Analysis of performance monitoring on production batch servers
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1 Analysis of performance monitoring on production batch servers being done by: Andreas Hirstius Paola Cecuk Gyorgy Balazs October 2008 Gyorgy Balazs
2 Performance monitoring Access low level information concerning running jobs Modern CPUs provide real time statistics on executed instructions via a Performance Monitoring Unit (PMU) A robust tool to access PMU counters: Perfmon2 pfmon System wide monitoring Only minimal overhead on the monitored system Gyorgy Balazs Analysis of performance monitoring on production batch servers October
3 Production batch servers CERN Batch Farm of 3000 machines Main users: Experiments Used for: Data analysis Simulation Reconstruction Etc. Etc. Etc. Accessible for everyone from lxplus and the grid (!) Queues for jobs of different length: 8nm,1nh,1nw Jobs are scheduled by LSF Gyorgy Balazs Analysis of performance monitoring on production batch servers October
4 Goals To get some data: oget low level, system wide information about the running nodes odetailed, instruction level information about jobs oreveal possible performance bottlenecks To answer some questions: owho (which experiments) are using the nodes? owhat are they running? odoes it fit their claims? Gyorgy Balazs Analysis of performance monitoring on production batch servers October
5 Monitoring 60 std. Batch nodes 49 days 761 users ~ jobs Gyorgy Balazs Analysis of performance monitoring on production batch servers October
6 What to do with the raw data? Raw data comes on an hourly basis from each machine Generate different reports: ohourly report per node odaily report per node oaggregated results per node oaggregated results all nodes Merging reports with LSF logs: oget user and queue data opin jobs to experiments ccid->groupid->experiment Generate files: oall data/node osimplified view/node oexperiments Gyorgy Balazs Analysis of performance monitoring on production batch servers October
7 Gained information Performance information: Unhalted Core Cycles Instructions retired Cycles Per Instruction (CPI) Branch instructions % Bus utilization % Data load/store % L2 cache misses % SIMD/x87 % Running jobs: Username Group Experiment Job queue Gyorgy Balazs Analysis of performance monitoring on production batch servers October
8 Aggregated results for all nodes Gyorgy Balazs Analysis of performance monitoring on production batch servers October
9 Analysis of 32bit vs. 64bit applications Indicator: x87 (32bit) and SIMD (64bit) instructions What are we looking for? opercentage of all instructions oratio at each experiment odoes it fit the claims? Problem: one experiment/node is very rare Normally 8-9 jobs from 2-4 experiments running on each node Solution: find timeframes when one experiment is dominant for a longer time Gyorgy Balazs Analysis of performance monitoring on production batch servers October
10 Analysis of 32bit vs. 64bit applications Analyzing dominant users: Selecting timeframes, when at least 80% of the running jobs belong to one experiment Experiment Hours when dominant All SIMD+x87 instr. NA ~12% ALICE 565 ~7% CMS 64 ~10% ATLAS 48 ~10% Gyorgy Balazs Analysis of performance monitoring on production batch servers October
11 32/64 bit usage at the experiments Experiment Principal instr. Utilization rate ATLAS x87 ~90% Dominant users ALICE SIMD ~90% CMS x87 ~90% NA58 x87 ~90% AT x87 >90% IT-GEANT4 SIMD >90% IT-GEAR SIMD >90% Minor users IT-DTEAM x87 low amount of data LHCB x87 >90% NA45/2 x87 >90% PH-TH SIMD low amount of data PS212 x87 low amount of data Gyorgy Balazs Analysis of performance monitoring on production batch servers October
12 The next steps Analyzing timeframes when the same jobs are running, but the performance is changing Castor s impact on performance How much data can be extracted from performance monitoring? How can we automate the process? Gyorgy Balazs Analysis of performance monitoring on production batch servers October
13 Q & A Gyorgy Balazs Analysis of performance monitoring on production batch servers October
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