Exploring Many Task Computing in Scientific Workflows

Transcription

1 Exploring Many Task Computing in Scientific Workflows Eduardo Ogasawara Daniel de Oliveira Fernando Seabra Carlos Barbosa Renato Elias Vanessa Braganholo Alvaro Coutinho Marta Mattoso Federal University of Rio de Janeiro, Brazil Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MTAGS '09 November 16th, 2009, Portland, Oregon, USA Copyright 2009 ACM /09/11... $10.00 MTAGS

2 Agenda Introduction o Scientific experiments o Scientific workflows o Experiments life cycle Hydra middleware Case study Related work Conclusion MTAGS

3 Typical scenario: scientific experiment 2. Data analyzed by program X 1. Data collection 3. Large Volume of Data Produced Results are analyzed by program Z 4....which need to be processed by program Y in a cluster MTAGS

4 Variations of data or parameters 2. Data analyzed by program X 1. Data collection 3. Large Volume of Data Produced Results are analyzed by program Z 4....which need to be processed by program Y in a MTC environment MTAGS

5 Current solutions Scientific Workflow Management Systems (SWfMS) SWfMS allow the execution of Scientific Workflows o Some SWfMS are strong in workflow design and provenance support (VisTrails, Kepler, Taverna) o Some SWfMS are strong in HPC support (Pegasus, Swift, Triana) Scientists should be free to choose the SWfMS that suits best for their needs This choice should not prevent the adoption of an MTC solution for executing one or more activities of a workflow MTAGS

6 Parallelization difficulties Controlling parallel execution in distributed environments Steering activities in distributed environments Provenance gathering in distributed/ heterogeneous environments MTAGS

7 Provenance can support analyzing scientific experiments Before execution: o What programs may be used? Is there any alternative to explore? o Is there any dependency between activities? Which activities are mandatory? After execution: o What were the parameters that lead the best result? o What was the scientific workflow that lead to the desired result? o Where are the output files generated by the distributed activity A using the parameters P? o How many times the activity A in version V was used in the experiment E? MTAGS

8 Our vision of the experiment life cycle GExpLine tool support s the experiment life cycle Composition Conception Reuse Provenance Data Analysis Query Discovery Distribution Monitoring Execution SWfMS Hydra HPC MTAGS

9 Hydra Middleware solution that bridges the SWfMS to the HPC supporting MTC parallelization strategies SWfMS Hydra Middleware HPC Environment Goal: reduce the complexity involved in designing and managing activity/workflow parallel executions while gathering distributed provenance data MTAGS

10 Supported parallelization types Data Input Data Parameters Parameter Sweep Data Fragmentation Parameter Sweep I 1 I n Pt 1 Pt n Activity/ Wf Activity/ Wf Parameters Activity/ Wf Activity/ Wf Data Input O 1 O n O 1 O n Data Analysis Data Analysis Data Output Data Output MTAGS

11 Hydra Architecture Hydra Setup Hydra MTC Layer Hydra Setup Configuration MUX Workflow Hydra Client Components Parameter Sweeper Workspace Handler Hydra Preprocessing Data Fragmenter Cartridge PBS Falkon Scheduler Uploader Swift Dispatcher Gatherer Downloader Hydra Dispatcher / Monitor Dispatcher Monitor VisTrails SWfMS Hydra External Components Hydra Post-processing Provenance Data Analyzer Cartridge Client Layer MTC Environment Storage Control Data MTAGS

12 Hydra setup Hydra Setup MTAGS

13 Hydra client components MTAGS

14 Hydra pre-processing components Parameter Sweeper Workspace Handler Data Fragmenter Cartridge Pre-Processing MTAGS

15 Hydra dispatcher/monitor components Dispatcher Monitor MTC Processing MTAGS

16 Hydra post-processing components Provenance Data Analyzer Cartridge Post-Processing MTAGS

17 Hydra Architecture Hydra MTC Layer Hydra Setup Configuration MUX Workflow Parameter Sweeper Workspace Handler Data Fragmenter Cartridge PBS Falkon Scheduler Hydra Client Componen nts Uploader Dispatcher Gatherer Downloader Pre-Processing Dispatcher Monitor MTC Processing Swift VisTrails SWfMS Provenance Data Analyzer Cartridge Post-Processing Client Layer MTC Environment Storage Control Data MTAGS

18 Case study Computational Fluid Dynamics (CFD) EdgeCFD: a parallel stabilized finite element incompressible flow solver Synthesized in four steps: omodeling o Preprocessing osolution o TAU parallel profiling of CFD solver on SGI Altix ICE 8200, 128 cores MTAGS

19 nn.part.msh velo_nnnn.vecnn part.mat press_0000_sdnn part.ic scal_nnnn_sdnn part.edg DD_nnnn_sdnn EdgeCFD experiment life cycle file nn.part.in file <<Automated>> EdgeCFD Preprocessor file file <<Sub-Workflow, Sweep>> EdgeCFD Solver and Control Applications File Composition file.case file nn.geo file file file file <<Semi-Automated>> Conception Reuse Analysis Query Discovery Provenance Data Distribution Monitoring Execution VisTrails & Hydra MTAGS

20 Workflow modeled in UML <<Automated>> EdgeCFD Preprocessor Pre-processing file nn.part.in file nn.part.msh file part.mat file part.ic File part.edg <<Sub-Workflow, Sweep>> EdgeCFD Solver and Control Applications solver file.case file nn.geo file velo_nnnn.vecnn file press_0000_sdnn file scal_nnnn_sdnn file DD_nnnn_sdnn <<Semi-Automated>> visualization MTAGS

21 Sequential workflow Pre-processing solver visualization MTAGS

22 Parameter sweep scenario MTAGS

23 Workflow with parameter sweep using Hydra Pre-processing solver visualization MTAGS

24 Hydra client setup for the solver activity MTAGS

25 Instrumentation of files for the experiment MTAGS

26 Hydra provenance MTAGS

27 Evaluation of a small experiment MTAGS

28 Related work Swift/Falkon o Provides MTC support from Swift SWfMS MyCluster osupports PBS with transient fault support over remote sites Dryad osupports data parallelization with high scalability Sawzal oit is a framework for MTC that explore data parallelism MTAGS

29 Conclusions Experiments life cycle must be managed as a whole: o Composition: experiment is modeled in a workflow abstraction level until being deployed into a specific SWfMS o Execution: some activities demand HPC with monitoring facilities and provenance gathering o Analysis: uses both information from the composition (prospective provenance) and from execution (local and distributed - retrospective provenance) Hydra can be a bridge between the SWfMS and the HPC environment o Supports workflow data and parameter sweep parallelization o Evaluated in a real case CFD solver with little overhead o Supports distributed provenance gathering MTAGS

30 Future work Evaluate different kinds of applications (e.g. blast, uncertainty quantification ) Model distributed activities that are actually subworkflows Run experiments in HPC with more cores MTAGS

31 Exploring Many Task Computing in Scientific Workflows Eduardo Ogasawara Fernando Seabra Renato Elias Alvaro Coutinho Thank you! Daniel de Oliveira Carlos Barbosa Vanessa Braganholo Marta Mattoso Federal University of Rio de Janeiro, Brazil Please visit oursite MTAGS