. Casas has been partially supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Co-fund programme of the Marie Curie Actions of the European Union's 7th FP (contract 2013 BP B 00243). We would like to thank Nicolas Vidal for his contribution on using huge pages natively. ; Peer Reviewed ; Postprint (author's final draft)

This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). This work was also partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-689878). Finally, the authors are grateful to the reviewers for their valuable comments, to the RoMoL team, to Xavier Teruel and Kallia Chronaki from the Programming Models group of BSC and the Computation Department of LLNL for their technical support and useful feedback. ; Peer Reviewed ; Postprint (published version)

This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). This work was also partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-689878). Finally, the authors are grateful to the reviewers for their valuable comments, to the RoMoL team, to Xavier Teruel and Kallia Chronaki from the Programming Models group of BSC and the Computation Department of LLNL for their technical support and useful feedback. ; Peer Reviewed ; Postprint (published version)

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. In this work, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work. ; This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number 66905, program manager Lucy Nowell. Pacific Northwest National Laboratory is operated by Battelle for DOE under Contract DE-AC05-76RL01830. In addition, this material is based upon work supported by the National Science Foundation under Grant No. 1619253, and also by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, program manager Lucy Nowell, under contract number DE-AC02-06CH11357 (DOE Catalog project) and in part by the European Union FEDER funds under contract TIN2015-65316-P. ; Peer Reviewed ; Postprint (author's final draft)

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. In this work, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work. ; This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number 66905, program manager Lucy Nowell. Pacific Northwest National Laboratory is operated by Battelle for DOE under Contract DE-AC05-76RL01830. In addition, this material is based upon work supported by the National Science Foundation under Grant No. 1619253, and also by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, program manager Lucy Nowell, under contract number DE-AC02-06CH11357 (DOE Catalog project) and in part by the European Union FEDER funds under contract TIN2015-65316-P. ; Peer Reviewed ; Postprint (author's final draft)

The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 800898. This work has been supported by the Spanish Government (contracts SEV2015- 0493 and TIN2015-65316-P), by the Generalitat de Catalunya (contract 2014-SGR-1051). The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (IM-2020-1-0016). ; Peer Reviewed ; Postprint (published version)

Task-based programming has proven to be a suitable model for high-performance computing (HPC) applications. Different implementations have been good demonstrators of this fact and have promoted the acceptance of task-based programming in the OpenMP standard. Furthermore, in recent years, Apache Spark has gained wide popularity in business and research environments as a programming model for addressing emerging big data problems. COMP Superscalar (COMPSs) is a task-based environment that tackles distributed computing (including Clouds) and is a good alternative for a task-based programming model for big data applications. This article describes why we consider that task-based programming models are a good approach for big data applications. The article includes a comparison of Spark and COMPSs in terms of architecture, programming model, and performance. It focuses on the differences that both frameworks have in structural terms, on their programmability interface, and in terms of their efficiency by means of three widely known benchmarking kernels: Wordcount, Kmeans, and Terasort. These kernels enable the evaluation of the more important functionalities of both programming models and analyze different work flows and conditions. The main results achieved from this comparison are (1) COMPSs is able to extract the inherent parallelism from the user code with minimal coding effort as opposed to Spark, which requires the existing algorithms to be adapted and rewritten by explicitly using their predefined functions, (2) it is an improvement in terms of performance when compared with Spark, and (3) COMPSs has shown to scale better than Spark in most cases. Finally, we discuss the advantages and disadvantages of both frameworks, highlighting the differences that make them unique, thereby helping to choose the right framework for each particular objective. ; This work is supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). Javier Conejero's postdoctoral contract is cofinanced by the Ministry of Economy and Competitiveness under the Juan de la Cierva Formación postdoctoral fellowship number FJCI-2015-24651. This work is also supported by the Intel-BSC Exascale Lab. The Human Brain Project receives funding from the EU's Seventh Framework Programme (FP7/2007- 2013) under grant agreement no 604102. ; Peer Reviewed ; Postprint (author's final draft)

Task-based programming has proven to be a suitable model for high-performance computing (HPC) applications. Different implementations have been good demonstrators of this fact and have promoted the acceptance of task-based programming in the OpenMP standard. Furthermore, in recent years, Apache Spark has gained wide popularity in business and research environments as a programming model for addressing emerging big data problems. COMP Superscalar (COMPSs) is a task-based environment that tackles distributed computing (including Clouds) and is a good alternative for a task-based programming model for big data applications. This article describes why we consider that task-based programming models are a good approach for big data applications. The article includes a comparison of Spark and COMPSs in terms of architecture, programming model, and performance. It focuses on the differences that both frameworks have in structural terms, on their programmability interface, and in terms of their efficiency by means of three widely known benchmarking kernels: Wordcount, Kmeans, and Terasort. These kernels enable the evaluation of the more important functionalities of both programming models and analyze different work flows and conditions. The main results achieved from this comparison are (1) COMPSs is able to extract the inherent parallelism from the user code with minimal coding effort as opposed to Spark, which requires the existing algorithms to be adapted and rewritten by explicitly using their predefined functions, (2) it is an improvement in terms of performance when compared with Spark, and (3) COMPSs has shown to scale better than Spark in most cases. Finally, we discuss the advantages and disadvantages of both frameworks, highlighting the differences that make them unique, thereby helping to choose the right framework for each particular objective. ; This work is supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). Javier Conejero's postdoctoral contract is cofinanced by the Ministry of Economy and Competitiveness under the Juan de la Cierva Formación postdoctoral fellowship number FJCI-2015-24651. This work is also supported by the Intel-BSC Exascale Lab. The Human Brain Project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 604102. ; Peer Reviewed ; Postprint (author's final draft)

In this study mathematical models are developed to study the opportunity provided by the extended warranty period for both two-dimensional and one-dimensional case for the consumer and the manufacturer of the product for different situations. We will express the total average cost incurred along the life cycle of the product, this to the two points of view: the consumer and the manufacturer. Also we will consider various options regarding maintenance policies to adopt (performing preventive maintenance or not) for the product during the following periods: the basic warranty period, the extended warranty period and post-warranty at the end of the life cycle of the product. We will express the total average cost incurred along the life cycle of the product for the consumer and the manufacturer. To test the performance of the analytical model, numerical calculations were performed to determine the area of compromise for the extended warranty ; Nous nous proposons dans ce travail de développer des modèles mathématiques pour étudier l'opportunité apportée par la période de garantie étendue pour les deux cas unidimensionnel et bidimensionnel aussi bien pour le consommateur que pour le fabricant du produit pour différentes situations. Nous exprimerons pour cela le coût total moyen encouru le long du cycle de vie du produit, ceci pour les deux points de vue : celui du consommateur et celui du fabriquant. Nous considèrerons également différentes options concernant les politiques de maintenance à adopter (faire de la maintenance préventive ou non) pour le produit durant les périodes suivantes : la période de garantie de base, la période de garantie étendue et la période post-garantie se terminant à la fin du cycle de vie du produit. Nous exprimerons pour cela le coût total moyen encouru le long du cycle de vie du produit pour les points de vue consommateur et fabricant. Pour tester les performances du modèle analytique, des calculs numériques ont été effectués afin de déterminer la zone de compromis pour la garantie ...

In this study mathematical models are developed to study the opportunity provided by the extended warranty period for both two-dimensional and one-dimensional case for the consumer and the manufacturer of the product for different situations. We will express the total average cost incurred along the life cycle of the product, this to the two points of view: the consumer and the manufacturer. Also we will consider various options regarding maintenance policies to adopt (performing preventive maintenance or not) for the product during the following periods: the basic warranty period, the extended warranty period and post-warranty at the end of the life cycle of the product. We will express the total average cost incurred along the life cycle of the product for the consumer and the manufacturer. To test the performance of the analytical model, numerical calculations were performed to determine the area of compromise for the extended warranty ; Nous nous proposons dans ce travail de développer des modèles mathématiques pour étudier l'opportunité apportée par la période de garantie étendue pour les deux cas unidimensionnel et bidimensionnel aussi bien pour le consommateur que pour le fabricant du produit pour différentes situations. Nous exprimerons pour cela le coût total moyen encouru le long du cycle de vie du produit, ceci pour les deux points de vue : celui du consommateur et celui du fabriquant. Nous considèrerons également différentes options concernant les politiques de maintenance à adopter (faire de la maintenance préventive ou non) pour le produit durant les périodes suivantes : la période de garantie de base, la période de garantie étendue et la période post-garantie se terminant à la fin du cycle de vie du produit. Nous exprimerons pour cela le coût total moyen encouru le long du cycle de vie du produit pour les points de vue consommateur et fabricant. Pour tester les performances du modèle analytique, des calculs numériques ont été effectués afin de déterminer la zone de compromis pour la garantie ...