Titlebook: High Performance Computing; 4th Latin American C Esteban Mocskos,Sergio Nesmachnow Conference proceedings 2018 The Editor(s) (if applicable

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書目名稱High Performance Computing影響因子(影響力)




書目名稱High Performance Computing影響因子(影響力)學(xué)科排名




書目名稱High Performance Computing網(wǎng)絡(luò)公開度




書目名稱High Performance Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱High Performance Computing被引頻次




書目名稱High Performance Computing被引頻次學(xué)科排名




書目名稱High Performance Computing年度引用




書目名稱High Performance Computing年度引用學(xué)科排名




書目名稱High Performance Computing讀者反饋




書目名稱High Performance Computing讀者反饋學(xué)科排名

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Initial Experiences from TUPAC Supercomputerfacilities in a country is an effort that should be carefully used and must be as open as possible to strengthen the impact of these resources..We describe the current status and characteristics of TUPAC supercomputer, which is hosted by a CONICET research institute. Unlike other experiences in Arge

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romeoLAB: A High Performance Training Platform for HPC, GPU and DeepLearningogeneous architectures. The full hybrid cluster . has been used in that purpose since a long time in order to train master students and cluster users. The main issue for trainees is the cost of accessing and exploiting a production facility in a pedagogic context. The use of some specific techniques

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Analysis and Characterization of GPU Benchmarks for Kernel Concurrency Efficiencyed to CPUs. However, programming GPUs requires a different mindset and optimization techniques that take advantage of the peculiarities of the GPU architecture. Moreover, GPUs are rapidly changing, in the sense of including capabilities that can improve performance of general purpose applications, s

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Evaluating the NVIDIA Tegra Processor as a Low-Power Alternative for Sparse GPU Computationsis the necessity to contemplate energy consumption restrictions. As an alternative for reducing the power consumption of large clusters, new systems that include unconventional devices have been proposed. In particular, it is now common to encounter energy-efficient hardware such as GPUs and low-pow

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Benchmarking Performance: Influence of Task Location on Cluster Throughputf parallelism, dedicated computational resources, strong- and weak-scaling behaviour, to cite some). This situation causes scheduling decisions to have a great influence on the performance of the applications, making difficult to achieve an optimal exploitation with cost-effective strategies of the

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PRIMULA: A Framework Based on Finite Elements to Address Multi Scale and Multi Physics Problemserential equations is presented. PRIMULA is portable to LINUX/UNIX, where it is compiled with gfortran, and to WINDOWS, compiled in the Visual Studio environment. It can be compiled to run in series, with shared memory under the Standard OPENMP, in a distributed environment under Standard MPI and on