Status and Developments of the CREAM Computing Element Service

Andreetto, Paolo; Bertocco, Sara; Capannini, Fabio; Cecchi, Marco; Dorigo, A.; Frizziero, Eric; Gianelle, A.; Giacomini, Francesco; Mezzadri, M.; Monforte, S.; Prelz, F.; Molinari, Elisa; Rebatto, David; Sgaravatto, M.; Zangrando, L.

doi:10.1088/1742-6596/331/6/062024

Cited by 6 publications

(3 citation statements)

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“…A snapshot of the EGI Grid Information System taken in October 2015 [1] has shown that the 75% of the 567 Compute Elements (CEs) composing the EGI HTC platform is based on CREAM (Computing Resource Execution And Management): a web service based CE for the UMD/gLite grid middleware [3]. CREAM has been since years the most popular grid interface in EGI for a number of Local Resource Management Systems (LRMS): TORQUE, LSF, SGE, Slurm, HTCondor.…”

Section: The Htc Accelerated Platformmentioning

confidence: 99%

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EGI federated platforms supporting accelerated computing

Andreetto¹,

Astalos²,

Dobrucký³

et al. 2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2017 — PoS(ISGC2017)

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While accelerated computing instances providing access to NVIDIA TM GPUs are already available since a couple of years in commercial public clouds like Amazon EC2, the EGI Federated Cloud has put in production its first OpenStack-based site providing GPU-equipped instances at the end of 2015. However, many EGI sites which are providing GPUs or MIC coprocessors to enable high performance processing are not directly supported yet in a federated manner by the EGI HTC and Cloud platforms. In fact, to use the accelerator cards capabilities available at resource centre level, users must directly interact with the local provider to get information about the type of resources and software libraries available, and which submission queues must be used to submit accelerated computing workloads. EU-funded project EGIEngage since March 2015 has worked to implement the support to accelerated computing on both its HTC and Cloud platforms addressing two levels: the information system, based on the OGF GLUE standard, and the middleware. By developing a common extension of the information system structure, it was possible to expose the correct information about the

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Section: The Htc Accelerated Platformmentioning

confidence: 99%

“…Considering that TORQUE is the most popular choice of LRMS in EGI for the CREAM CE instances, we started to work with a testbed composed by 3 …”

Section: The Htc Accelerated Platformmentioning

confidence: 99%

EGI federated platforms supporting accelerated computing

Andreetto¹,

Astalos²,

Dobrucký³

et al. 2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2017 — PoS(ISGC2017)

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“…The role of the CE is to provide services for the execution of jobs on Grid resources that are managed by a LRMS such as Torque, LSF or Condor. In this environment the Computing Resource Execution and Management (CREAM) [17] CE is the last CE implementation in the gLite middleware which exposes a gSOAP web service interface to other Grid components such as job management clients allowing standard (via WMS [16]) as well as direct submission from the adopted User Interface.…”

Section: A the Role Of Glite Cementioning

confidence: 99%

GPU Computing in EGI Environment Using a Cloud Approach

Vella

Cefal²,

Costantini

et al. 2011

2011 International Conference on Computational Science and Its Applications

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Recently GPU computing, namely the possibility to use the vector processors of graphics card as computational general purpose units of High Performance Computing environments, has generated considerable interest in the scientific community. Some communities in European Grid Infrastructure (EGI) are reshaping their applications to exploit this new programming paradigm. Each EGI community, called Virtual Organization (VO), often requires specific environments, making necessary for each Grid site to enable an efficient system to fulfill VO's software requirements. Cloud Computing and more generally the opportunity to transparently use computational resources, together with the consolidation of virtualization technologies, allows to provide to the end users the required environment for their activities. The present work is aimed at exploring the possibilities to provide for each VO an on-demand GPU environment (GPU framework, Operating System and libraries) and making it accessible via the EGI infrastructure using a Cloud approach. International Conference on Computational Science and Its Applications978-0-7695-4404-5/11 $25.00

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