Grid Mashups. Gluing grids together with Condor and BOINC
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1 Grid Mashups Gluing grids together with Condor and BOINC, Artyom Sharov, Assaf Schuster, Dan Geiger Technion Israel Institute of Technology 1
2 Problem... 2
3 Problem... 3
4 Problem... 4
5 Parallelization From tens to millions of subtasks 5
6 Parallelization From tens to millions of subtasks Where to find the computers????? 6
7 Let's build a cluster!!! 7
8 Let's build a cluster!!! 8
9 Let's use several clusters 9
10 Let's use several clusters 10
11 Let's also ask for help from... 11
12 Let's also ask for help 12
13 Let's also ask for help community! 13
14 Can we glue these grids together?? 14
15 Can we glue these grids together? Yes? 15
16 We also want to hide the complexity.. 16
17 By building virtual supercomputer for domain researchers 17
18 Virtual supercomputer for geneticists Superlink-online: 18
19 Main components BOINC Server HTTP frontend Scheduler Database jobs, monitoring, system statistics 19
20 Main components BOINC Server HTTP frontend Scheduler Database jobs, monitoring, system statistics 20
21 Main components BOINC Server HTTP frontend Scheduler Database BOINC clients submittor for EGEE Submittor to Technion Condor jobs, monitoring, system statistics BOINC clients submittor for Condor in Madison 21
22 Main components BOINC Server HTTP frontend Scheduler Database BOINC clients submittor for Condor in Madison BOINC clients submittor for EGEE Submittor to Technion Condor jobs, monitoring, system statistics Web Portal Task state 22
23 Main components BOINC Server HTTP frontend Scheduler Database BOINC clients submittor for Condor in Madison BOINC clients submittor for EGEE Submittor to Technion Condor jobs, monitoring, system statistics Web Portal Task state Task execution and monitoring workflow 23
24 Main components BOINC Server HTTP frontend Scheduler Database BOINC clients submittor for Condor in Madison Virtual cluster maintainer jobs, monitoring, system statistics BOINC clients submittor for EGEE Submittor to Technion Condor Web Portal Task state Task execution and monitoring workflow 24
25 Main components BOINC Server HTTP frontend Scheduler Database Web Portal BOINC clients submittor for EGEE Virtual cluster maintainer jobs, monitoring, system statistics Task state BOINC clients submittor for Condor in Madison Task execution and monitoring workflow Submittor to Technion Condor Dedicated cluster fallback 25
26 BOINC Server HTTP frontend Scheduler Database BOINC clients submittor for EGEE Virtual cluster maintainer jobs, monitoring, system statistics Web Portal Task state BOINC clients submittor for Condor in Madison Task execution and monitoring workflow Submittor to Technion Condor Dedicated cluster fallback Condor INSIDE Condor DAGman reliability 26
27 BOINC Server HTTP frontend Scheduler Database Web Portal Task execution and monitoring workflow Condor INSIDE Condor DAGman reliability BOINC clients submittor for EGEE Virtual cluster maintainer jobs, monitoring, system statistics Task state BOINC clients submittor for Condor in Madison Submittor to Technion Condor Dedicated cluster fallback Condor INSIDE Condor priority management 27
28 Condor STARTD as a stand-alone resource manager (Condor over BOINC) Condor INSIDE BOINC Server HTTP frontend Scheduler Database Web Portal Task execution and monitoring workflow Condor INSIDE Condor DAGman reliability BOINC clients submittor for EGEE Virtual cluster maintainer jobs, monitoring, system statistics Task state BOINC clients submittor for Condor in Madison Submittor to Technion Condor Dedicated cluster fallback Condor INSIDE Condor priority management 28
29 29
30 Preliminary results 110 CPU Years consumed and a few millions jobs completed in 4 months ~ 350 CPUs working for us around the clock 49 (clusters: EGEE, UW Madison, Technion CS > 20,000 CPUs ) 61 (Contributors of Superlink@Technion > 8000 CPUs ) 30
31 31
32 ~CPU time ~CPU time 32
33 Single framework for grid performance analysis environment is more predictable! Jobs left in queue Jobs left in queue Since clusters are much more volatile because of policy Eviction rate Eviction rate 33
34 Single framework for grid performance analysis EGEE vs. Madison pool EGEE behaves more than Madison pool. Why? Jobs left High load, complex policies and centralized control result in rapid (statistically unpredictable) changes in resource allocation MADISON Jobs left EGEE is much less centralized evictions are less correlated, hence have steady state EGEE 34
35 Lesson 1: Track the system performance from the application perspective Simple criterion - compare with the time it would take on a dedicated cluster Active hosts for 24 hours Actual throughput Error rate Eviction rate Using BOINC (glide-ins) helps separate application performance from batch system performance 35
36 Lesson 2: Learn the system parameters Example: Average running time of a BOINC client until preempted Wrong job granularity The same run with proper job granularity Using BOINC allows to tailor job size with no additional overhead : few minutes long jobs run as fast as few hours long 36
37 Lesson 3: Distinguish between high throughput and high performance runs Many-jobs: high throughput runs number of jobs much more than number of running machines Less sensitive to failures - overlapped with the execution of other jobs Few-jobs: high performance runs number of jobs is about the number of running machines Performance very sensitive to failures Dedicated cluster comes handy!!! High throughput High performance 37
38 Thinking out loud Will grids become less useful to opportunistic users when they become easy to use (higher utilized)? Should Condor policy be hostile to opportunistic users, or some guarantees are better to be provided? And if above is true, and you are going to have a lot of jobs maybe it's better to buy your own cluster... 38
39 Future work... Venus.. Moon But before that... If your grid can handle backfill let us in! If you want to contribute your PC(s) to the search for disease-provoking genetic mutations Join 39
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