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Managing Your Workforce on a Computational Grid

Managing Your Workforce on a Computational Grid. Computational Grids will give us access to resources anywhere. How can we make them usable by anyone?. You do not have to be a “super” person in order to benefit from “super” computing power. Grid Computing. Obstacles to HTC *. (Sociology)

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Managing Your Workforce on a Computational Grid

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  1. Managing YourWorkforce on a Computational Grid

  2. Computational Grids will give us access to resourcesanywhere.How can we make them usable by anyone?

  3. You do not have to be a “super” person in order to benefit from “super” computing power

  4. Grid Computing Obstacles to HTC * (Sociology) (Education) (Robustness) (Portability) (Technology) • Ownership Distribution • Customer Awareness • Size and Uncertainties • Technology Evolution • Physical Distribution *(this slide has been in use for more than 5 years)

  5. Our Bottom-Up Approach Build and organize your computing capabilities from the bottom up: • Organize the resources on your desk • Organize the resources on your floor • Organize the resources in your building • Organize the resources on your campus • Organize the resources in your state • Organize the resources in your nation • Organize the resources in your world

  6. Here is what a group of empowered (and well organized) scientists can do with Grid resources …

  7. NUG28 - Solved!!!! We are pleased to announce the exact solution of the nug28 quadratic assignment problem (QAP). This problem was derived from the well known nug30 problem using the distance matrix from a 4 by 7 grid, and the flow matrix from nug30 with the last 2 facilities deleted. This is to our knowledge the largest instance from the nugxx series ever provably solved to optimality. The problem was solved using the branch-and-bound algorithm described in the paper "Solving quadratic assignment problems using convex quadratic programming relaxations," N.W. Brixius and K.M. Anstreicher. The computation was performed on a pool of workstations using the Condor high-throughput computing system in a total wall time of approximately 4 days, 8 hours. During this time the number of active worker machines averaged approximately 200. Machines from UW, UNM and (INFN) all participated in the computation.

  8. The NUG30 Personal Condor … For the NUG30 run we will be flocking to -- the main Condor pool at Wisconsin (600 processors) -- the Condor pool at Georgia Tech (190 Linux boxes) -- the Condor pool at UNM (40 processors) -- the Condor pool at Columbia (16 processors) -- the Condor pool at Northwestern (12 processors) -- the Condor pool at NCSA (65 processors) -- the Condor pool at INFN (200 processors) We will be using glide_in to access the Origin 2000 (through LSF ) at NCSA. We will use "hobble_in" to access the Chiba City Linux cluster and Origin 2000 at Argonne.

  9. It works!!! Date: Thu, 8 Jun 2000 22:41:00 -0500 (CDT) From: Jeff Linderoth <linderot@mcs.anl.gov> To: Miron Livny <miron@cs.wisc.edu> Subject: Re: Priority This has been a great day for metacomputing! Everything is going wonderfully. We've had over 900 machines (currently around 890), and all the pieces are working great… Date: Fri, 9 Jun 2000 11:41:11 -0500 (CDT) From: Jeff Linderoth <linderot@mcs.anl.gov> Still rolling along. Over three billion nodes in about 1 day!

  10. Up to a Point … Date: Fri, 9 Jun 2000 14:35:11 -0500 (CDT) From: Jeff Linderoth <linderot@mcs.anl.gov> Hi Gang, The glory days of metacomputing are over. Our job just crashed. I watched it happen right before my very eyes. It was what I was afraid of -- they just shut down denali, and losing all of those machines at once caused other connections to time out -- and the snowball effect had bad repercussions for the Schedd.

  11. Back in Business Date: Fri, 9 Jun 2000 18:55:59 -0500 (CDT) From: Jeff Linderoth <linderot@mcs.anl.gov> Hi Gang, We are back up and running. And, yes, it took me all afternoon to get it going again. There was a (brand new) bug in the QAP "read checkpoint" information that was making the master coredump. (Only with optimization level -O4). I was nearly reduced to tears, but with some supportive words from Jean-Pierre, I made it through.

  12. The First 600K seconds … Number of workers

  13. NUG30 - Solved!!! Sender: goux@dantec.ece.nwu.edu Subject: Re: Let the festivities begin. Hi dear Condor Team, you all have been amazing. NUG30 required 10.9 years of Condor Time. In just seven days ! More stats tomorrow !!! We are off celebrating ! condor rules ! cheers, JP.

  14. 11 CPU years in less than a week,How did they do it? Effective management of their workforce!(www.mcs.anl.gov/metaneos/nug30)

  15. I. Employ the MW Paradigm Map the problem to a dynamic Master-Worker parallel algorithm (branch and bond) with the following properties: • Large number of independent tasks (always have work ready to be sent out in case a worker shows up) • Workers may join or leave/quit at anytime • Mater has control over size of tasks • Master is fault tolerant

  16. Decisions, decisions, decisions … • How many workers? • What kind of workers? • How long to wait for a worker to finish a task? • How big should a task be? • Who should get which task? • When to checkpoint?

  17. II. Build a robust implementation Use our MW runtime support library to implement the MW algorithm (www.cs.wisc.edu/condor/MW). • Opportunistic compute resources • Use PVM or file I/O to move work to workers and results back to master • Heterogeneous compute and communication resources • Mater and workers can recover from a crash

  18. III. Build a Personal Computational Grid Use Condor-G (a grid enabled version of Condor) to manage the workforce of your MW application on Condor and Grid resources • Uses Globus technology and services for inter-domain resource access • Uses Condor capabilities for intra-domain resource and job management and scheduling

  19. How doesCondor-Gwork?(joint effort of the Condor and Globus projects)

  20. Glide-in Glide-in Glide-in Grid Problem Solving Environment/User Condor - G Globus PBS LSF Condor Condor App App App App

  21. Condor_status Condor_submit Job Manager Grid_Man Gate Keeper Sched_D Local Job Scheduler Condor-G

  22. Adding the G to Condor Support for the G Universe

  23. Capabilities • G jobs look and feel like a Condor job • Local and personalized job management services • Reliable and fault tolerant job management

  24. Open Issues • Provide “run once and only once” guarantees • Dealing with failures/exceptions • File staging • Remote I/O • Visibility of remote job and queue status information

  25. Adding the G to CondorCondor “Glide-ins”

  26. Capabilities • Turn allocated G resources into members of a Condor pool • Use GSIFTP services to move Condor executable to the executing environment • Provide safeguards for broken communication lines • Support SMPs and clusters for multi CPU allocations

  27. Open Issues • Manage inventory of glide-ins - how many? When to submit? Which G resource? Which queue? • Provide status and exception information to application • Package Condor software for automatic installation on all platforms • Make remote scheduler aware of opportunistic nature of resource requests

  28. Current Status • MW runtime library available • Proof of concept (used by the NUGxx group) version of Condor-G available (based on Globus Run) • New version of Condor-G will be released as a “Contrib Module” by the end of the month (based on GRAM API) • “Glide-in” package available

  29. C High Throughput Computing ondor Don’t be picky, be agile!!!

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