Multicore Salsa Parallel Programming 2.0

Multicore SalsaParallel Programming 2.0 Peking University October 31 2007 Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae Community Grids Laboratory, Indiana University Bloomington IN 47404 Xiaohong Qiu Research Computing UITS, Indiana University Bloomington IN George Chrysanthakopoulos, Henrik Frystyk Nielsen Microsoft Research, Redmond WA gcf@indiana.edu, http://www.infomall.org

Abstract of Multicore SalsaParallel Programming 2.0 Multicore or manycore systems are probably not architecturally that different from parallel machines with which we are familiar. However in next 5-8 years the basic commodity (PC) chips will have 64-256 cores and currently there is little understanding of how to use them. It is clearly essential (at least for major US technology companies) that we effectively use such cores on broadly deployed machines. This constraint makes multicore chips an exciting and different problem.We describe general issues in context of the SALSA project at http://www.infomall.org/multicore. This is using Service Aggregated Linked Sequential Activities where we are looking at a suite of parallel datamining applications as one important broadly useful capability for future multicore-based systems that will offer users navigation and advice based on the ever increasing data from sensors and the Internet. A key idea is using services not libraries as the basic building block so that we can offer productive user interfaces (Parallel Programming 2.0) by adapting workflow and mashups for composing parallel services. We still imagine that services will be constructed by experts using extensions of current threading and MPI models. Automatic compilers do not seem practical in the key 5-8 year time frame although PGAS((Partitioned Global Address Space) could be valuable. We present results on 8 cores (two quadcore chips) using the Microsoft CCR/DSS runtime that combines MPI, threading and service capabilities. 2

Too much Computing? Historically both grids and parallel computing have tried to increase computing capabilities by Optimizing performance of codes at cost of re-usability Exploiting all possible CPU’s such as Graphics co-processors and “idle cycles” (across administrative domains) Linking central computers together such as NSF/DoE/DoD supercomputer networks without clear user requirements Next Crisis in technology area will be the opposite problem – commodity chips will be 32-128way parallel in 5 years time and we currently have no idea how to use them – especially on clients Only 2 releases of standard software (e.g. Office) in this time span so need solutions that can be implemented in next 3-5 years Note that even cell phones will be multicore There is “Too much data” as well as “Too much computing” and maybe processing the data deluge will “solve” the “Too much computing” problem Quite plausible on servers where we naturally will have lots of data Less clear on clients but short of other ideas Intel RMS analysis: Gaming and Generalized decision support (data mining) are two ways of using these cycles

Intel’s Projection

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

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: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html

Intel’s Application Stack

Too much Data to the Rescue? • Multicore servers have clear “universal parallelism” as many users can access and use machines simultaneously • Maybe also need application parallelism (e.g. datamining) as needed on client machines • Over next years, we will be submerged of course in data deluge • Scientific observations for e-Science • Local (video, environmental) sensors • Data fetched from Internet defining users interests • Maybe data-mining of this “too much data” will use up the “too much computing” both for science and commodity PC’s • PC will use this data(-mining) to be intelligent user assistant? • Must have highly parallel algorithms

Broad Parallelism Issues and Data-mining Algorithms • Looking at Intel list of algorithms (and all previous experience), we find there are two styles of “micro” parallelism • Dynamic search as in integer programming, Hidden Markov Methods (and computer chess); irregular synchronization with dynamic threads • “MPI Style” i.e. several threads running typically in SPMD (Single Program Multiple Data); collective synchronization of all threads together • Most Intel RMS are “MPI Style” and very close to scientific algorithms even if applications are not science • Note MPI historically runs with processes not threads but likely that threads will be implementation of choice for commodity applications • Most “commodity experience” is for few way concurrency to support Windows/Linux O/S in “dynamic thread” paradigm • Little experience in MPI style synchronization with threads

“Space-Time” Picture Computer Time T4 T3 t4 Application Time t3 T2 t2 t1 t0 T1 Application Space T0 4-wayParallelComputer(CPU’s) • Data-parallel applications map spatial structure of problem on parallel structure of both CPU’s and memory • However “left over” parallelism has to map into time on computer • Data-parallel languages support this “Internal” (to data chunk) application spatial dependence (n degrees of freedom) maps into time on the computer

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

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

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

Dynamic (search/Thread) Applications Application Time Application Time Application Space Application Space • Here there is no natural universal ‘time’ in the application 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

Some links • See http://www.connotea.org/user/crmc for references -- select tag oldies for venerable links; tags like MPI Applications Compiler have obvious significance • http://www.infomall.org/salsa for recent work including publications • My tutorialhttp://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/index.htmlIf you have forgotten about parallel computing (or never learnt)

Multicore SALSA at CGL Service Aggregated Linked Sequential Activities Aims to link parallel and distributed (Grid) computing by developing parallel applications as services and not as programs or libraries Improve traditionally poor parallel programming development environments Can use messaging to link parallel and Grid services but performance – functionality tradeoffs different Parallelism needs few µs latency for message latency and thread spawning Network overheads in Grid 10-100’s µs Use low latency where performance needed; use high latency where productivity needed Developing set of services (library) of multicore parallel data mining algorithms

Parallel Programming Model If multicore technology is to succeed, mere mortals must be able to build effective parallel programs There are interesting new developments – especially the new Darpa HPCS Languages X10, Chapel and Fortress However if mortals are to program the 64-256 core chips expected in 5-7 years, then we must use today’s technology and we must make it easy This rules out radical new approaches such as new languages Remember that the important applications are not scientific computing but most of the algorithms needed are similar to those explored in scientific parallel computing We can divide problem into two parts: Micro-parallelism: High Performance scalable (in number of cores) parallel kernels or libraries Macro-parallelism:Composition of kernels into complete applications We currently assume that the kernels of the scalable parallel algorithms/applications/libraries will be built by experts with a Broader group of programmers (mere mortals) composing library members into complete applications.

Scalable Parallel Components There are no agreed high-level programming environments for building library members that are broadly applicable. However lower level approaches where experts define parallelism explicitly are available and have clear performance models. These include MPI for messaging or just locks within a single shared memory. There are several patterns to support here including the collective synchronization of MPI, dynamic irregular thread parallelism needed in search algorithms, and more specialized cases like discrete event simulation. We use Microsoft CCRhttp://msdn.microsoft.com/robotics/ as it supports both MPI and dynamic threading style of parallelism

Good and Bad about MPI MPI (or equivalent locks on shared memory machine) has a bad reputation as the “machine-code” approach to parallel computing User must break problem into parts User must program each part User must generate synchronization/messaging between parts However these defects imply a very clear performance model as user needs to make explicit both application and machine structure Thus if you can do this, one expects reliable understandable results that port well between different architectures

Other Parallel Programming Models OpenMP annotation or Automatic Parallelism of existing software is practical way to use those pesky cores with existing code As parallelism is typically not expressed precisely, one needs luck to get good performance Remember writing in Fortran, C, C#, Java … throws away information about parallelism HPCS Languages should be able to properly express parallelism but we do not know how efficient and reliable compilers will be High Performance Fortran failed as language expressed a subset of parallelism and compilers did not give predictable performance PGAS (Partitioned Global Address Space) like UPC, Co-array Fortran, Titanium, HPJava One decomposes application into parts and writes the code for each component but use some form of global index Compiler generates synchronization and messaging PGAS approach should work but has never been widely used – presumably because compilers not mature

Summary of micro-parallelism On new applications, use MPI/locks with explicit user decomposition A subset of applications can use “data parallel” compilers which follow in HPF footsteps Graphics Chips and Cell processor motivate such special compilers but not clear how many applications can be done this way OpenMP and/or Compiler-based Automatic Parallelism for existing codes in conventional languages

Composition of Parallel Components The composition (macro-parallelism) step has many excellent solutions as this does not have the same drastic synchronization and correctness constraints as one has for scalable kernels Unlike micro-parallelism step which has no very good solutions Task parallelism in languages such as C++, C#, Java and Fortran90; General scripting languages like PHP Perl Python Domain specific environments like Matlab and Mathematica Functional Languages like MapReduce, F# HeNCE, AVS and Khoros from the past and CCA from DoE Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE, Pipeline Pilot (from SciTegic) and the LEAD environment built at Indiana University. Web solutions like Mash-ups and DSS Many scientific applications use MPI for the coarse grain composition as well as fine grain parallelism but this doesn’t seem elegant The new languages from Darpa’s HPCS program support task parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be supported.

Mashups v Workflow? Mashup Tools are reviewed at http://blogs.zdnet.com/Hinchcliffe/?p=63 Workflow Tools are reviewed by Gannon and Foxhttp://grids.ucs.indiana.edu/ptliupages/publications/Workflow-overview.pdf 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 perhaps do not have the potential robustness (security) of Grid service approach Mashups typically “pure” HTTP (REST) 23

Grid Workflow Data Assimilation in Earth Science Grid services triggered by abnormal events and controlled by workflow process real time data from radar and high resolution simulations for tornado forecasts Typical graphical interface to service composition Taverna another well known Grid/Web Service workflow tool Recent Web 2.0 visual Mashup tools include Yahoo Pipes and Microsoft Popfly

“Service Aggregation” in SALSA Kernels and Composition must be supported both inside chips (the multicore problem) and between machines in clusters (the traditional parallel computing problem) or Grids. The scalable parallelism (kernel) problem is typically only interesting on true parallel computers as the algorithms require low communication latency. However composition is similar in both parallel and distributed scenarios and it seems useful to allow the use of Grid and Web composition tools for the parallel problem. This should allow parallel computing to exploit large investment in service programming environments Thus in SALSA we express parallel kernels not as traditional libraries but as (some variant of) services so they can be used by non expert programmers For parallelism expressed in CCR, DSS represents the natural service (composition) model.

Parallel Programming 2.0 Web 2.0 Mashups will (by definition the largest market) drive composition tools for Grid, web and parallel programming Parallel Programming 2.0 will build on Mashup tools like Yahoo Pipes and Microsoft Popfly Yahoo Pipes

Inter-Service Communication Note that we are not assuming a uniform implementation of service composition even if user sees same interface for multicore and a Grid Good service composition inside a multicore chip can require highly optimized communication mechanisms between the services that minimize memory bandwidth use. Between systems interoperability could motivate very different mechanisms to integrate services. Need both MPI/CCR level and Service/DSS levelcommunication optimization Note bandwidth and latency requirements reduce as one increases the grain size of services Suggests the smaller services inside closely coupled cores and machines will have stringent communication requirements.

Inside the SALSA Services We generalize the well known CSP (Communicating Sequential Processes) of Hoare to describe the low level approaches to fine grain parallelism as “Linked Sequential Activities” in SALSA. We use term “activities” in SALSA to allow one to build services from either threads, processes (usual MPI choice) or even just other services. We choose term “linkage” in SALSA to denote the different ways of synchronizing the parallel activities that may involve shared memory rather than some form of messaging or communication. There are several engineering and research issues for SALSA There is the critical communication optimization problem area for communication inside chips, clusters and Grids. We need to discuss what we mean by services The requirements of multi-language support Further it seems useful to re-examine MPI and define a simpler model that naturally supports threads or processes and the full set of communication patterns needed in SALSA (including dynamic threads). Should start a new standards effort in OGF perhaps?

Need to make all this parallel OSCAR Document Analysis InChI Generation/Search Computational Chemistry (Gamess, Jaguar etc.) Varuna.net Quantum Chemistry CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure Portal Services RSS Feeds User Profiles Collaboration as in Sakai Core Grid Services Service Registry Job Submission and Management Local Clusters IU Big Red, TeraGrid, Open Science Grid

Deterministic Annealing for Data Mining • We are looking at deterministic annealing algorithms because although heuristic • They have clear scalable parallelism (e.g. use parallel BLAS) • They avoid (some) local minima and regularize ill defined problems in an intuitively clear fashion • They are fast (no Monte Carlo) • I understand them and Google Scholar likes them • Developed first by Durbin as Elastic Net for TSP • Extended by Rose (my student then; now at UCSB)) and Gurewitz (visitor to C3P) at Caltech for signal processing and applied later to many optimization and supervised and unsupervised learning methods. • See K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proceedings of the IEEE, vol. 80, pp. 2210-2239, November 1998

High Level Theory • Deterministic Annealing can be looked at from a Physics, Statistics and/or Information theoretic point of view • Consider a function (e.g. a likelihood) L({y}) that we want to operate on (e.g. maximize) • Set L ({y},T) =L({y}) exp(- ({y} - {y})2 /T ) d{y} • Incorporating entropy term ensuring that one looks for most likely states at temperature T • If {y} is a distance, replacing L by L corresponds to smearing or smoothing it over resolution T • Minimize Free Energy F = -Ln L ({y},T) rather than energy E = -Ln L({y}) • Use mean field approximation to avoid Monte Carlo (simulated annealing)

Deterministic Annealing for Clustering I • Illustrating similarity between clustering and Gaussian mixtures • Deterministic annealing for mixtures replaces by and anneals down to mixture size

Deterministic Annealing for Clustering II • This is an extended K-means algorithm • Start with a single cluster giving as solution y1 as centroid • For some annealing schedule for T, iterate above algorithm testing correlation matrix in xi about each cluster center to see if “elongated” • Split cluster if elongation “long enough”; splitting is a phase transition in physics view • You do not need to assume number of clusters but rather a final resolution T or equivalent • At T=0, uninteresting solution is N clusters; one at each point xi

DeterministicAnnealing • Minimum evolving as temperature decreases • Movement at fixed temperature going to local minima if not initialized “correctly F({y}, T) Solve Linear Equations for each temperature Nonlinearity removed by approximating with solution at previous higher temperature Configuration {y}

Clustering Data • Cheminformatics was tested successfully with small datasets and compared to commercial tools • Cluster on properties of chemicals from high throughput screening results to chemical properties (structure, molecular weight etc.) • Applying to PubChem (and commercial databases) that have 6-20 million compounds • Comparing traditional fingerprint (binary properties) with real-valued properties • GIS uses publicly available Census data; in particular the 2000 Census aggregated in 200,000 Census Blocks covering Indiana • 100MB of data • Initial clustering done on simple attributes given in this data • Total population and number of Asian, Hispanic and Renters • Working with POLIS Center at Indianapolis on clustering of SAVI (Social Assets and Vulnerabilities Indicators) attributes at http://www.savi.org) for community and decision makers • Economy, Loans, Crime, Religion etc.

Where are we? • We have deterministically annealed clustering running well on 8-core (2-processor quad core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS • Could also run on multicore-based parallel machines but didn’t do this (is there a large Windows quad core cluster on TeraGrid?) • This would also be efficient on large problems • Applied to Geographical Information Systems (GIS) and census data • Could be an interesting application on future broadly deployed PC’s • Visualize nicely on Google Maps (and presumably Microsoft Virtual Earth) • Applied to several Cheminformatics problems and have parallel efficiency but visualization harder as in 150-1024 (or more) dimensions • Will develop a family of such parallel annealing data-mining tools where basic approach known for • Clustering • Gaussian Mixtures (Expectation Maximization) • and possibly Hidden Markov Methods

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 http://msdn.microsoft.com/robotics/ 37

Preliminary Results • Parallel Deterministic Annealing Clustering in C# with speed-up of 7 on Intel 2 quadcore systems • Analysis of performance of Java, C, C# in MPI and dynamic threading with XP, Vista, Windows Server, Fedora, Redhat on Intel/AMD systems • Study of cache effects coming with MPI thread-based parallelism • Study of execution time fluctuations in Windows (limiting speed-up to 7 not 8!)

Machines Used

Clustering algorithm annealing by decreasing distance scale and gradually finds more clusters as resolution improved Here we see 10 clusters increasing to 30 as algorithm progresses

Total Total Asian Asian Hispanic Hispanic Purdue Renters Renters IUB 30 Clusters 10 Clusters Renters

In detail, different groups have different cluster centers

DSS Section • We view system as a collection of services – in this case • One to supply data • One to run parallel clustering • One to visualize results – in this by spawning a Google maps browser • Note we are clustering Indiana census data • DSS is convenient as built on CCR

Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release) Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better DSS Service Measurements 44

Clustering Problem

Deterministic Annealing • See K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proceedings of the IEEE, vol. 80, pp. 2210-2239, November 1998 • Parallelization is similar to ordinary K-Means as we are calculating global sums which are decomposed into local averages and then summed over components calculated in each processor • Many similar data mining algorithms (such as annealing for E-M expectation maximization) which have high parallel efficiency and avoid local minima • For more details see • http://grids.ucs.indiana.edu/ptliupages/presentations/Grid2007PosterSept19-07.ppt and • http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/PC07BYOPA.ppt

Parallel MulticoreDeterministic Annealing Clustering Parallel Overheadon 8 Threads Intel 8b Speedup = 8/(1+Overhead) 10 Clusters Overhead = Constant1 + Constant2/n Constant1 = 0.05 to 0.1 (Client Windows) due to threadruntime fluctuations 20 Clusters 10000/(Grain Size n = points per core)

Multicore Salsa Parallel Programming 2.0