Suchergebnisse

High-performance computing (HPC) systems are very big and powerful systems, with the main goal of achieving maximum performance of parallel jobs. Many dynamic factors influence the performance which makes this goal a non-trivial task. According to our knowledge, there is no standard tool to automatize performance evaluation through comparing different configurations and helping system administrators to select the best scheduling policy or the best job scheduler. This paper presents the Dynamic Job Scheduler Benchmark (DJSB). It is a configurable tool that compares performance metrics for different scenarios. DJSB receives a workload description and some general arguments such as job submission commands and generates performance metrics and performance plots. To test and present DJSB, we have compared three different scenarios with dynamic resource management strategies using DJSB experiment-driven tool. Results show that just changing some DJSB arguments we can set up and execute quite different experiments, making easy the comparison. In this particular case, a cooperative-dynamic resource management is evaluated compared with other resource management approaches. ; This work is supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (project TIN2015-65316-P), by the Generalitat de Catalunya (grant 2014-SGR-1051), by the European Union's Horizon 2020 research and innovation program under grant agreement No. 720270 (HBP SGA1). ; Peer Reviewed ; Postprint (author's final draft)

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources. ; This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration. ; Peer Reviewed ; Postprint (published version)

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources. ; This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration. ; Peer Reviewed ; Postprint (published version)