Modules avx512: Difference between revisions

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| align="center" | [https://arrow.apache.org arrow]
| align="center" | [https://arrow.apache.org arrow]
| align="center" | tools
| align="center" | tools
| align="center" | 0.11.1, 0.15.1, 0.16.0, 0.17.1, 2.0.0, 4.0.0, 5.0.0, 7.0.0, 8.0.0, 9.0.0, 10.0.1, 11.0.0, 12.0.1, 13.0.0
| align="center" | 0.11.1, 0.15.1, 0.16.0, 0.17.1, 1.0.0, 2.0.0, 4.0.0, 5.0.0, 7.0.0, 8.0.0, 9.0.0, 10.0.1, 11.0.0, 12.0.1, 13.0.0
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Apache Arrow is a cross-language development platform for in-memory data. Homepage: https://arrow.apache.org URL: https://arrow.apache.org Compatible modules: python/3.6.3, python/3.7.4, python/3.8.0 Keyword:tools<br /><br /><br /></div>
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Apache Arrow is a cross-language development platform for in-memory data. Homepage: https://arrow.apache.org URL: https://arrow.apache.org Compatible modules: python/3.6.3, python/3.7.4, python/3.8.0 Keyword:tools<br /><br /><br /></div>
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| align="center" | [https://github.com/libMesh/libmesh libmesh]
| align="center" | [https://github.com/libMesh/libmesh libmesh]
| align="center" | math
| align="center" | math
| align="center" | 1.2.1
| align="center" | 1.2.1, 1.7.1
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: The libMesh library provides a framework for the numerical simulation of partial differential equations using arbitrary unstructured discretizations on serial and parallel platforms. A major goal of the library is to provide support for adaptive mesh refinement (AMR) computations in parallel while allowing a research scientist to focus on the physics they are modeling. Homepage: https://github.com/libMesh/libmesh Keyword:math<br /><br /><br /></div>
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: The libMesh library provides a framework for the numerical simulation of partial differential equations using arbitrary unstructured discretizations on serial and parallel platforms. A major goal of the library is to provide support for adaptive mesh refinement (AMR) computations in parallel while allowing a research scientist to focus on the physics they are modeling. Homepage: https://github.com/libMesh/libmesh URL: https://github.com/libMesh/libmesh Keyword:math<br /><br /><br /></div>
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| align="center" | [https://bazaar.launchpad.net/~libomm-team/libomm/trunk/ libomm]
| align="center" | [https://bazaar.launchpad.net/~libomm-team/libomm/trunk/ libomm]
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| align="center" | [https://repast.github.io/ repasthpc]
| align="center" | [https://repast.github.io/ repasthpc]
| align="center" | bio
| align="center" | bio
| align="center" | 2.0, 2.2.0, 2.3.0
| align="center" | 2.2.0, 2.3.0
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: The Repast Suite is a family of advanced, free, and open source agent-based modeling and simulation platforms that have collectively been under continuous development for over 15 years: Repast for High Performance Computing 2.2.0, released on 30 September 2016, is a lean and expert-focused C++-based modeling system that is designed for use on large computing clusters and supercomputers. Homepage: https://repast.github.io/ URL: https://repast.github.io/ Keyword:bio<br /><br /><br /></div>
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: The Repast Suite is a family of advanced, free, and open source agent-based modeling and simulation platforms that have collectively been under continuous development for over 15 years: Repast for High Performance Computing 2.2.0, released on 30 September 2016, is a lean and expert-focused C++-based modeling system that is designed for use on large computing clusters and supercomputers. Homepage: https://repast.github.io/ URL: https://repast.github.io/ Keyword:bio<br /><br /><br /></div>
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