This page provides an overview of file systems at OSC. Each file system is configured differently to serve a different purpose:
This documentation is to discuss how to set up an interactive parallel COMSOL job at OSC. The following example demonstrates the process of using COMSOL version 5.1 on Oakley. Depending on the version of COMSOL and cluster you work on, there mighe be some differences from the example. Feel free to contact OSC Help if you have any questions.
This document shows you how to use the NFSv4 ACL permissions system. An ACL is a list of permissions associated with a file or directory. These permissions allow you to restrict access to a certian file or directory by user or group. NFSv4 ACLs provide more specific options than typical POSIX read/write/execute permissions used in most systems.
Understanding NFSv4 ACL
This is an example of an NFSv4 ACL
This "how to" will demonstrate how to lower ones' disk space usage. The following procedures can be applied to all of OSC's file systems.
We recommend users regularly check their data usage and clean out old data that is no longer needed.
Users who need assistance lowering their data usage can contact OSC Help.
Search our client documentation below, optionally filtered by one or more systems.
This documentation is to discuss how to run STAR-CCM+ to STAR-CCM+ Coupling simulation in batch job at OSC. The following example demonstrates the process of using STAR-CCM+ version 11.02.010 on Owens. Depending on the version of STAR-CCM+ and cluster you work on, there mighe be some differences from the example. Feel free to contact OSC Help if you have any questions.
The LAMMPS 14May16 known issue wherein parallel lammps spawned too many threads has been fixed on all clusters. No user action is required; if a user had applied the
OMP_NUM_THREADS workaround then it may be removed, but it will not cause probems if left in place. The corrected executables were made the defaults for module lammps/14may16 at these times:
Apache Spark is an open source cluster-computing framework originally developed in the AMPLab at University of California, Berkeley but was later donated to the Apache Software Foundation where it remains today. In contrast to Hadoop's disk-based analytics paradigm, Spark has multi-stage in-memory analytics. Spark can run programs upto 100x faster than Hadoop’s MapReduce in memory or 10x faster on disk. Spark support applications written in python, java, scala and R
Our current GPFS file system is a distributed process with significant interactions between the clients. As the compute nodes being GPFS flle system clients, a certain amount of memory of each node needs to be reserved for these interactions. As a result, the maximum physical memory of each node allowed to be used by users' jobs are reduced, in order to keep the healthy performance of the file system. In addition, using swap memory is not allowed anymore.
From WARP3D's webpage:
WARP3D is under continuing development as a research code for the solution of large-scale, 3-Dsolid models subjected to static and dynamic loads. The capabilities of the code focus on fatigue & fracture analyses primarily in metals. WARP3D runs on laptops-to-supercomputers and can analyze models with several million nodes and elements.
Availability and Restrictions
The following versions of WARP3D are available on OSC clusters: