1. 1 Dr. Frederica Darema Senior Science and Technology Advisor NSF Research and Technology Advances in Systems Software for Emerging Computer Systems EDGE 2006 Workshop

2. 2 Outline The BIG PICTURE Applications Directions Computing Platforms Directions Research and Technology Directions Examples of some advances Future Challenges and Opportunities

3. Science, Engineering, and “Commercial” Applications Environments: how are they shaping in the future What does it entail for: Multicore Processors and… for Computing in the Larger-Scale

4. 4 –Mostly monolithic –Mostly one programming language –Multi-Modular –Multi-Language –Multi-Developers –Multi-Source Data Present / Future –Computation Intensive –Batch –Hours/days –Computation Intensive –Data Intensive –Real Time –Few Minutes/hours –Visualization –Interactive Steering –Integrated Simulations&Experiments Dynamic Data Driven Applications Systems Past Applications Directions

5. 5 Platforms Directions Distributed Platform MPPNOW SAR tac-com data base fire cntl fire cntl alg accelerator data base SP …. –Vector Processors –SIMD MPPs –Distributed Memory MPs –Shared Memory MPs Latencies –variable (internode, intranode) Bandwidths –different for different links –different based on traffic –Distributed Platforms, Heterogeneous Computers and Networks Heterogeneity –architecture (computer &network) –node power (supernodes, MCP) Past Present/Future Petaflops Platform (Grid-in-a-Box)

6. 6 EXAMPLE OF EMERGING DIRECTIONS: Dynamic Data Driven Application Systems (DDDAS) New Direction for applications/simulations and measurement methodology Multi-agency DDDAS program – NSF, NIH, NOAA with cooperation with the EU/IST & e-Sciences Programs (www.cise.nsf.gov/dddas)

7. 7 Measurements Experiments Field-Data User Theory (First Principles) Simulations (Math.Modeling Phenomenology) Experiment Measurements Field-Data (on-line/archival) User Theory (First Principles) Simulations (Math.Modeling Phenomenology Observation Modeling Design) OLD (serialized and static ) NEW PARADIGM (Dynamic Data-Driven Simulation Systems) Challenges : Application Simulations Development Algorithms Measurement Instruments Interfaces Computing Systems Support Dynamic Feedback & Control Loop What is DDDAS (Symbiotic Measurement&Simulation Systems)

8. 8 Beyond Grid Computing “Extended Grid’: the Application Platform is the computational&measurement system Applications Computational Platforms Instruments Sensors Archival/ Stored Data MeasurementsComputational Grids

9. 9 Experimental Dynamic Observations Users ADaM ADAS Tools NWS National Static Observations & Grids Mesoscale Weather Local Observations Local Physical Resources Remote Physical (Grid) Resources Virtual/Digital Resources and Services LEAD: Users INTERACTING with Weather Interaction Level II: Tools and People Driving Observing Systems – Dynamic Adaptation

10. 10

11. 11 Examples of Computational Req’s - examples from DDDAS applications - * often results needed in Real-Time or near-RT * Water Pollution/Contaminant Transport/Detection: Today’s problem: 500nodes- 4.4Pflops;1.2GBmem;.02GB/s -> Large/Projected problem: 10,000nodes-212Pflops; 10.2GBmem;.9GB/sec) Chemical Pollution/Contaminant Transport/Detection Today’s problem: 2000nodes(Lemieux); 4TBmem ; 5hrs -> Large/Projected problem: 10Knodes; 20TBmem; 1hr results in Real-Time (or near RT): 50-100Knodes Protein Folding Today’s problem: 1024nodes(IBM-BlueGeneL); 6/7days for 1 protein (w 150aminoacids) ElectricPowerGrid Today’s problem: 100Gflops; 50MBmem Aircraft modeling Today’s problem: Full FEM&CFD: 384,000cpu-hrs; 320GBmem ROM: 72secs; 78KB Fire Propagation: Today’s problem: FireModel: 100procs(BG, Teragrid clusters); 30GBmem; 1hr-5hrs Coupled Weather/Fire: 100-1000nodes; 200-400GBmem

12. 12 So… Processing at multiple levels Computation and data processing both: at the application and the instruments/sensors side Multicores in high-end platforms, workstations, visualization servers, data servers, etc, … Multicores at application side Multicores at the data collection side …. MULTICORES EVERYWHERE!!!

13. 13 Some Challenges Programmability –models of concurrency, multiple heterogeneous models optimized performance –application –system scalability across multiple levels –application algorithms –systems software fault tolerance, recovery, reliability, security power management verification, validation ….. These challenges have been articulated for years on the past and present platforms Multicores add to the complexity of all the above

14. 14 The need for a holistic approach Large-Scale Systems does not entail only “flops” (Giga-, Tera-, Peta-, Zetta-,…) Large-scale “parallel” systems are the POWERFUL nodes/platforms - in balance with other resources in the system Analogy: the “stars” and the “galaxy” within the “cosmos” Methods andTools needed at all levels, and they need to work together synergistically

15. 15 Performance Engineering Dynamic Compilers & Application Composition Dynamic Data-Driven Application Systems -- Symbiotic Measurement&Simulation Systems Large-Scale Systems (e.g. Enabling DDDAS) Systems Software (NGS: 1998-2004) (CSR/AES&SMA: 2004-todate) Multidisciplinary Research Applications Modeling & Measurements CS Research

16. 16 System Modeling and Analysis (SMA) (a component of the Computer Systems Research Program) (CSR Program) Develop methods and tools for modeling, measuring, analyzing, evaluating, and predicting the performance and dependability of complex computing and communications systems taking a “system level view” Topics of Interest Hardware and Software modeling –methods tools and measurements, providing multimodal, hierarchical or multilevel modeling and analysis capabilities of such systems; –methods that describe components of the system, but also the system as a total, and enable assessment of the effects of individual hardware and software layers and components of these systems; –ability to describe the system in multiple levels of detail (characteristics and time-scales); –combine different methods of describing components and layers

17. 17 System Modeling and Analysis (SMA) Topics of Interest (cont’d) Novel modeling and measurement approaches –Develop capabilities to describe, analyze and predict the behavior of the components as well as the systems; Analysis and prediction due to changes in the application, system software, hardware; multilevel approaches and multi-modal approaches Performance Frameworks –combine tools in “plug-and-play” fashion –multiple views of the system

18. 18 Multiple views of the system The applications’ view Authenication/ Authorization Dependability Services Distributed Systems Management Distributed, Heterogeneous, Dynamic, Adaptive Computing Platforms and Networks Device Technology... CPU Technology Visualization Scalable I/O Data Management Archiving/Retrieval Services Other Services... Collaboration Environments Distributed Applications Memory Technology Application Models OS Scheduler Models Architecture / Network Models Memory Models IO / File Models... Languages Libraries Tools Compilers

19. 19 Advanced Execution Systems (AES) (a component of the Computer Systems Research Program) (CSR Program) Seeks to create systems software to facilitate the development and runtime support of complex applications executing on large, heterogeneous high-end computing and grid platforms AES emphasizes runtime compiling systems and application composition systems interface with the underlying operating systems services and incorporating systems modeling and analysis methods and tools. Topics of Interest Novel Compiler Technology that go beyond the standard static notion of a compiler –for example by embedding a portion of the compiler in the runtime and endowing the system with resource awareness and adaptive mapping capabilities; –new compiler techniques for determining functional and data dependencies across multiple levels of memory hierarchy and across platforms; –mechanisms for matching an application’s resource needs to underlying resources when both are changing as the application executes

20. 20 Advanced Execution Systems (AES) Topics of Interest Programming models and tools –expressing application partitioning across distributed, heterogeneous computing platforms; application-level checkpointing and recovery Application composition system (ACS) technology –constructing applications to fit the available resources and to adapt to changes in the underlying execution environment; –methods for automatically selecting application components; –creating knowledge bases for application components; interfacing with the underlying computing platform models to determine suitable application components; –and developing appropriate application component libraries and interfaces so the run-time portion of the RCS can link to such libraries.

21. 21 Dynamically Link & Execute The AES component develops technology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition Application Model Application Program Application Intermediate Representation Compiler Front-End Compiler Back-End Performance Measuremetns & Models Distributed Programming Model Application Components & Frameworks Dynamic Analysis Situation Launch Application (s) Distributed Platform Adaptable computing Systems Infrastructure Distributed Computing Resources MPPNOW SAR tac-com data base fire cntl fire cntl alg accelerator data base SP ….

22. 22 Examples of areas funded Programming Models, Languages, Environments –“legacy models” (MPI), to high-level, domain, hierarchical multithreading, software component libraries, dynamic workflow, streaming environments (languages/compilers), Compiler methods and tools –program analysis methods – program transformation methods –program Phase detection – dynamic detection –combine: static, dynamic, and feedback methods; Continuous optimization methods –scheduling, scalability across hierarchies –checkpoint & recovery (system level, application level) Real-Time systems and integration (with server, high-end, etc… environments) Systems management including power-management –optimization & constraints ( performance&power optimization) Validation, Verification, Testing System Modeling and Analysis –Modeling of applications, algorithms, platforms (at all levels) –performance, dependability (performability), reliability –multi-modal modeling, power modeling (at all levels : application, computational platforms, processor/multicores), Performance specification (languages, compilers); performance frameworks –Fast real-time or near-real-time simulation methods Have seen an increase in all these areas with respect to MULTICORES

23. 23 Summary Thoughts MultiCoreProcessors provide an opportunity for: enhanced capabilities in computation, communication and data management Multicores present the promise of populating all levels of computational platforms and environments They should be viewed in the presence of other resources’ heterogeneity, dynamicity, adaptivity Multicores cannot exist in isolation – they will be “nodes” in other systems, high-end platforms, servers, real-time systems, instruments, and grids (InformationPowerGrid, TeraGrid) Complexity of applications and platforms presents a significant opportunity for innovative research and technology in systems software (methods & tools) Multicores will resurrect and build upon ideas/methods started in 80’s shared memory “parallel processing” and the recent advances for distributed systems Need to advance the technologies that will automate the mapping of such complex and dynamic applications on complex platforms with multiple and heterogeneous levels of processors, memory, and networks An important item: do we nurture a critical mass of people that will work on these challenges? (where are the compiler people to address/contribute to these challenges?!!!) { I personally hope that the opportunities of MultiCoreProcessors will attract the attention and the people needed}