“…We present the main concepts of the ISF, which allows a fine-tuned detector simulation targeted at specific physics cases with a decrease in CPU time per event by orders of magnitude. Additionally, we will discuss the implications of a customised simulation in terms of validity and accuracy and will present new concepts in digitization and reconstruction to achieve a fast Monte Carlo chain with a per event execution time of a few seconds.…”
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“…The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. …”
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“…Therefore the Jet trigger software requires CPU-intensive reconstruction algorithms. In order to reduce the resources needed for the reconstruction step, a partial detector readout scheme was developed, which effectively suppresses the low activity regions of the calorimeter. …”
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“…Input and output control and data flows, bookkeeping, monitoring, and data storage are all managed at the event level in an implementation capable of supporting ATLAS-scale distributed processing throughputs (about 4M CPU-hours/day). Input data flows utilize remote data repositories with no data locality or pre­staging requirements, minimizing the use of costly storage in favor of strongly leveraging powerful networks. …”
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“…Therefore the Jet trigger software requires CPU-intensive reconstruction algorithms. In order to reduce the resources needed for the reconstruction step, a partial detector readout scheme was developed, which effectively suppresses the low activity regions of the calorimeter. …”
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“…--HTML-->Next-gen computing (NGC) platform is a new architectural concept, which will greatly enhance application performance in modern data centers, and will fundamentally deal with following challenges: Application’s parallelization, like e.g. map/reduce, driven by rapidly growing number of CPU cores per socket Efficient use of resources, driven by diversity of hardware as well as power constrains Data center wide system consistency, addressing failure of the system components will be a norm not an exception The paradox of the IT industry is that hardware evolution has advanced at much faster pace than system software features. …”
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“…In particular, this requires the CPU-intensive reconstruction of tridimensional energy deposits in the ATLAS calorimeter to be used as the basic input to the jet finding algorithms. …”
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“…This allowed to respect the dependencies of algorithms by just organizing them in a well-defined execution path to be run on CPU. The evolution of the Gaudi framework into its concurrent incarnation, though, implies the necessity to split the execution path dynamically into subsets of algorithms to fill up efficiently the available computing resources. …”
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“…The authors demonstrate that FPGAs and their programming model reflect the needs of stream processing problems much better than traditional CPU or GPU architectures, making them well-suited for a wide variety of systems, from embedded systems performing sensor processing to large setups for Big Data number crunching. …”
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“…Simulation jobs have a high ratio of CPU computation to disk I/O and have been shown to scale well in multi-threading and across many nodes. …”
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“…Selected results from the validation of the physics outputs on these ARM 64-bit servers will be reported. CPU, memory and IO intensive benchmarks using ATLAS specific environment and infrastructure have been performed, with a particular emphasis on the performance vs. energy consumption.…”
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“…To meet a sharply increasing demand for computing resources for LHC Run 2, ATLAS distributed computing systems reach far and wide to gather CPU resources and storage capacity to execute an evolving ecosystem of production and analysis workflow tools. …”
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“…ATLAS simulation jobs have a high ratio of CPU computation to disk I/O and have been shown to scale well in multi-threading and across many nodes. …”
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“…Over the course of the test, more than 500000 CPU-hours of processing time have been provided to ATLAS, which is roughly equivalent to the combined computing power of the two ATLAS clusters at the University of Bern. …”
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“…Some of the topics highlighted introduce the reader to selected problems with comments on numerical methods and implementation on modern platforms including CPU-GPU computing. Developed from lectures on mathematical physics in astronomy to advanced undergraduate and beginning graduate students, this book will be a valuable guide for students and a useful reference for practicing researchers. …”
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“…It achieves high utilization by combining dynamic job definition based upon many criteria, such as input and output size, memory requirements and CPU consumption, with manageable scheduling policies and by supporting different kinds of computational resources, such as GRID, clouds, supercomputers and volunteer computers. …”
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“…The features of the algorithm, as well as an analysis of the CPU requirements in the presence of large pileup, are presented.…”
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“…The CMS experiment has an HTCondor Global Pool, composed of more than 200K CPU cores available for Monte Carlo production and the analysis of data. …”
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“…A further extension to 17 PB gross storage and 1800 CPU cores are already in the pipeline. In 2018 the EOS service has been migrated to the new Citrine release. …”
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“…Estimations of the CPU resources that will be needed to produce simulated data for the future runs of the ATLAS experiment at the LHC indicate a compelling need to speed-up the process to reduce the computational time required. …”
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