linking programming models between grids web 2 0 and multicore n.
Skip this Video
Loading SlideShow in 5 Seconds..
Linking Programming models between Grids, Web 2.0 and Multicore PowerPoint Presentation
Download Presentation
Linking Programming models between Grids, Web 2.0 and Multicore

Loading in 2 Seconds...

play fullscreen
1 / 47
Download Presentation

Linking Programming models between Grids, Web 2.0 and Multicore - PowerPoint PPT Presentation

Download Presentation

Linking Programming models between Grids, Web 2.0 and Multicore

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Linking Programming models between Grids, Web 2.0 and Multicore Distributed Programming Abstractions Workshop NESC Edinburgh UK May 31 2007 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401

  2. Points in Talk I • All parallel programming projects more or less fail • All distributed programming projects report success • There are several hundred in Grid workflow area alone • Few constraints on distributed programming • Composition (in distributed computing) v decomposition (in parallel computing) • There is not much difference between distributed programming and a key paradigm of parallel computing (functional parallelism) • Pervasive use of  64 core chips in the future will often require one to build a Grid on a chip i.e. to execute a traditional distributed application on a chip • XML is a pretty dubious syntax for expressing programs • Web 2.0 is pretty scruffy but there are some large companies and many users behind it. • Web 2.0 and Grids will converge and features of both will survive or disappear in merged environment • Web 2.0 has a more plausible approach to distributed programming than Web Services/Grids • Dominant Distributed Programming models will support Multicore, Web 2.0 and Grids

  3. Some More points • Services could be universal abstraction in parallel and distributed computing • Whereas objects could not be universal so perhaps should move away from their use • Gateways/Portals (Portlets, Widgets, Gadgets) are natural user (application usage) interface to a collection of services • Important Data (SQL, WFS, RSS Feeds) abstractions • Divide Parallel Programming Run-time (matching application structure) into 3 or 4 Broad classes • Inter-entity communication time characteristic of different programming model • 1-5 µs for MPI/Thread switching to 1-1000 milliseconds for services on the Grid and 25 µs for services inside a chip • Multicore Commodity Programming Model • Marine corps write libraries in “HLA++”, MPI or dynamic threads (internally one microsecond latency) expressed as services • Services composed/mashuped by “millions” • Many composition (coordination) or mashup approaches • Functional (cf. Google Map Reduce for data transformations) • Dataflow • Workflow • Visual • Script • The difficulties of making effective use of multicore chips will so great that it will be main driver of new programming environments • Microsoft CCR DSS is good example of unification of parallel and distributed computing

  4. Some Details • See or more conventionally • Web 2.0 and Grid Tutorial • • • Multicore and Parallel Computing Tutorial • • “Web 2.0” citation site

  5. Web 2.0 and Web Services I • Web Services have clearly defined protocols (SOAP) and a well defined mechanism (WSDL) to define service interfaces • There is good .NET and Java support • The so-called WS-* (WS-Nightmare) specifications provide a rich sophisticated but complicated standard set of capabilities for security, fault tolerance, meta-data, discovery, notification etc. • “Narrow Grids” build on Web Services and provide a robust managed environment with growing adoption in Enterprise systems and distributed science (e-Science) • We can use the term Grids strictly as NarrowGrids that are collections of Web Services (or even more strictly OGSA Grids) or just call any collections of services as “Broad Grids” which actually is quite often done • Web 2.0 supports a similar architecture to Web services but has developed in a more chaotic but remarkably successful fashion with a service architecture with a variety of protocols including those of Web and Grid services • Over 400 Interfaces defined at • One can easily combine SOAP (Web Service) based services/systems with HTTP messages but the “lowest common denominator” suggests additional structure/complexity of SOAP will not easily survive

  6. Web 2.0 and Web Services II • Web 2.0 also has many well known capabilities with Google Maps and Amazon Compute/Storage services of clear general relevance • There are also Web 2.0 services supporting novel collaboration modes and user interaction with the web as seen in social networking sites and portals such as: MySpace, YouTube, Connotea, Slideshare …. • I once thought Web Services were inevitable but this is no longer clear to me • Web services are complicated, slow and non functional • WS-Security is unnecessarily slow and pedantic (canonicalization of XML) • WS-RM (Reliable Messaging) seems to have poor adoption and doesn’t work well in collaboration • WSDM (distributed management) specifies a lot • There are de facto standards like Google Maps and powerful suppliers like Google which “define the rules”

  7. Attack of the Killer Multicores • Today commodity Intel systems are sold with 8 cores spread over two processors • Specialized chips such as GPU’s and IBM Cell processor have substantially more cores • Moore’s Law implies and will be satisfied by and imply exponentially increasing number of cores doubling every 1.5-3 Years • Modest increase in clock speed • Intel has already prototyped a 80 core Server chip ready in 2011? • Huge activity in parallel computing programming (recycled from the past?) • Some programming models and application styles similar to Grids • We will have a Grid on a chip …………….

  8. Grids meet Multicore Systems • The expected rapid growth in the number of cores per chip has important implications for Grids • With 16-128 cores on a single commodity system 5 years from now one will both be able to build a Grid like application on a chip and indeed must build such an application to get the Moore’s law performance increase • Otherwise you will “waste” cores ….. • One will not want to reprogram as you move your application from a 64 node cluster or transcontinental implementation to a single chip Grid • However multicore chips have a very different architecture from Grids • Shared not Distributed Memory • Latencies measured in microseconds not milliseconds • Thus Grid and multicore technologies will need to “converge” and converged technology model will have different requirements from current Grid assumptions

  9. Grid versus Multicore Applications • It seems likely that future multicore applications will involve a loosely coupled mix of multiple modules that fall into three classes • Data access/query/store • Analysis and/or simulation • User visualization and interaction • This is precisely mix that Grids support but Grids of course involve distributed modules • Grids and Web 2.0 use service oriented architectures to describe system at module level – is this appropriate model for multicore programming? • Where do multicore systems get their data from?

  10. Today Tomorrow RMS: Recognition Mining Synthesis Recognition Mining Synthesis Is it …? What is …? What if …? Find a model instance Create a model instance Model Model-less Real-time streaming and transactions on static – structured datasets Very limited realism Model-based multimodal recognition Real-time analytics on dynamic, unstructured, multimodal datasets Photo-realism and physics-based animation Intel has probably most sophisticated analysis of future “killer” multicore applications – they are “just” standard Grid and parallel computing 10

  11. Recognition Mining Synthesis What is a tumor? Is there a tumor here? What if the tumor progresses? It is all about dealing efficiently with complex multimodal datasets Images courtesy: 11

  12. Intel’s Application Stack PC07Intro 12

  13. Role of Data in Grid/Multicore I • One typically is told to place compute (analysis) at the data but most of the computing power is in multicore clients on the edge • These multicore clients can get data from the internet i.e. distributed sources • This could be personal interests of client and used by client to help user interact with world • It could be cached or copied • It could be a standalone calculation or part of a distributed coordinated computation (SETI@Home) • Or they could get data from set of local sensors (video-cams and environmental sensors) naturally stored on client or locally to client

  14. Role of Data in Grid/Multicore • Note that as you increase sophistication of data analysis, you increase ratio of compute to I/O • Typical modern datamining approach like Support Vector Machine is sophisticated (dense) matrix algebra and not just text matching • • Time complexity of Sophisticated data analysis will make it more attractive to fetch data from the Internet and cache/store on client • It will also help with memory bandwidth problems in multicore chips • In this vision, the Grid “just” acts as a source of data and the Grid application runs locally

  15. Multicore Programming Paradigms At a very high level, there are three or four broad classes of parallelism Coarse grain functional parallelism typified by workflow and often used to build composite “metaproblems” whose parts are also parallel “Compute-File”, Database/Sensor, Community, Service, Pleasing Parallel (Master-worker) are sub-classses Large Scale loosely synchronous data parallelism where dynamic irregular work has clear synchronization points as in most large scale scientific and engineering problems Fine grain (asynchronous) thread parallelism as used in search algorithms which are often data parallel (over choices) but don’t have universal synchronization points Discrete Event Simulations are either a fourth class or a variant of thread parallelism PC07Intro 15

  16. Data Parallel Time Dependence t4 t3 t2 t1 t0 • A simple form of data parallel applications are synchronous with all elements of the application space being evolved with essentially the same instructions • Such applications are suitable for SIMD computers and run well on vector supercomputers (and GPUs but these are more general than just synchronous) • However synchronous applications also run fine on MIMD machines • SIMD CM-2 evolved to MIMD CM-5 with same data parallel language CMFortran • The iterative solutions to Laplace’s equation are synchronous as are many full matrix algorithms Application Time Synchronous Synchronization on MIMD machines is accomplished by messaging It is automatic on SIMD machines! Application Space Identical evolution algorithms

  17. Local Messaging for Synchronization CommunicationPhase CommunicationPhase CommunicationPhase ComputePhase ComputePhase ComputePhase • MPI_SENDRECV is typical primitive • Processors do a send followed by a receive or a receive followed by a send • In two stages (needed to avoid race conditions), one has a complete left shift • Often follow by equivalent right shift, do get a complete exchange • This logic guarantees correctly updated data is sent to processors that have their data at same simulation time ……… Application and Processor Time 8 Processors CommunicationPhase Application Space

  18. Loosely Synchronous Applications This is most common large scale science and engineering and one has the traditional data parallelism but now each data point has in general a different update Comes from heterogeneity in problems that would be synchronous if homogeneous Time steps typically uniform but sometimes need to support variable time steps across application space – however ensure small time steps are t = (t1-t0)/Integer so subspaces with finer time steps do synchronize with full domain The time synchronization via messaging is still valid However one no longer load balances (ensure each processor does equal work in each time step) by putting equal number of points in each processor Load balancing although NP complete is in practice surprisingly easy Application Time t4 t3 t2 t1 t0 Application Space Distinct evolution algorithms for each data point in each processor

  19. MPI Futures? • MPI likely to become more important as multicore systems become more common • Should use MPI when MPI needed and use other messaging for other cases (such as linking services) where different features/performance appropriate • MPI has too many primitives which will handicap broad implementation/adoption • Perhaps only have one collective primitive like CCR which allows general collective operations to be built by user

  20. Fine Grain Dynamic Applications Here there is no natural universal ‘time’ as there is in science algorithms where an iteration number or Mother Nature’s time gives global synchronization Loose (zero) coupling or special features of application needed for successful parallelization In computer chess, the minimax scores at parent nodes provide multiple dynamic synchronization points Application Time Application Time Application Space Application Space

  21. Computer Chess Increasing search depth • Thread level parallelism unlike position evaluation parallelism used in other systems • Competed with poor reliability and results in 1987 and 1988 ACM Computer Chess Championships

  22. Discrete Event Simulations Battle of Hastings • These are familiar in military and circuit (system) simulations when one uses macroscopic approximations • Also probably paradigm of most multiplayer Internet games/worlds • Note Nature is perhaps synchronous when viewed quantum mechanically in terms of uniform fundamental elements (quarks and gluons etc.) • It is loosely synchronous when considered in terms of particles and mesh points • It is asynchronous when viewed in terms of tanks, people, arrows etc. • Circuit simulationscan be done looselysynchronously but inefficient as many inactive elements

  23. Programming Models • The three major models are supported by HPCS languages which are very interesting but too monolithic • So the Fine grain thread parallelism and Large Scale loosely synchronous data parallelism styles are distinctive to parallel computing while • Coarse grain functional parallelism of multicore overlaps with workflows from Grids and Mashups from Web 2.0 • Seems plausible that a more uniform approach evolve for coarse grain case although this is least constrained of programming styles as typically latency issues are not critical • Multicore would have strongest performance constraints • Web 2.0 and Multicore the most important usability constraints • A possible model for broad use of multicores is that the difficult parallel algorithms are coded as libraries (Fine grain thread parallelism and Large Scale loosely synchronous data parallelism styles) while the general user uses composes with visual interfaces, scripting and systems like Google MapReduce

  24. Google MapReduceSimplified Data Processing on Large Clusters This is a dataflow model between services where services can do useful document oriented data parallel applications including reductions The decomposition of services onto cluster engines is automated The large I/O requirements of datasets changes efficiency analysis in favor of dataflow Services (count words in example) can obviously be extended to general parallel applications There are many alternatives to language expressing either dataflow and/or parallel operations and indeed one should support multiple languages in spirit of services PC07Intro 24

  25. Programming Models • The services and objects in distributed computing are usually “natural” (come from application) whereas parts connected by MPI (or created by parallelizing compiler) come from “artificial” decompositions and not naturally considered services • Services in multicore (parallel computing) are original modules before decomposition and its these modules that coarse grain functional parallelism addresses • Most of “difficult” issues in parallel computing concern treatment of decomposition

  26. Parallel Software Paradigms: Top Level • In the conventional two-level Grid/Web Service programming model, one programs each individual service and then separately programs their interaction • This is Grid-aware Services programming model • SAGA supports Grid-aware programs? • This is generalized to multicore with “Marine Corps” programming services for “difficult” cases • Loosely Synchronous • Fine Grain threading • Discrete Event Simulation • “Average” Programmer produces mashups or workflows from these parallelized services

  27. The Marine Corps Lack of Programming Paradigm Library Model • One could assume that parallel computing is “just too hard for real people” and assume that we use a Marine Corps of programmers to build as libraries excellent parallel implementations of “all” core capabilities • e.g. the primitives identified in the Intel application analysis • e.g. the primitives supported in Google MapReduce, HPF, PeakStream, Microsoft Data Parallel .NET etc. • These primitives are orchestrated (linked together) by overall frameworks such as workflow or mashups • The Marine Corps probably is content with efficient rather than easy to use programming models

  28. Component Parallel and Program Parallel • Component parallel paradigm is where one explicitly programs the different parts of a parallel application with the linkage either specified externally as in workflow or in components themselves as in most other component parallel approaches • In Grids, components are natural • In Parallel computing, components are produced by decomposition • In the program parallel paradigm, one writes a single program to describe the whole application and some combination of compiler and runtime breaks up the program into the multiple parts that execute in parallel • Note that a program parallel approach will often call a built in runtime library written in component parallel fashion • A parallelizing compiler could call an MPI library routine • Could perhaps better call “Program Parallel” as “Implicitly Parallel” and “Component Parallel” as “Explicitly Parallel”

  29. Component Parallel and Program Parallel • Program Parallel approaches include • Data structure parallel as in Google MapReduce, HPF (High Performance Fortran), HPCS (High-Productivity Computing Systems) or “SIMD” co-processor languages (PeakStream, ClearSpeed and Microsoft Data Parallel .NET) • Parallelizing compilers including OpenMP annotation • Note OpenMP and HPF have failed in some sense for large scale parallel computing (writing algorithm in standard sequential languages throws away information needed for parallelization) • Component Parallel approaches include • MPI (and related systems like PVM) parallel message passing • PGAS (Partitioned Global Address Space CAF, UPC, Titanium, HPJava ) • C++ futures and active objects • CSP … Microsoft CCR and DSS • Workflow and Mashups • Discrete Event Simulation

  30. Why people like MPI! After Optimization of UPC cluster cluster • Jason J Beech-Brandt, and Andrew A. Johnson, at AHPCRC Minneapolis • BenchC is unstructured finite element CFD Solver • Looked at OpenMP on shared memory Altix with some effort to optimize • Optimized UPCon severalmachines • MPI always goodbut other approacheserratic • Other studies reach similar conclusions?

  31. The world does itself in large numbers! Web 2.0 Systems are Portals, Services, Resources • Captures the incredible development of interactive Web sites enabling people to create and collaborate

  32. Mashup Tools are reviewed at Workflow Tools are reviewed by Gannon and Fox Both include scripting in PHP, Python, sh etc. as both implement distributed programming at level of services Mashups use all types of service interfaces and do not have the potential robustness (security) of Grid service approach Typically “pure” HTTP (REST) Mashups v Workflow?

  33. Web 2.0 APIs • has (May 14 2007) 431 Web 2.0 APIs with GoogleMaps the most often used in Mashups • This site acts as a “UDDI” for Web 2.0

  34. The List of Web 2.0 API’s • Each site has API and its features • Divided into broad categories • Only a few used a lot (42 API’s used in more than 10 mashups) • RSS feed of new APIs • Amazon S3 growing in popularity

  35. google maps virtual earth 411sync yahoo! search yahoo! geocoding technorati netvibes yahoo! images trynt amazon ECS yahoo! local google search flickr ebay youtube amazon S3 REST SOAP XML-RPC REST, XML-RPC REST, XML-RPC, SOAP REST, SOAP JS Other APIs/Mashups per Protocol Distribution Number of APIs Number of Mashups

  36. Growing number of commercial Mashup Tools 4 more Mashups each day • For a total of 1906 April 17 2007 (4.0 a day over last month) • Note ClearForest runs Semantic Web Services Mashup competitions (not workflow competitions) • Some Mashup types: aggregators, search aggregators, visualizers, mobile, maps, games

  37. Implication for Grid Technology of Multicore and Web 2.0 I • Web 2.0 and Grids are addressing a similar application class although Web 2.0 has focused on user interactions • So technology has similar requirements • Multicore differs significantly from Grids in component location and this seems particularly significant for data • Not clear therefore how similar applications will be • Intel RMS multicore application class pretty similar to Grids • Multicore has more stringent software requirements than Grids as latter has intrinsic network overhead 37

  38. Implication for Grid Technology of Multicore and Web 2.0 II • Multicore chips require low overhead protocols to exploit low latency that suggests simplicity • We need to simplify MPI AND Grids! • Web 2.0 chooses simplicity (REST rather than SOAP) to lower barrier to everyone participating • Web 2.0 and Multicore tend to use traditional (possibly visual) (scripting) languages for equivalent of workflow whereas Grids use visual interface backend recorded in BPEL • Google MapReduce illustrates a popular Web 2.0 and Multicore approach to dataflow 38

  39. Implication for Grid Technology of Multicore and Web 2.0 III • Web 2.0 and Grids both use SOA Service Oriented Architectures • Seems likely that Multicore will also adopt although a more conventional object oriented approach also possible • Services should help multicore applications integrate modules from different sources • Multicore will use fine grain objects but coarse grain services • “System of Systems”: Grids, Web 2.0 and Multicore are likely to build systems hierarchically out of smaller systems • We need to support Grids of Grids, Webs of Grids, Grids of Multicores etc. i.e. systems of systems of all sorts 39

  40. The Ten areas covered by the 60 core WS-* Specifications

  41. WS-* Areas and Web 2.0

  42. WS-* Areas and Multicore

  43. CCR as an example of a Cross Paradigm Run Time • Naturally supports fine grain thread switching with message passing with around 4 microsecond latency for 4 threads switching to 4 others on an AMD PC with C#. Threads spawned – no rendezvous • Has around 50 microsecond latency for coarse grain service interactions with DSS extension which supports Web 2.0 style messaging • MPI Collectives – Shift and Exchange vary from 10 to 20 microsecond latency in rendezvous mode • Not as good as best MPI’s but managed code and supports Grids Web 2.0 and Parallel Computing …… • See

  44. Microsoft CCR Supports exchange of messages between threads using named ports FromHandler: Spawn threads without reading ports Receive: Each handler reads one item from a single port MultipleItemReceive: Each handler reads a prescribed number of items of a given type from a given port. Note items in a port can be general structures but all must have same type. MultiplePortReceive: Each handler reads a one item of a given type from multiple ports. JoinedReceive: Each handler reads one item from each of two ports. The items can be of different type. Choice: Execute a choice of two or more port-handler pairings Interleave: Consists of a set of arbiters (port -- handler pairs) of 3 types that are Concurrent, Exclusive or Teardown (called at end for clean up). Concurrent arbiters are run concurrently but exclusive handlers are PC07Intro 44

  45. Rendezvous exchange as two shifts Rendezvous exchange customized for MPI Rendezvous Shift Time Microseconds Stages (millions) Overhead (latency) of AMD 4-core PC with 4 execution threads on MPI style Rendezvous Messaging for Shift and Exchange implemented either as two shifts or as custom CCR pattern. Compute time is 10 seconds divided by number of stages

  46. Rendezvous exchange as two shifts Rendezvous exchange customized for MPI Rendezvous Shift Time Microseconds Stages (millions) Overhead (latency) of INTEL 8-core PC with 8 execution threads on MPI style Rendezvous Messaging for Shift and Exchange implemented either as two shifts or as custom CCR pattern. Compute time is 15 seconds divided by number of stages

  47. Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release) CGL Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better DSS Service Measurements PC07Intro 47