XLDB /19/2011 Scott A. Klasky. To Exascale and beyond!

Transcription

1 XLDB /19/2011 Scott A. Klasky H. Abbasi 2, Q. Liu 2, J. Logan 1, M. Parashar 6, N. Podhorszki 1, K. Schwan 4, A. Shoshani 3, M. Wolf 4, S. Ahern 1, I.Altintas 9, W.Bethel 3, L. Chacon 1, CS Chang 10, J. Chen 5, H. Childs 3, J. Cummings 13, C. Docan 6, G. Eisenhauer 4, S. Ethier 10, R. Grout 7, R. Kendall 1, J. Kim 3, T. Kordenbrock 5, Z. Lin 10, J. Lofstead 5, X. Ma 15, K. Moreland 5, R. Oldfield 5, V. Pascucci 12, N. Samatova 15, W. Schroeder 8, R. Tchoua 1, Y. Tian 14, R. Vatsavai 1, M. Vouk 15, K. Wu 3, W. Yu 14, F. Zhang 6, F. Zheng 4 1 ORNL, 2 U.T. Knoxville, 3 LBNL, 4 Georgia Tech, 5 Sandia Labs, 6 Rutgers, 7 NREL, 8 Kitware, 9 UCSD, 10 PPPL, 11 UC Irvine, 12 U. Utah, 13 Caltech, 14 Auburn University, 15 NCSU To Exascale and beyond! Thanks to: V. Bhat, B. Bland, S. Canon, T. Critchlow, G. Y. Fu, B. Geveci, G. Gibson, C. Jin, B. Ludaescher, S. Oral, M. Polte, R. Ross, J. Saltz, R. Sankaran, C. Silva, W. Tang, W. Wang, P. Widener

2 Outline I/O challenges for extreme scale computing The Adaptable I/O System (ADIOS) SOA In transit approach Future work Work supported under: ASCR : CPES, SDM, ASCR SDM Runtime Staging, SciDAC Application Partnership, OLCF, exact co-design OFES : GPSC, GSEP NSF : HECURA, RDAV NASA : ROSES

3 Extreme scale computing Trends More FLOPS Limited number of users at the extreme scale Problems Performance Resiliency Debugging Getting Science done Problems will get worse Need a revolutionary way to store, access, debug to get the science done! ASCI purple (49 TB/140 GB/s) JaguarPF (300 TB/200 GB/s) Most people get < 5 GB/s at scale From J. Dongarra, Impact of Architecture and Technology for Extreme Scale on Software and Algorithm Design, Crosscutting Technologies for Computing at the Exascale, February 2-5, 2010.

4 Next generation I/O and file system challenges At the architecture or node level Use increasingly deep memory hierarchies coupled with new memory properties At the system level Cope with I/O rates and volumes stress the interconnect and can severely limit application performance Can consume unsustainable levels of power At the exascale machine level Immense aggregate I/O needs with potentially uneven loads placed on underlying resource Can result in data hotspots, interconnect congestion and similar issues

5 Applications are the main drivers Our team works directly with (24% of time at OLCF) Fusion: XGC1, GTC, GTS, GTC-P, Pixie3D, M3D, GEM Combustion: S3D, Raptor, Boxlib Relativity: PAMR Nuclear Physics: Nuclear Physics Astrophysics: Chimera, Flash many more application teams Requirements (in order) Fast I/O with low overhead Simplicity in using I/O Self Describing file formats QoS Extras, if possible In-transit visualization Couple multiple codes Perform in transit workflows

6 ADIOS: Adaptable I/O System Provides portable, fast, scalable, easy-to-use, metadata rich output with a simple API Change I/O method by changing XML Layered software architecture: Allows plug-ins for different I/O implementations Abstracts the API from the method used for I/O Open source: Research methods from many groups. S3D: 10X performance improvement over previous work Chimera: 10X performance improvement XGC1 code 40 GB/s, SCEC code 30 GB/s GTC code 40 GB/s, GTS code: 35 GB/s SCEC code 10X performance improvement

7 We want our system to be so easy, even a chimp can use it Even I can use it! Sustainable Fast Scalable Portable

8 std dev. time Usability of Optimizations New technologies are usually constrained by the lack of usability in extracting performance Next generation I/O frameworks must address this concern Partitioning the task of optimizations from the actual description of the I/O Allow framework to optimize for both read/write performance on machines with high-variability, and increase the average I/O performance

9 Data Formats: Elastic Data Organization File systems have increased in complexity due to high levels of concurrency Must understand how to place data on the file system How to decrease the overhead of both writing and reading data to and from these complex file systems File formats should be Easy to use, easy to program, high performance, portable, self-describing, and able to work efficiently on parallel file systems and simple file systems. Allow for simple queries into the data A major challenge has always been in the understanding of the performance of I/O for both reading and writing, and in trying to optimize for both of these cases

10 Excellent read performance for patterns Use Hilbert curve to place chunks on lustre file system with an Elastic Data Organization. Theoretical concurrency Observed Performance

11 GB/s I/O skeltal code creation s3d.xml skel params I/O Skeletal Applications are automatically generated skel xml Generated from ADIOS XML file No computation or communication. Standard build mechanism s3d_skel.xml s3d_params.xml Standard measurement strategies Extensible collection of applications skel src skel makefile skel submit Skel generates source code, makefile, and submission scripts GTS Comparison ADIOS-MPI Source files Makefile Submit scripts 100 GTS App Skel GTS make make deploy 10 Executables Skel_s3d Number of cores

12 SOA philosophy The overarching design philosophy of our framework is based on the Service-Oriented Architecture Used to deal with system/application complexity, rapidly changing requirements, evolving target platforms, and diverse teams Applications constructed by assembling services based on a universal view of their functionality using a well-defined API Service implementations can be changed easily Integrated simulation can be assembled using these services Manage complexity while maintaining performance/scalability Complexity from the problem (complex physics) Complexity from the codes and how they are Complexity of underlying disruptive infrastructure Complexity from coordination across codes and research teams

13 Data staging 101 Why staging? Decouple file system performance variations and limitations from application run time Enables optimizations based on dynamic number of writers High bandwidth data extraction from application with RDMA Scalable data movement with shared resources requires us to manage the transfers Asynchronous data movement, for low impact on application Managing interference Use small staging area for large data output Storage/memory hierarchies Using fast local storage lets us make up for capacity issues Staging fills this gap

14 Our discovery of data scheduling for asynchronous I/O Run me Overhead comparison for all evaluated scheduling mechanism 16 Stagers Naïve scheduling (Continuous Drain), can be slower than synchronous I/O Scheduling technique Smart scheduling can be scalable and much better than synchronous I/O

15 In situ processing Data movement is a deterrent to effective use of the data The output costs increase the runtime and force the user to reduce the output data Input costs can make up to 90% of the total running time for scientific workflows Our goal is to create an infrastructure to make it easy for you to forget the technology and focus on your science! Interface to apps for descrip on of data (ADIOS, etc.) Feedback Mul -resolu on methods Provenance Data Management Services Buffering Data Compression methods Plugins to the hybrid staging area Cool! Schedule Data Indexing (FastBit) methods Workflow Engine Run me engine Data movement Analysis Plugins Parallel and Distributed File System Visualiza on Plugins Adios-bp IDX HDF5 pnetcdf raw data Image data Viz. Client

16 Staging Revolution: Phase 1 Staging Area Staging Area Application Monolithic staging applications Multiple pipelines are separate staging areas Data movement is between each staging code High level of fault tolerance Staging Process 1 Storage Staging Process 2 Storage

17 Shared Space Abstraction in the Staging Area Semantically-specialized virtual shared space abstraction in the staging area Shared (distributed) data object Simple put/get/query API Supports application application, workflows E.g. ADIOS DataSpaces (HPDC10) Constructed on-the-fly on hybrid staging nodes Indexes data for quick access and retrieval Provides asynchronous coordination and interaction support Complements existing interaction/coordination mechanisms Code coupling using DataSpaces Maintain locality for in-situ exchange Complex geometry-based queries In-space (online) data transformation and manipulations

18 Phase 2: Introducing Plugins Staging Area Application Plugin A Plugin B Plugin C Plugin E Plugin D Disruptive step Staging codes broken down into multiple plugins Staging area is launched as a framework to launch plugins Data movement occurs between all plugins Scheduler decides resource allocations

19 Phase 3: Managing the staging area Managed Staging Area Application Plugin A Plugin B Plugin C Plugin E Plugin D Resources are co-allocated to plugins Plugins are executed consecutively, co-located plugins for no data movement Scheduler attempts to minimize data movement Co-located plugins

20 Hybrid staging Plugins executed within the application node Managed Staging Area Plugin A Application B 1 B 2 Plugin B Plugin E Plugin C Plugin D Next evolution of staging Move plugins/services back to application Largest computational capacity Integrate with plugins in the remote staging area

21 Third-Party Plugins in the Staging Area Data processing plugins in the hybrid staging area In-situ data processing Analytics pipelines Many issues Programming (data and control) models for plugins Deployment mechanisms Robustness, correctness, etc. Multiple approaches Code, binary, scripts, etc. E.g., ADIOS ActiveSpaces: Dynamically deploy custom application data transformation/filters on-demand and execute in staging area (DataSpaces) Provide the programming support to define custom data kernels to operate on data objects of interest Several implementations in ADIOS Runtime system to dynamically deploy ActiveSpace, SmartTap, etc. binary code to DataSpaces, and execute them on the relevant data objects Define a filter, implement and compile with the application E.g., min(), max(), sum(), avg(), etc.

22 Dividing up the pipeline On Compute Nodes 10 6 cores On Staging Nodes 10 3 cores Post processing 10^3 Disks Local Statistics Local features Binning of data Sorting within a bucket Compression Minimize cross node communication Global Statistics Global features Ordered sorts Indexing of data Compression Spatial correlation Topology mapping Interactive Visualization Minimize cross timestep communication Temporal Correlations Validation Everything else Especially those which require human interaction

23 Data Management (Data Reduction/in situ) Objectives Impact Develop a lossy and lossless compression methodology to reduce incompressible spatio-temporal double precision scientific data Ensure high compression rates with ISABELA, ISOBAR, while adhering to user-controlled accuracy bounds Provide fast and accurate approximate query processing technique directly over ISABELA-compressed scientific data ISABELA Compression Accuracy at 75% reduction in size Analysis of XGC1 Temperature Data Up to 85% reduction in storage on data from petascale simulation applications: S3D, GTS, XGC, Guaranteed bounds in per-point error, and overall 0.99 correlation, and 0.01 NRMSE with original data Communication-free, minimal overhead technique easily pluggable into any simulation application

24 Data Management (Multi resolution) Storage of the data on each process group in multiple levels for contiguous arrays Real-time decisions to decide which levels to stream to the staging resources, and which data to store Can reduce overall storage by real-time streaming/storing reduced representations of the data when it s within an error-bound

25

26 Conclusions and Future Direction ADIOS, developed by many institutions, is a proven DOE framework which blends together Self-describing file formats Staging methods with visualization and analysis plugins Advanced data management plugins, for data reduction, multi-resolution data reduction, and advanced workflow scheduling Allows for multiplexing of output using a novel SOA approach New in situ techniques are critical for application success on extreme scale architecture We still must use this to prepare data for further exploratory data analytics Hybrid staging integration with cloud resources for next generation HPC-Cloud services New customers and new technologies bring new challenges AMR Image processing Experimental and Sensor Data

27 Fusion 2006 Battelle Annual report, cover image ault.htm 2/16/2007 Supercomputing Keys Fusion Research 16/supercomputing_keys_fusion_research-1.html 7/14/2008 Researchers Conduct Breakthrough Fusion Simulation 14/researchers_conduct_breakthrough_fusion_sim ulation.html 8/27/2009 Fusion Gets Faster 27/fusion_gets_faster.html 4/21/2011 Jaguar Supercomputing Harnesses Heat for Fusion Energy 19/jaguar_supercomputer_harnesses_heat_for_fusi on_energy.html Combustion Community highlights 9/29/2009 ADIOS Ignites Combustion Simulations 29/adios_ignites_combustion_simulations.html Computer science 2/12/2009 Breaking the bottleneck in computer data flow 9/12/2010 Say Hello to the NEW ADIOS 12/say_hello_to_the_new_adios.html 2/28/2011 OLCF, Partners Release esimon Dashboard Simulation Tool 28/olcf_partners_release_esimon_dashboard_simu lation_tool.html 7/21/2011 ORNL ADIOS Team Releases Version 1.3 of Adaptive Input/Output System 21/ornl_adios_team_releases_version_1.3_of_adap tive_input_output_system.html

28 2008 I/O Pipeline Publications SOA 1. S. Klasky, et al., In Situ data processing for extremescale computing, appear SciDAC C. Docan, J. Cummings, S. Klasky, M. Parashar, Moving the Code to the Data Dynamic Code Deployment using ActiveSpaces, IPDPS F. Zhang, C. Docan, M. Parashar, S. Klasky, Enabling Multi-Physics Coupled Simulations within the PGAS Programming Framework, CCgrid C. Docan, S. Klasky, M. Parashar, DataSpaces: An Interaction and Coordination Framework for Coupled Simulation Workflows, HPDC 10. ACM, Chicago Ill. 5. Cummings, Klasky, et al., EFFIS: and End-to-end Framework for Fusion Integrated Simulation, PDP 2010, 6. C. Docan, J. Cummings, S. Klasky, M. Parashar, N. Podhorszki, F. Zhang, Experiments with Memory-to- Memory Coupling for End-to-End fusion Simulation Workflows, ccgrid2010, IEEE Computer Society Press N. Podhorszki, S. Klasky, et al.: Plasma fusion code coupling using scalable I/O services and scientific workflows. SC-WORKS C. Docan, M. Parashar and S. Klasky. DataSpaces: An Interaction and Coordination Framework for Coupled Simulation Workflows. Journal of Cluster Computing, 2011 Data Formats 8. Y. Tian, S. Klasky, H. Abbasi, J. Lofstead, R. Grout, N. Podhorszki, Q. Liu, Y. Wang, W. Yu, EDO: Improving Read Performance for Scientific Applications Through Elastic Data Organization, to appear Cluster Y. Tian, SRC: Enabling Petascale Data Analysis for Scientific Applications Through Data Reorganization, ICS 2011, First Place: Student Research Competition 10. M. Polte, J. Lofstead, J. Bent, G. Gibson, S. Klasky, "...And Eat it Too: High Read Performance in Write- Optimized HPC I/O Middleware File Formats," in In Proceedings of Petascale Data Storage Workshop 2009 at Supercomputing 2009, ed, S. Klasky, et al., Adaptive IO System (ADIOS), Cray User Group Meeting 2008.

29 2008 I/O Pipeline Publications Data SOA Staging Services, coupling using Cluster scalable Computing I/O services 2009, New and Orleans, scientific LA, August workflows SC-WORKS S. H. Klasky, Abbasi, et G. al., Eisenhauer, In Situ data S. Klasky, processing K. Schwan, for extremescale Wolf. computing, Just In Time: appear Adding SciDAC Value to IO Pipelines of Data Formats M. 17. C. Docan, M. Parashar, S. Klasky, Enabling High Speed Asynchronous Data Extraction and Transfer Using 2. C. High Docan, Performance J. Cummings, Applications S. Klasky, with M. JITStaging, Parashar, HPDC 8. DART, Y. Tian, Proceedings S. Klasky, H. of Abbasi, the 17J. Lofstead, R. Grout, N. Moving th International the Code to the Data Dynamic Code Symposium Podhorszki, on Q. High-Performance Liu, Y. Wang, W. Yu, Distributed EDO: Improving 13. Deployment H. Abbasi, M. using Wolf, ActiveSpaces, G. Eisenhauer, IPDPS S. Klasky, K. Schwan, Computing Read Performance (HPDC), for Boston, Scientific MA, USA, Applications IEEE Computer Through F. Zheng, DataStager: scalable data staging services 3. F. Zhang, C. Docan, M. Parashar, S. Klasky, Enabling Society Elastic Data Press, Organization, June to appear Cluster for petascale applications, Cluster Computer, Springer Multi-Physics Coupled Simulations within the PGAS 18. H. 9. Y. Abbasi, Tian, SRC: M. Wolf, Enabling K. Schwan, Petascale S. Klasky, Data Analysis Managed , pp , Vol 13, Issue 3, for Programming Framework, CCgrid Streams: Scientific Scalable Applications I/O, Through HPDC C. Docan, M. Parashar, S. Klasky: Enabling high-speed Data Reorganization, ICS 2011, First Place: Student Research Competition 4. C. asynchronous Docan, S. Klasky, data extraction M. Parashar, and DataSpaces: transfer using An Interaction DART. Concurrency and Coordination and Computation: Framework Practice for Coupled and 10. M. Polte, J. Lofstead, J. Bent, G. Gibson, S. Klasky, Simulation Experience Workflows, 22(9): HPDC 10. (2010) ACM, Chicago Ill. "...And Eat it Too: High Read Performance in Write- 15. Cummings, H. Abbasi, Wolf, Klasky, M., et Eisenhauer, al., EFFIS: G., and Klasky, End-to-end S., Schwan, Optimized HPC I/O Middleware File Formats," in In Framework K.,, Zheng, F. for Fusion DataStager: Integrated scalable Simulation, data staging PDP Proceedings of Petascale Data Storage Workshop 2010, services for petascale applications. In Proceedings of 2009 at Supercomputing 2009, ed, the 18th ACM international Symposium on High C. Docan, J. Cummings, S. Klasky, M. Parashar, N. S. Klasky, et al., Adaptive IO System (ADIOS), Cray Performance Distributed Computing (Garching, Podhorszki, F. Zhang, Experiments with Memory-to- User Group Meeting Germany, June 11-13, 2009). HPDC '09. ACM, New Memory York, NY, Coupling for End-to-End fusion Simulation Workflows, ccgrid2010, IEEE Computer Society Press H. Abbasi, J. Lofstead, F. Zheng, S. Klasky, K. Schwan, M. Wolf, Extending I/O through High Performance Data 7. N. Podhorszki, S. Klasky, et al.: Plasma fusion code

30 2008 I/O Pipeline Publications Usability of Optimizations 19. J. Lofstead, F. Zheng, Q. Liu, S. Klasky, R. Oldfield, T. Kordenbrock, Karsten Schwan, Matthew Wolf. "Managing Variability in the IO Performance of Petascale Storage Systems". In Proceedings of SC 10. New Orleans, LA. November J. Lofstead, M. Polte, G. Gibson, S. Klasky, K. Schwan, R. Oldfield, M. Wolf, Six Degrees of Scientific Data: Reading Patterns for extreme scale science IO, HPDC Y. Xiao, I. Holod, W. L. Zhang, S. Klasky, Z. H. Lin, Fluctuation characteristics and transport properties of collisionless trapped electron mode turbulence, Physics of Plasmas, 17, J. Lofstead, F. Zheng, S. Klasky, K. Schwan, Adaptable Metadata Rich IO Methods for Portable High Performance IO, IPDPS 2009, IEEE Computer Society Press Lofstead, Zheng, Klasky, Schwan, Input/Output APIs and Data Organization for High Performance Scientific Computing, PDSW SC J. Lofstead, S. Klasky, K. Schwan, N. Podhorszki, C. Jin, Flexible IO and Integration for Scientific Codes Through the adaptable IO System, Challenges of Large Applications in Distributed Environments (CLADE), June Y. Tian, S. Klasky, B. Wang, H. Abbasi, N. Podhorszki, R. Grout, M. Wolf, Chimera: Efficient Multiresolution Data Representation through Chimeric Organization, submitted to IPDPS 2012.

31 2008 I/O Pipeline Publications Data Management 26. J. Kim, H. Abbasi, C. Docan, S. Klasky, Q. Liu, N. Podhorszki, A. Shoshani, K. Wu, Parallel In Situ Indexing for Data- Intensive Computing, accepted 2011 LDAV 27. T. Critchlow, et al., Working with Workflows: Highlights from 5 years Building Scientific Workflows, SciDAC S. Lakshminarasimhan, N. Shah, Stephane Ethier, Scott Klasky, Rob Latham, Rob Ross, Nagiza F. Samatova, Compressing the Incompressible with ISABELA: In Situ Reduction of Spatio-Temporal Data, Europar S. Lakshminarasimhan, J. Jenkins, I. Arkatkar, Z. Gong, H. Kolla, S. H. Ku, S. Ethier, J. Chen, C.S. Chang, S. Klasky, R. Latham, R. Ross,, N. F. Samatova, ISABELA-QA: Querydriven Analytics with ISABELA-compressed Extreme-Scale Scientific Data, to appear SC K. Wu, R. Sinha, C. Jones, S. Ethier, S. Klasky, K. L. Ma, A. Shoshani, M. Winslett, Finding Regions of Interest on Toroidal Meshes, to appear in Journal of Computational Science and Discovery, J. Logan, S. Klasky, H. Abbasi, et al., Skel: generative software for producing I/O skeletal applications, submitted to Workshop on D 3 Science, E. Schendel, Y. Jin, N. Shah, J. Chen, C.S. Chang, S.-H. Ku, S. Ethier, S. Klasky, R. Latham, R. Ross, N. Samatova, ISOBAR Preconditioner for Effective and Highthroughput Lossless Data Compression, submitted to ICDE I. Arkatkar, J. Jenkins, S. Lakshminarasimhan, N. Shah, E. Schendel, S. Ethier, CS Chang, J. Chen, H. Kolla, S. Klasky, R. Ross, N. Samatova, ALACRI2TY: Analyticsdriven lossless data compression for rapid in-situ indexing, storing, and querying, submitted to ICDE Z. Gong, S. Lakshminarasimhan, J. Jenkins, H. Kolla, S. Ethier, J. Chen, R. Ross, S. Klasky, N. Samatova, Multilevel layout optimizations for efficient spatio-temporal queries of ISABELA-compressed data, submitted to ICDE Y. Jin, S. Lakshminarasimhan, N. Shah, Z. Gong, C Chang, J. Chen, S. Ethier, H. Kolla, S. Ku, S. Klasky, R. Latham, R. Ross, K. Schuchardt, N. Samatova, S-preconditioner for Multifold Data Reduction with Guaranteed user-controlled accuracy, ICDM 2011.

32 2008 I/O Pipeline Publications Simulation Monitoring (CTS 2008). 36. R. Tchoua, S. Klasky, N. Podhorszki, B. Grimm, A. Khan, E. Santos, C. Silva, P. Mouallem, M. Vouk, "Collaborative Monitoring and Analysis for Simulation Scientist", in Proceedings of CTS R. Tchoua, S. Klasky, et. Al, Schema for Collaborative Monitoring and Visualization of Scientific Data, PDAC P. Mouallem, R. Barreto, S. Klasky, N. Podhorszki, M. Vouk Tracking Files in the Kepler Provenance Framework. In Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM 2009), Marianne Winslett (Ed.). Springer-Verla, E. Santos, J. Tierny, A. Khan, B. Grimm, L. Lins, J. Freire, V. Pascucci, C. Silva, S. Klasky, R. Barreto, N. Podhorszki, Enabling Advanced Visualization Tools in a Web-Based Simulation Monitoring System, escience R. Barreto, S. Klasky, N. Podhorszki, P. Mouallem, M. Vouk: Collaboration Portal for Petascale Simulations, International Symposium on Collaborative Technologies and Systems, pp , Baltimore, Maryland, May R. Barreto, S. Klasky, C. Jin, N. Podhorszki, Framework for Integrating End to End SDM Technologies and Applications (FIESTA)., Chi 09 workshop, The Changing Face of Digital Science, Klasky, et al., Collaborative Visualization Spaces for Petascale Simulations, to appear in the 2008 International Symposium on Collaborative Technologies and Systems

33 2008 I/O Pipeline Publications In situ processing 43. A. Shoshani, et al., The Scientific Data Management Center: Available Technologies and Highlights, SciDAC A. Shoshani, S. Klasky, R. Ross, Scientific Data Management: Challenges and Approaches in the Extreme Scale Era, SciDAC F. Zheng, H. Abbasi, C. Docan, J. Lofstead, Q. Liu, S. Klasky, M. Parashar, N. Podhorszki, K. Schwan, M. Wolf, PreDatA - Preparatory Data Analytics on Peta-Scale Machines, IPDPS 2010, IEEE Computer Society Press S. Klasky, et al., High Throughput Data Movement,, Scientific Data Management: Challenges, Existing Technologies, and Deployment, Editors: A. Shoshani and D. Rotem, Chapman and Hall, G. Eisenhauer, M. Wolf, H. Abbasi, S. Klasky, K. Schwan, A Type System for High Performance Communication and Computation, submitted to Workshop on D 3 Science, F. Zhang, C. Docan, M. Parashar, S. Klasky, N. Podhorszki, H. Abbasi, Enabling In-situ Execution of Coupled Scientific Workflow on Multi-core Platform, submitted IPDPS F. Zheng, H. Abbasi, J. Cao, J. Dayal, K. Schwan, M. Wolf, S. Klasky, N. Podhorszki, In-Situ I/O Processing: A Case for Location Flexibility, PDSW F. Zheng, J. Cao, J. Dayal, G. Eisenhauer, K. Schwan, M. Wolf, H. Abbasi, S. Klasky, N. Podhorszki, High End Scientific Codes with Computational I/O Pipelines: Improving their End-to-End Performance, PDAC K. Moreland, R. Oldfield, P. Marion, S. Jourdain, N. Podhorszki, C. Docan, M. Parashar, M. Hereld, M. Papka, S. Klasky, Examples of In Transit Visualization, PDAC Misc. 52. C. S. Chang, S. Ku, et al., Whole-volume integrated gyrokinetic simulation of plasma turbulence in realistic diverted-tokamak geometry, SciDAC C. S. Chang, S. Klasky, et al., Towards a first-principles integrated simulation of tokamak edge plasmas, SciDAC Z Lin, Y. Xiao, W. Deng, I. Holod, C. Kamath, S. Klasky, Z. Wang, H. Zhang, Size scaling and nondiffusive features of electron heat transport in multi-scale turbulence, IAEA Z. Lin, Y. Xiao, I. Holod, W. Zhang, W. Deng, S. Klasky, J. Lofstead, C. Kamath, N. Wichmann, Advanced simulation of electron heat transport in fusion plasmas, SciDAC 2009.