awesome/awesome-go
1
0
Fork 0
mirror of https://github.com/avelino/awesome-go.git synced 2024-12-12 17:13:58 +00:00
Code Issues Packages Projects Releases Wiki Activity

Added DRMAA job submission library for cluster schedulers. (#740)

Browse Source
This commit is contained in:
Daniel Gruber 2017-06-16 21:43:39 +02:00 committed by Avelino
parent b691d04377
commit 9aa128813a
1 changed files with 1 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -378,6 +378,7 @@ Please take a quick gander at the [contribution guidelines](https://github.com/a
*Packages that help with building Distributed Systems.* *Packages that help with building Distributed Systems.*
* [celeriac](https://github.com/svcavallar/celeriac.v1) - A library for adding support for interacting and monitoring Celery workers, tasks and events in Go * [celeriac](https://github.com/svcavallar/celeriac.v1) - A library for adding support for interacting and monitoring Celery workers, tasks and events in Go
* [drmaa](https://github.com/dgruber/drmaa) - Job submission library for cluster schedulers based on the DRMAA standard.
* [flowgraph](https://github.com/vectaport/flowgraph) - MPI-style ready-send coordination layer. * [flowgraph](https://github.com/vectaport/flowgraph) - MPI-style ready-send coordination layer.
* [gleam](https://github.com/chrislusf/gleam) - Fast and scalable distributed map/reduce system written in pure Go and Luajit, combining Go's high concurrency with Luajit's high performance, runs standalone or distributed. * [gleam](https://github.com/chrislusf/gleam) - Fast and scalable distributed map/reduce system written in pure Go and Luajit, combining Go's high concurrency with Luajit's high performance, runs standalone or distributed.
* [glow](https://github.com/chrislusf/glow) - Easy-to-Use scalable distributed big data processing, Map-Reduce, DAG execution, all in pure Go. * [glow](https://github.com/chrislusf/glow) - Easy-to-Use scalable distributed big data processing, Map-Reduce, DAG execution, all in pure Go.