Presentation courtesy of Steve Lantz.
Red Cloud is Cornell’s university-wide private cloud for research computing, maintained by the Center for Advanced Computing. Subscribers can meet their on-demand computing needs by launching one or more virtual server instances of up to 28 CPU cores each from two pools comprising 472 total cores. Red Cloud is based on HPE Helion Eucalyptus; this opens up the possibility for extra-large workloads to burst out to the Amazon cloud, as Eucalyptus is fully compatible with Amazon Web Services. Red Cloud instances are commonly managed through a Web-based user console, which also allows Elastic Block Storage (EBS) volumes to be defined and attached to instances.
This presentation focuses on one potential use of Red Cloud, namely, to act as a host for MDCS clusters. MDCS is probably the ultimate strategy for accelerating MATLAB computations. The first step to parallelize a MATLAB script through the mechanisms provided in the Parallel Computing Toolbox. If local CPU cores do not offer enough speedup for your PCT computations, then it may be possible to scale out to Red Cloud and bring more resources to bear. I will demo the process of launching a Red Cloud MDCS instance, connecting to it from a MATLAB R2016a client, and speeding up a PCT-enabled computation through the use of multiple workers in the cloud. I will also talk about the various modes of PCT usage, and discuss when it might make sense to introduce one or more of these into your MATLAB scripts.
Presented at SSW: https://cornell-ssw.github.io/meetings/2017-05-08