Linking Programming models between Grids, Web 2.0 and Multicore

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 [email_address] http:// www.infomall.org

Points in Talk
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

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
Service s composed/ mashup ed by “ millions ”
Many composition 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

Some Details
See http://www.slideshare.net/Foxsden or more conventionally
Web 2.0 and Grid Tutorial
http://grids.ucs.indiana.edu/ptliupages/presentations/CTSpartIMay21-07.ppt
http://grids.ucs.indiana.edu/ptliupages/presentations/Web20Tutorial_CTS.ppt
Multicore and Parallel Computing Tutorial
http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/index.html
“Web 2.0” citation site http://www.connotea.org/user/crmc

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-* 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 (so called e-Science)
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 http:// www.programmableweb.com/apis
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, portals, MySpace, YouTube,

Web 2.0 and Web Services II
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”
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
We can use the term Grids strictly as Narrow Grids 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

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 …………….

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

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 ?

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

What is a tumor? Is there a tumor here? What if the tumor progresses? It is all about dealing efficiently with complex multimodal datasets R ecognition M ining S ynthesis Images courtesy: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html

Intel’s Application Stack PC07Intro [email_address]

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

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
http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/PC07BYOPA.ppt
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

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 [email_address]

Data Parallel Time Dependence
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
Synchronization on MIMD machines is accomplished by messaging It is automatic on SIMD machines! Application Time Application Space Synchronous Identical evolution algorithms t 0 t 1 t 2 t 3 t 4

Local Messaging for Synchronization
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
……… 8 Processors Application and Processor Time Application Space Communication Phase Compute Phase Communication Phase Compute Phase Communication Phase Compute Phase Communication Phase

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 = (t 1 -t 0 )/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
Distinct evolution algorithms for each data point in each processor Application Time Application Space t 0 t 1 t 2 t 3 t 4

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

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 Space Application Space Application Time

Computer Chess
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
Increasing search depth

Discrete Event Simulations
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 simulations can be done loosely synchronously but inefficient as many inactive elements
Battle of Hastings

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

Google MapReduce Simplified Data Processing on Large Clusters
http:// labs.google.com/papers/mapreduce.html
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

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

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

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

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 ”

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

Why people like MPI!
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 UPC on several machines
MPI always good but other approaches erratic
Other studies reach similar conclusions?
cluster After Optimization of UPC cluster

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

Mashups v Workflow?
Mashup Tools are reviewed at http:// blogs.zdnet.com/Hinchcliffe/?p =63
Workflow Tools are reviewed by Gannon and Fox http://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 do not have the potential robustness (security) of Grid service approach
Typically “pure” HTTP ( REST )

Web 2.0 APIs
http://www.programmableweb.com/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

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

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

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
Growing number of commercial Mashup Tools

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

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

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

The Ten areas covered by the 60 core WS-* Specifications WSRP (Remote Portlets) 10: Portals and User Interfaces WS-Policy, WS-Agreement 9: Policy and Agreements WSDM, WS-Management, WS-Transfer 8: Management WSRF, WS-MetadataExchange, WS-Context 7: System Metadata and State UDDI, WS-Discovery 6: Service Discovery WS-Security, WS-Trust, WS-Federation, SAML, WS-SecureConversation 5: Security BPEL, WS-Choreography, WS-Coordination 4: Workflow and Transactions WS-Notification, WS-Eventing (Publish-Subscribe) 3: Notification WS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM 2: Service Internet XML, WSDL, SOAP 1: Core Service Model Typical Grid/Web Service Examples WS-* Specification Area

WS-* Areas and Web 2.0 Start Pages, AJAX and Widgets(Netvibes) Gadgets 10: Portals and User Interfaces Service dependent. Processed by application 9: Policy and Agreements WS-Transfer style Protocols GET PUT etc. 8: Management==Interaction Processed by application – no system state – Microformats are a universal metadata approach 7: System Metadata and State http://www.programmableweb.com 6: Service Discovery SSL, HTTP Authentication/Authorization, OpenID is Web 2.0 Single Sign on 5: Security Mashups, Google MapReduce Scripting with PHP JavaScript …. 4: Workflow and Transactions (no Transactions in Web 2.0) Hard with HTTP without polling – JMS perhaps? 3: Notification No special QoS. Use JMS or equivalent? 2: Service Internet XML becomes optional but still useful SOAP becomes JSON RSS ATOM WSDL becomes REST with API as GET PUT etc. Axis becomes XmlHttpRequest 1: Core Service Model Web 2.0 Approach WS-* Specification Area

WS-* Areas and Multicore Web 2.0 technology popular 10: Portals and User Interfaces Handled by application 9: Policy and Agreements Interaction between objects key issue in parallel programming trading off efficiency versus performance 8: Management == Interaction Environment Variables 7: System Metadata and State Use libraries 6: Service Discovery Not so important intrachip 5: Security Many approaches; scripting languages popular 4: Workflow and Transactions Publish-Subscribe for events and Interrupts 3: Notification Not so important intrachip 2: Service Internet Fine grain Java C# C++ Objects and coarse grain services as in DSS . Information passed explicitly or by handles. MPI needs to be updated to handle non scientific applications as in CCR 1: Core Service Model Typical Grid/Web Service Examples WS-* Specification Area

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 http://grids.ucs.indiana.edu/ptliupages/publications/CCRApril16open.pdf

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/

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 Stages (millions) Time Microseconds Rendezvous exchange as two shifts Rendezvous exchange customized for MPI Rendezvous Shift

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 Stages (millions) Time Microseconds Rendezvous exchange as two shifts Rendezvous exchange customized for MPI Rendezvous Shift

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
PC07Intro [email_address] DSS Service Measurements

Linking Programming models between Grids, Web 2.0 and Multicore

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