[Beowulf] Please help to setup Beowulf

Reuti reuti at staff.uni-marburg.de
Tue Feb 17 12:47:53 PST 2009

Hi, to throw in one more:

Am 17.02.2009 um 20:51 schrieb Chris Dagdigian:

> On Feb 17, 2009, at 2:29 PM, Michael Will wrote:
>> What features differentiate SGE in support of life science workflow
>> from LSF/PBS/Torque/Condor?

is anyone using IBM's LoadLeveler for Linux and why? I saw that it's  
available already some time ago, but I got the impression, that it's  
mostly addressing sites which have already LoadLeveler on AIX, and  
don't want to introduce a second queuingsystem in their infrastructure.

> They all have their pros and cons, heck I'm still an LSF zealot  
> when cost is not an issue as Platform has the best APIs,  
> documentation and layered products for the industry types who need  
> to stand these things up in full production mode within enterprise  
> organizations that may have varying levels of Linux/HPC/MPI  
> experience.
> The short list of why Grid Engine became popular in the life sciences:
> LSF: great product but commercial-only and a pricing model that can  
> get out of hand (I remember when having more than 4GB RAM in a  
> Linux 1U pushed me into an obscene license tier ...).
> Condor: Did not have the fine grained policy and resource  
> allocation tools that make life easier when you need to have a  
> shared cluster resource supporting multiple competing users,  
> groups, projects and workflows. The policy tools for LSF/SGE/PBS  
> were more capable.  When I saw condor out in the field seemed to be  
> mostly used only in academic sites and in situations where cycles  
> from PC systems were being aggregated across LAN, metro and wan- 
> scale distances. Bio problems tend to be more I/O or memory bound  
> rather than CPU bound so most bio clusters tend to be closely  
> situated racks of gear.
> PBS/TORQUE: I'll ignore the FUD from back in the day when people  
> were claiming that PBS lost jobs and data at high scale and  
> concentrate on just one key differentiator. At the time when life  
> science was transitioning from big SGI Altix and Tru64 Alphaservers  
> machines to commodity compute farms, PBS did not support the  
> concept of array jobs. If there was one overwhelming cluster  
> resource management feature essential for bio work
> it would be array tasks. This is because we tend to have a very  
> high concentration of batch/serial workflows that involve running  
> an application many many times in a row with varying input files  
> and parameter options. The cliche example in bioinformatics is  
> needing to run half a million blast searches. Without array task  
> scheduling this would require 500,000 individual job submissions.  
> The fact that I never met a serious PBS shop that had not made  
> local custom changes to the source code also soured me on deploying  
> it when I was putting such things into conservative IT shops who  
> were still new and fearful of Linux.

One thing more: AFAIK Torque has no scheduler built-in besides the  
FIFO one. You will need MAUI (free) or MOAB (commercial) to get a  
scheduler, with the side effect to have to use "qstat" (for Torque)  
and "showq" (for MAUI) to investigate the status of the jobs.

-- Reuti

> We also don't make heavy use of the globus style WAN-scale capital  
> "G" grid computing as much of our workflows and pipelines are  
> actually performance bound by the speed of storage rather than CPU  
> or memory issues. It was always easier, cheaper and more secure to  
> colocate dedicated CPU resources local to fast storage rather than  
> distribute things out as far as possible.
> The big news in Bio-IT these days is actually the terabyte scale  
> wet lab instruments such as confocal microscopes and next-gen DNA  
> sequencing systems that can produce 1-3TB of raw data per  
> experiment. Some of these lab instruments ship with software  
> pipelines developed to run under grid engine. A popular example is  
> the Solexa/Illumina Genome Analyzer which alone has driven SGE  
> uptake in our field. A notable exception is the SOLiD system which  
> (I think) ships with a Windows front end that hides a back end  
> ROCKS cluster running either PBS or torque under the hood.
> And from Mark:
>> how about providing some useful content - for instance, what is it  
>> that you think is especially valuable about sge?
> Hopefully I've done some of that with this message. It basically  
> boils down to the fact that at the time our field started using  
> compute farms in a serious manner, SGE offered the best overall  
> combination of features, price and fine grained resource allocation  
> & policy control. I think what made us a bit different from some  
> other use cases is our heavy use of serial/batch workflows combined  
> with our tendency to require that our HPC infrastructures support  
> multiple (and potentially competing) workflows and pipelines which  
> made the policy/allocation features a key selection criteria. We  
> also do little if any true WAN-scale "grid" computing due to  
> workflows that tend to be more storage/IO bound than anything  
> else.  For people starting fresh with a cluster scheduling layer  
> who did not have an investment in time, expertise and/or software  
> licensing costs, Grid Engine turned out to be a popular choice.  
> With that popularity came a good set of people in the community who  
> can now support and configure these systems (as well as evangelize  
> them) so the cycle is fairly self perpetuating.
> General life science cluster cheat sheet:
> - Workloads tend to be far more serial/batch in nature than true  
> parallel
> - Policy and resource allocation features are very important to  
> people deploying these systems
> - Storage speed is often more important than network speed or  
> latency in many cases
> - Fast interconnects are often used for cluster/distributed  
> filesystems rather than application message passing
> - Our MPI codes are often quite horrific from an efficiency/tuning  
> standpoint - gigE works just as well as Myrinet or IB
> - Exceptions to the MPI rule: computational chemistry, modeling and  
> structure prediction (those fields have well written commercial MPI  
> codes in use)
> - Huge resistance to improved algorithms as scientists want to use  
> *exactly* the same code that was used to publish the journal paper
> -Chris
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