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Application Performance Profiling and Prediction in Grid Environment

This presentation by Marlon Bright discusses the grid enablement of the Weather Research and Forecasting Code (WRF), as well as the tools and techniques used for performance profiling and prediction in a grid environment. The goal is to improve weather prediction accuracy and efficiency.

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Application Performance Profiling and Prediction in Grid Environment

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  1. Presented by: Marlon Bright 14 July 2008 Advisor: Masoud Sadjadi, Ph.D. REU – Florida International University Application Performance Profiling and Prediction in Grid Environment

  2. Outline • Grid Enablement of Weather Research and Forecasting Code (WRF) • Profiling and Prediction Tools • Research Goals • Project Timeline • Current Progress • Challenges • Remaining Work REU - Florida International University

  3. Motivation – Weather Research and Forecasting Code (WRF) • Goal – Improved Weather Prediction • Accurate and Timely Results • Precise Location Information • WRF Status • Over 160,000 lines (mostly FORTRAN and C) • Single Machine/Cluster compatible • Single Domain • Fine Resolution -> Resource Requirements • How to Overcome this? • Through Grid Enablement • Expected Benefits to WRF • More available resources – Different Domains • Faster results • Improved Accuracy REU - Florida International University

  4. System Overview • Web-Based Portal • Grid Middleware (Plumbing) • Job-Flow Management • Meta-Scheduling • Performance Prediction • Profiling and Benchmarking • Development Tools and Environments • Transparent Grid Enablement (TGE) • TRAP: Static and Dynamic adaptation of programs • TRAP/BPEL, TRAP/J, TRAP.NET, etc. • GRID superscalar: Programming Paradigm for parallelizing a sequential application dynamically in a Computational Grid REU - Florida International University

  5. Performance Prediction IMPORTANT part of Meta-Scheduling Allows for: • Optimal usage of grid resources through “smarter” meta-scheduling • Many users overestimate job requirements • Reduced idle time for compute resources • Could save costs and energy • Optimal resource selection for most expedient job return time REU - Florida International University

  6. The Tools:Amon / Aprof Dimemas / Paraver

  7. Amon / Aprof • Amon – monitoring program that runs on each compute node recording new processes • Aprof – regression analysis program running on head node; receives input from Amon to make execution time predictions (within cluster & between clusters) REU - Florida International University

  8. Amon / AprofMonitoring and Prediction REU - Florida International University

  9. Amon / Aprof Approach to Modeling Resource Usage WRF Network Latency CPU Speed Hard Disk I/O Number of Nodes Network Bandwidth FSB Bandwidth RAM Size L2 Cache Application Resource Usage Model REU - Florida International University

  10. Sample Amon Output Process --- (464) --- name: wrf.exe cpus: 8 inv clock: 1/2297.700 [MHz] inv cache size: 1/1024 [KB] elapsed time: 1234232 [msec] utime: 1233890 [msec] 1236360 [msec] stime: 560 [msec] 1420 [msec] intr: 44959 ctxt switch: 84394 fork: 89 storage R: 0 [blocks] 0 [blocks] storage W: 0 [blocks] network Rx: 4188840 [bytes] network Tx: 2106854 [bytes] REU - Florida International University

  11. Sample Aprof Output name: wrf_arw_DM.exe elapsed time: 5.783787e+06 =========================================================== explanatory: value parameter std.dev ----------------- ------------- ------------- ------------- : 1.000000e+00 5.783787e+06 1.982074e+05 =========================================================== predicted: value residue rms std.dev ----------------- ------------- ------------- ------------- elapsed time: 5.783787e+06 4.246451e+06 1.982074e+05 =========================================================== REU - Florida International University

  12. Sample Query Automation Script Output adj. cpu speed, processors, actual, predicted, rms, std. dev, actual difference, 3591.363, 1, 5222, 5924.82, 1592.459, 415.3491, 13.4588280352 3591.363, 2, 2881, 3246.283, 1592.459, 181.5382, 12.6790350573 3591.363, 3, 2281, 2353.438, 1592.459, 105.334, 3.17571240684 3591.363, 4, 1860, 1907.015, 1592.459, 69.19778, 2.52768817204 3591.363, 5, 1681, 1639.161, 1592.459, 49.83672, 2.48893515764 3591.363, 6, 1440, 1460.592, 1592.459, 39.5442, 1.43 3591.363, 7, 1380, 1333.043, 1592.459, 34.76459, 3.40268115942 3591.363, 8, 1200, 1237.381, 1592.459, 33.27651, 3.11508333333 3591.363, 9, 1200, 1162.977, 1592.459, 33.56231, 3.08525 3591.363, 10, 1080, 1103.454, 1592.459, 34.68943, 2.17166666667 3591.363, 11, 1200, 1054.753, 1592.459, 36.15324, 12.1039166667 3591.363, 12, 1080, 1014.169, 1592.459, 37.70271, 6.09546296296 3591.363, 13, 1200, 979.8292, 1592.459, 39.22018, 18.3475666667 3591.363, 14, 1021, 950.3947, 1592.459, 40.65455, 6.91530852106 3591.363, 15, 1020, 924.8848, 1592.459, 41.9872, 9.32501960784 REU - Florida International University

  13. Previous Findings for Amon / Aprof Experiments were performed on two clusters at FIU—Mind (16 nodes) and GCB (8 nodes) • Experiments were run to predict for different number of nodes and cpu loads (i.e. 2,3,…,14,15 and 20%, 30%,…,90%, 100%) • Aprof predictions were within 10% error versus actual recorded runtimes within Mind and GCB and between Mind and GCB • Conclusion: first step assumption was valid. -> Move to extending research to higher number of nodes. REU - Florida International University

  14. Paraver / Dimemas • Dimemas - simulation tool for the parametric analysis of the behavior of message-passing applications on a configurable parallel platform. • Paraver – tool that allows for performance visualization and analysis of trace files generated from actual executions and by Dimemas Tracefiles generated by MPItrace that is linked into execution code REU - Florida International University

  15. Dimemas Simulation Process Overview • Link MPItrace into application source code—dynamically generates tracefiles for each node application running on (.mpit) • Use CEPBA tool ‘mpi2prv’ to convert .mpit files into one .prv file • Load file into Parver using XML filtering file (provided by CEPBA) to reduce tracefile eliminating ‘perturbed regions’ (i.e. much of the initialization) • Open tracefile in Paraver using ‘useful_duration’ configuration file and adjust scales to fit events • Identify computation iterations compose a smaller trace file by selecting a few iterations, preserving communications and eliminating initialization phases REU - Florida International University

  16. Paraver tracefile with iterations selected, cut, and ready for Dimemas conversion. REU - Florida International University

  17. Simulation Process (cont’d) • Convert the new tracefile to Dimemas format (.trf) using CEPBA provided ‘prv2trf’ tool • Load tracefile into Dimemas simulator, configure target machine, and with information generate Dimemas configuration file • Call simulator with or without option of generating a Paraver (.prv) tracefile for viewing. Great News: You only have to go through this process once if done for the maximum amount of nodes you will simulate for! Once configuration file is generated, different numbers of nodes can be simulated for through alterations to the file. REU - Florida International University

  18. Dimemas Simulator Results REU - Florida International University

  19. Goals • Extend Amon/Aprof research to larger number of nodes, different architecture, and different version of WRF (Version 2.2.1). • Compare/contrast Aprof predictions to Dimemas predictions in terms of accuracy and prediction computation time. • Analyze if/how Amon/Aprof could be used in conjunction with Dimemas/Paraver for optimized application performance prediction and, ultimately, meta-scheduling REU - Florida International University

  20. Timeline • End of June: • Get MPItrace linking properly with WRF Version Compiled on GCB, then Mind COMPLETE • a) Install Amon and Aprof on MareNostrum and ensure proper functioning AMON COMPLETE; APROF FINAL STAGES b) Run Amon benchmarks on MareNostrum COMPLETE • Early/Mid July: • Use and analyze Aprof predictions within MareNostrum (and possibly between MareNostrum, GCB, and Mind) IN PROGRESS • Use generated MPI/ OpenMP tracefiles (Paraver/Dimemas) to predict within (and possibly between) Mind, GCB, and MareNostrum IN PROGRESS • Late July/Early August: • Experiment with how well Amon and Aprof relate to/could possibly be combined with Dimemas • Analyze how findings relate to bigger picture. Make optimizations on grid-enablement of WRF. • Compose paper presenting significant findings. REU - Florida International University

  21. Current Progress REU - Florida International University

  22. General • Completed reading of related works papers • Well advanced in Linux studies • Established effective collaboration/working relationship with developers of Dimemas and Paraver REU - Florida International University

  23. Amon • Installed on MareNostrum • Adjusted source code to properly read node information from MareNostrum (will document this on Wiki to be considered when configuring on new architectures) REU - Florida International University

  24. Amon (cont’d) • Automated benchmarking shell script developed • Starts Amon on each compute node returned by system scheduler • Executes WRF with one process per node for: • Node counts of: 8, 16, 32, 64, 96, and 128 • CPU percentage (%) loads of: 25, 50, 75, & 100 (Done through implementation of CPULimit program) • Writes results (to be used as Aprof input) to organized results directory of …/<cpu load percentage>/<number of nodes>/<timestamp of run>/ <amon output by node> REU - Florida International University

  25. Aprof • Installed on MareNostrum • Adjusted source code to change the way Aprof reads in information • Before: Input files had to specify number of bytes in process listing in process header (This was very complicated and error prone. Aprof was inconsistent in loading MareNostrum data). • Now: Input files simply need to separate process entries with one or more blank lines. REU - Florida International University

  26. Aprof (cont’d) • Script developed that combines Amon output from all nodes and edits it into the necessary read-in format for Aprof. • Aprof query automation script adjusted /developed for MareNostrum • Queries Aprof for prediction information for different cases (number of nodes; cpu percentage loads) • Compares predicted values to actual values returned by run REU - Florida International University

  27. Dimemas / Paraver • Paraver tracefile successfully generated and visualized with GUI on MareNostrum • Dimemas tracefile successfully generated from Paraver on MareNostrum • Configuration file for MareNostrum developed • Prediction simulations will begin shortly REU - Florida International University

  28. Significant Challenges Overcome • Amon: • Adjustment of source code to proper functioning on MareNostrum • Development of benchmarking script to conform to system architecture of MareNostrum (i.e. going through its scheduler; one process per node; etc.) • Aprof: • Adjustment of source code for less complex, more consistent data input • Development of prediction and comparison scripts for MareNostrum REU - Florida International University

  29. Significant Challenges Overcome(cont’d) • Dimemas/Paraver • MPItrace properly linked in with WRF on GCB and Mind • Paraver and Dimemas successfully generated and configuration file configured for MareNostrum. • WRF • Version 2.2 installed and compiled on Mind REU - Florida International University

  30. Remaining Work • Scripting Dimemas prediction simulations for the same scenarios of those of Amon and Aprof • Finalizing Aprof prediction/comparison script so that Aprof’s performance on new architecture of MareNostrum can be analyzed • Deciding if and how to compare results from MareNostrum, GCB, and Mind (i.e. the same versions of WRF would have to be running in all three locations) • Experiment with how well Amon and Aprof relate to/could possibly be combined with Dimemas REU - Florida International University

  31. References • S. MasoudSadjadi, Liana Fong, Rosa M. Badia, Javier Figueroa, Javier Delgado, Xabriel J. Collazo-Mojica, Khalid Saleem, RajuRangaswami, Shu Shimizu, Hector A. Duran Limon, Pat Welsh, SandeepPattnaik, Anthony Praino, David Villegas, SelimKalayci, GargiDasgupta, OnyekaEzenwoye, Juan Carlos Martinez, Ivan Rodero, Shuyi Chen, Javier Muñoz, Diego Lopez, JulitaCorbalan, Hugh Willoughby, Michael McFail, Christine Lisetti, and MalekAdjouadi. Transparent grid enablement of weather research and forecasting. In Proceedings of the Mardi Gras Conference 2008 - Workshop on Grid-Enabling Applications, Baton Rouge, Louisiana, USA, January 2008. http://www.cs.fiu.edu/~sadjadi/Presentations/Mardi-Gras-GEA-2008-TGE-WRF.ppt • S. MasoudSadjadi, Shu Shimizu, Javier Figueroa, RajuRangaswami, Javier Delgado, Hector Duran, and XabrielCollazo. A modeling approach for estimating execution time of long-running scientific applications. In Proceedings of the 22nd IEEE International Parallel & Distributed Processing Symposium (IPDPS-2008), the Fifth High-Performance Grid Computing Workshop (HPGC-2008), Miami, Florida, April 2008. http://www.cs.fiu.edu/~sadjadi/Presentations/HPGC-2008-WRF%20Modeling%20Paper%20Presentationl.ppt • “Performance/Profiling”. Presented by Javier Figueroa in Special Topics in Grid Enablement of Scientific Applications Class. 13 May 2008 REU - Florida International University

  32. Acknowledgements • REU • PIRE • BSC • MasoudSadjadi, Ph. D. - FIU • Rosa Badia, Ph.D. - BSC • Javier Delgado – FIU • Javier Figueroa - UM REU - Florida International University

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