Orcina news

This is a minor update release which fixes a few bugs that have come to light since the 6.0b release. The new release is available here: Distributed OrcaFlex 6.0c

The changes include:

• Bug fix: Sometimes, in the event of an error, the DOF Server would produce a cascade of error report files that caused the DOF Server to become unresponsive for a while. The DOF Server now only logs error details to the DOFServer.log file without generating any further error reports.
• Bug fix: When using the command line tool ‘dofcmd’ to submit jobs, specifying an auto-save interval of 0 was not allowed. This is in fact a valid interval used to disable the auto-save.
• Bug fix: If the DOF Server Service was restarted while jobs were still running on clients, then those jobs could end up being cancelled by the restarted DOF Server rather than re-added to the job list to continue as normal.
• Bug fix: If jobs were submitted whilst the DOF Server was already distributing jobs to DOF Clients then the scheduler’s ramping feature was re-initiated unnecessarily. Now, the ramping feature only starts if the jobs are added and the DOF Server is idle.
• In the client list view of the DOF Viewer, the list columns can now be resized.

We have just released Distributed OrcaFlex 6.0a which you can download from the Distributed OrcaFlex web page.

The significant change introduced in this release is the ability to run more than one Distributed OrcaFlex (DOF) Client process on the same computer. The motivation for the change is to support machines with very large numbers of processors. Machines with more than 64 processors have the processors split into processor groups. Each DOF Client can only use processors from a single group. For previous versions, there was a single DOF Client process per machine and so that process could only use processors from a single group. Allowing multiple DOF Client processes on a single machine enables full use of the processing capacity of that machine.

As well as the issue of processor groups, machines with large numbers of processors usually use NUMA memory architecture. In order to make the best use of such machines, programs need to make sure that memory is allocated on the NUMA node which contains the processors that use that memory.

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