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881por Svatos, Michal“…The system will need further modifications to improve CPU efficiency when running on worker nodes with very high number of cores. …”
Publicado 2021
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882por Chatain, Peter“…--HTML-->The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. …”
Publicado 2021
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883por Cruz Martínez, Juan M.“…The simulation is then performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. We show some preliminary results for leading-order simulations on different hardware configurations.…”
Publicado 2021
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884por Boehler, Michael“…The impact of the heterogeneous hardware of the Grid sites and the performance difference of different hardware generations is analysed in detail, and a direct, in depth comparison of jobs performed on identical CPU types is also done. The choice of the physics sample used in the benchmark is validated by comparing the performance on each Grid site measured with HammerCloud, weighted by its contribution to the total ATLAS full simulation production output.…”
Publicado 2022
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885por CMS Collaboration“…The physics validation and performance of the GPU-accelerated algorithms are demonstrated by comparing to the CPU-based implementation.…”
Publicado 2022
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886por Boehler, Michael“…The impact of the heterogeneous hardware of the Grid sites and the performance difference of different hardware generations is analysed in detail, and a direct, in depth comparison of jobs performed on identical CPU types is also done. The choice of the physics sample used in the benchmark is validated by comparing the performance on each Grid site measured with HammerCloud, weighted by its contribution to the total ATLAS full simulation production output.…”
Publicado 2023
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887“…This project focused on the algorithm used to cluster charge deposits in the CMS silicon strip tracker, for both the online and the offline event reconstruction, with the goal to enable its execution on any CPU or GPU architecture, in view of the future usage of the Alpaka library.…”
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888por Adams, D, Barberis, D, Bee, C P, Hawkings, R, Jarp, S, Jones, R, Malon, D, Poggioli, L, Poulard, G, Quarrie, D, Wenaus, T“…The data flow from the output of the ATLAS trigger system through processing and analysis stages is analysed, in order to estimate the computing resources, in terms of CPU power, disk and tape storage and network bandwidth, which will be necessary to guarantee speedy access to ATLAS data to all members of the Collaboration. …”
Publicado 2004
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889“…Further speed increases can be achieved by attaching special-purpose hardwired logic to the CPU. The architecture of MICE has been described elsewhere (HALAT80); in this paper we describe those features which make it a user-friendly device in the real-time experimental environment encountered at CERN.…”
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890“…It includes almost 200,000 CPU cores, 200 PB of disk storage and 200 PB of tape storage distributed among more than 150 sites. …”
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891por Thomas, Laurent“…The HLT is a farm made of approximately 30k CPU cores that reduces the rate from 100 kHz to about 1 kHz. …”
Publicado 2019
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892por Abramyan, Armenuhi, Manukyan, Narine, Hallyburton, Tim, Betev, Latchezar, Buncic, Predrag, Grigoryan, Ara“…The LHC experiments produce many tens of petabytes of data each year, which must be stored, processed and analyzed. The CPU and storage resources required for these activities are planned for and requested on yearly basis. …”
Publicado 2019
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893por Albertsson, Kim, Gleyzer, Sergei, Huwiler, Marc, Ilievski, Vladimir, Moneta, Lorenzo, Shekar, Saurav, Estrade, Victor, Vashistha, Akshay, Wunsch, Stefan, Zapata Mesa, Omar Andres“…Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convolutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. …”
Publicado 2019
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894“…These pre-processed data can then be used as seeds by the High Level Trigger (HLT) farm to find tracks for the Level 1 trigger with much lower computational effort than possible by starting from the raw detector data, thus freeing an important fraction of the power of the CPU farm for higher level processing tasks.…”
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895
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896
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897
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898por Tricoire, Fabien“…Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems.…”
Publicado 2012
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Online Artículo Texto -
899“…The two approaches are compared by measuring their total number of function evaluations and CPU times. The stability property of the method is examined. …”
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900“…In equivalent conditions, DAMMIF is 25–40 times faster than DAMMIN on a single CPU. The possibility to utilize multiple CPUs is added to DAMMIF. …”
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Online Artículo Texto