HPC: CLUSTER MANAGEMENT
Especially with the advent of cloud computing, virtualisation and the increasing popularity of GPUs, what a physical computing system looks like is very fluid. Paul Schreier looks into how vendors of cluster management software address the provisioning and workload management needs resulting from this major trend
he basic concept behind cluster management is relatively simple: you provision compute nodes with the operating systems, middleware and applications they need and then assign jobs so as to make maximum use of these resources. However, this job is getting ever more complicated, as system architectures add new twists and turns. For example, how are vendors handling the cluster management aspects of cloud computing? To what degree do they support the GPGPUs that are playing an increasingly important role in HPC? The way computer architectures are evolving so rapidly, it seems like achieving effective provisioning and workload management is like shooting at a moving target, only the target continues to move faster and faster. Further, adds Steve Conway, research VP for technical computing at market- research firm IDC, hardware is advancing and growing in scope faster than people can exploit it, and all the pressure is now on the software side. The largest clusters today have 250,000 processors and they’re headed to a million, while some people are talking
T
22
about eventually reaching a billion cores – and the task of keeping all of them running at peak efficiency is growing just as rapidly, if not more so.
Another factor is that, because of all the new technological options available, there’s pressure on companies to add additional heterogeneous resources by including nodes with GPUs, Cell processors, high- speed networking or whatever else is the latest technology. At the same time, there’s pressure to consolidate computing resources, and running multiple specialised heterogeneous systems isn’t cost-effective. All applications, however, don’t run best on one size or type of system. Flexibility is becoming the operative word.
Impact on cluster management
All this has certainly had an effect on cluster management in a big way. Gary Tyreman, senior VP of products and alliances at Univa UD, notes that a year ago cluster managers were focusing on how to deal with bare metal, but since then the industry has been on an amazing journey into virtualisation and cloud computing. He
SCIENTIFIC COMPUTING WORLD APRIL/MAY 2010
points out the need for software to be able to do checkpointing (saving a snapshot of a virtual machine in the event a machine fails during a long job), swapping machines or growing/shrinking the number of CPUs in a virtual machine to reduce swapping, and mobility. It’s also desirable to have the ability to migrate a job among machines so that you can, for instance, take a machine out for repair without disrupting jobs. And a typical scientist or engineer should experience only negligible performance degradation when switching from a bare metal machine to a virtual machine. The key is provisioning bare metal to virtual machines with a policy engine that takes into account the infrastructure and business rules. Now everyone seems to be building a cluster, for instance, Univision has rewritten its UniCloud to support all of these: bare metal, virtual machines, as well as cloud suppliers including Amazon and Rackspace, to handle ‘bursting’. In thinking about provisioning and workload management, however, one must be specific about the differences between grid computing and cloud computing. According to Martin Harris, director of product management at Platform Computing, enterprise grids typically enable infrastructure services as a utility – ask any grid veterans, he adds, and they’ll claim to have been doing cloud-like computing for years. But when you look at the definition of IaaS (internet as a service) that most analysts adhere to – elastic application
www.scientific-computing.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44