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(Field of Science):
- information and communication technology (Engineering and Technology)
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- computer and information sciences (Natural sciences)
(Field of Science)
Ministry points: Help
| Year | Points | List |
|---|---|---|
| Year 2024 | 100 | Ministry scored journals list 2024 |
| Year | Points | List |
|---|---|---|
| 2024 | 100 | Ministry scored journals list 2024 |
| 2023 | 100 | Ministry Scored Journals List |
| 2022 | 100 | Ministry Scored Journals List 2019-2022 |
| 2021 | 100 | Ministry Scored Journals List 2019-2022 |
| 2020 | 100 | Ministry Scored Journals List 2019-2022 |
| 2019 | 100 | Ministry Scored Journals List 2019-2022 |
| 2018 | 25 | A |
| 2017 | 25 | A |
| 2016 | 20 | A |
| 2015 | 25 | A |
| 2014 | 25 | A |
| 2013 | 20 | A |
| 2012 | 25 | A |
| 2011 | 25 | A |
| 2010 | 27 | A |
Model:
Points CiteScore:
| Year | Points |
|---|---|
| Year 2022 | 4.2 |
| Year | Points |
|---|---|
| 2022 | 4.2 |
| 2021 | 3.8 |
| 2020 | 3.8 |
| 2019 | 3.4 |
| 2018 | 3.6 |
| 2017 | 3 |
| 2016 | 3.1 |
| 2015 | 2.9 |
| 2014 | 3.3 |
| 2013 | 3.2 |
| 2012 | 2.8 |
| 2011 | 3.7 |
Impact Factor:
Sherpa Romeo:
Papers published in journal
Filters
total: 6
Catalog Journals
Year 2023
-
A multithreaded CUDA and OpenMP based power‐aware programming framework for multi‐node GPU systems
PublicationIn the paper, we have proposed a framework that allows programming a parallel application for a multi-node system, with one or more GPUs per node, using an OpenMP+extended CUDA API. OpenMP is used for launching threads responsible for management of particular GPUs and extended CUDA calls allow to manage CUDA objects, data and launch kernels. The framework hides inter-node MPI communication from the programmer who can benefit from...
Year 2016
-
KernelHive: a new workflow-based framework for multilevel high performance computing using clusters and workstations with CPUs and GPUs
PublicationThe paper presents a new open-source framework called KernelHive for multilevel parallelization of computations among various clusters, cluster nodes, and finally, among both CPUs and GPUs for a particular application. An application is modeled as an acyclic directed graph with a possibility to run nodes in parallel and automatic expansion of nodes (called node unrolling) depending on the number of computation units available....
Year 2015
-
Optimizing the computation of a parallel 3D finite difference algorithm for graphics processing units
PublicationThis paper explores the possibilities of using a graphics processing unit for complex 3D finite difference computation via MUSTA‐FORCE and WENO algorithms. We propose a novel algorithm based on the new properties of CUDA surface memory optimized for 2D spatial locality and compare it with 3D stencil computations carried out via shared memory, which is currently considered to be the best approach. A case study was performed for...
Year 2007
-
Flexibility and user‐friendliness of Grid portals: the PROGRESS approach
Publication -
Plugging grids into computing portals: the PROGRESS grid resource broker plug‐in mechanism
Publication
Year 2006
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