Titlebook: High Performance Computing; 6th Latin American C Juan Luis Crespo-Mari?o,Esteban Meneses-Rojas Conference proceedings 2020 Springer Nature

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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

ESPY

Collaborative Development and Use of Scientific Applications in Orlando Tools: Integration, Delivery solve real problems. Such packages include a special class of scientific applications characterized by a wide class of problem solvers, modular structure of software, algorithmic knowledge implemented by modules, computations scalability, execution in heterogeneous resources, etc. It is adapted for

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Optimizing Big Data Network Transfers in FPGA SoC Clusters: TECBrain Case Studyundreds of MB to tens of GB per second) from and to a data host, usually a PC or a server. TECBrain is an SNN simulator which currently uses Ethernet for transmitting results from its simulations, which can potentially take hours if the effective connection speed is around 100?Mbps. This paper propo

Fillet,Filet

A Load Balancing Algorithm for Fog Computing Environmentsation closer to the sensors. However, given the increasing demand for numerous IoT applications, even when close to the sensors, Fog nodes tend to be overloaded, compromising the response times of IoT applications that have latency restrictions, and consequently compromising users’ quality experienc

Pert敏捷

Multi-objective Configuration of a Secured Distributed Cloud Data Storageal data storage infrastructures. However, it brings several cybersecurity issues. In this work, we address the methods of mitigating risks of confidentiality, integrity, availability, information leakage associated with the information loss/change, technical failures, and denial of access. We rely o

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Bounding Volume Hierarchy Acceleration Through Tightly Coupled Heterogeneous Computingade to optimize the BVH algorithm for GPU and CPU architectures. Nonetheless, as far as we know, no study has targeted the APU (Accelerated Processing Unit) that have a CPU and an integrated GPU in the same die. The APU has the advantage of being able to share workloads within its internal processor

SYN

Towards a Lightweight Method to Predict the Performance of Sparse Triangular Solvers on Heterogeneouields and the considerable computational cost of this operation have motivated several efforts to accelerate it on different hardware platforms and, in particular, on those equipped with massively-parallel processors. Until recently, the dominant approach to parallelize this operation on this sort o

Water-Brash

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