Web 2.0, Grids and Parallel Computing Oxford University December 07 2007 Geoffrey Fox Community Grids Laboratory, School of informatics Indiana University http://www.infomall.org/multicore [email protected], http://www.infomall.org.
Download ReportTranscript Web 2.0, Grids and Parallel Computing Oxford University December 07 2007 Geoffrey Fox Community Grids Laboratory, School of informatics Indiana University http://www.infomall.org/multicore [email protected], http://www.infomall.org.
Web 2.0, Grids and Parallel Computing
Oxford University December 07 2007 Geoffrey Fox Community Grids Laboratory
,
School of informatics Indiana University http://www.infomall.org/multicore [email protected]
, http://www.infomall.org
1
Abstract of Web 2.0,
Grids and Parallel Computing
We discuss the application of Web 2.0 to support scientific research (e-Science) and related e-moreorlessanything applications. Web 2.0 offers interesting technical approaches to build the core e infrastructure (Cyberinfrastructure) as well as a host of interesting services exemplified by Facebook, YouTube, Amazon S3/EC2 and Google maps.
We discuss why some of the original Grid goals of linking the world's computer systems may not be so relevant today and that interoperability is needed at the data and not always at the infrastructure level. Web 2.0 may also support Parallel Programming 2.0
-- a better parallel computing software environment motivated by the need to run commodity applications on multicore chips.
2
Applications, Infrastructure, Technologies
This field is confused by inconsistent use of terminology; I define Web Services , Grids and (aspects of) Web 2.0
( Enterprise 2.0
) are technologies Grids could be everything ( Broad Grids implementing some sort of managed web ) or reserved for specific architectures like OGSA or Web Services ( Narrow Grids ) These technologies combine and compete to build electronic infrastructures termed e-infrastructure or Cyberinfrastructure e-moreorlessanything is an emerging application area of broad importance that is hosted on the infrastructures e-infrastructure or Cyberinfrastructure e-Science or perhaps better e-Research is a special case of e moreorlessanything
e-moreorlessanything
‘ e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.’ from its inventor John Taylor Director General of Research Councils UK, Office of Science and Technology e-Science is about developing tools and technologies that allow scientists to do ‘faster, better or different’ research Similarly e-Business captures an emerging view of corporations as dynamic virtual organizations linking employees, customers and stakeholders across the world. This generalizes to e-moreorlessanything including presumably OxfordResearch and e-OxfordEducation ….
e A deluge of data of unprecedented and inevitable size must be managed and understood.
People (see Web 2.0
), computers , data instruments ) must be linked. (including sensors and On demand assignment of experts, computers, networks and storage resources must be supported
4
What is Cyberinfrastructure
Cyberinfrastructure is (from NSF) infrastructure that supports distributed science ( e-Science )– data, people, computers
•
Clearly core concept more general than Science Exploits Internet technology ( Web2.0
) adding (via Grid technology) management, security, supercomputers etc.
It has two aspects: parallel – low latency (microseconds) between nodes and distributed – highish latency (milliseconds) between nodes Parallel needed to get high performance on individual large simulations, data analysis etc.; must decompose problem Distributed aspect integrates especially natural for data already distinct components – Cyberinfrastructure is in general a distributed collection of parallel systems Cyberinfrastructure is made of services (originally Web services ) that are “just” programs or data sources packaged for distributed access
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Service or Web Service Approach
One uses GML, CML etc. to define the uses services data structure to capture “methods” or “ programs ” in a system and one In eScience, important services fall in four classes
• •
Simulations Data access, storage , federation, discovery
• •
Filters for data mining and manipulation General capabilities like collaboration, security etc.
Services could use something like WSDL (Web Service Definition Language) to define interoperable interfaces but Web 2.0 follows old library practice: one just specifies interface Service Interface (WSDL) establishes a “ contract ” independent of implementation between two services or a service and a client Services grain should be loosely coupled which normally means they are coarse Services will be composed (linked together) by mashups (typically scripts) or workflow (often XML – BPEL) Software Engineering and Interoperability/Standards are closely related
Relevance of Web 2.0
They say that Web 1.0
was a read-only Web while Web 2.0
is the wildly read-write collaborative Web Web 2.0
can help e-Science in many ways Its tools can enhance scientific collaboration, i.e. effectively support virtual organizations , in different ways from grids The popularity of Web 2.0 can provide high quality technologies and software that (due to large commercial investment) can be very useful in e-Science and preferable to Grid or Web Service solutions The usability and participatory nature of Web 2.0 can bring science and its informatics to a broader audience Web 2.0 can even help the emerging challenge of using multicore chips i.e. in improving parallel computing programming and runtime environments
“Best Web 2.0 Sites” -- 2006
Extracted from http://web2.wsj2.com/ All important capabilities for e-Science Social Networking
Start Pages
Social Bookmarking
Peer Production News
Social Media Sharing
Online Storage (Computing)
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Web 2.0, Grids and Web Services I
Web Services defined mechanism (WSDL) to define service interfaces
•
have clearly defined protocols (SOAP) and a well 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 but still small 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 500 Interfaces defined at http://www.programmableweb.com/apis Web 2.0
Maps also has many well known capabilities with and Amazon Compute/Storage services Google 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 Systems like Grids have Portals, Services, Resources
Captures the incredible development of interactive Web sites enabling people to create and collaborate
Web 2.0, Grids 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 Web 2.0 standards architectures/interfaces” like Google Maps and powerful suppliers like Google/Microsoft which “define the One can easily combine SOAP (Web Service) based services/systems with HTTP messages but dominance of “lowest common denominator” suggests additional structure/complexity of SOAP will not easily survive
Distribution of APIs and Mashups per Protocol
Number of APIs Number of Mashups google maps 411sync del.icio.us
yahoo! search yahoo! geocoding technorati yahoo! images trynt yahoo! local google search amazon S3 SOAP is quite a small fraction youtube flickr amazon ECS ebay virtual earth netvibes live.com
REST SOAP XML-RPC REST, XML-RPC REST, XML-RPC, SOAP REST, SOAP JS Other
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 on commodity systems – 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 Intel RMS support analysis : Gaming and Generalized decision (data mining) are ways of using these cycles
Intel’s Projection
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
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Data fetched from Internet defining users interests Maybe data-mining of this “too much computing” “too much data” will use up the both for science and commodity PC’s
•
PC will use this data(-mining) to be intelligent user assistant ?
•
Must have highly parallel algorithms
Where did Narrow Grids and Web Services go wrong?
Interoperability Interfaces will be for data not for infrastructure
•
Google, Amazon, TeraGrid, European Grids will not interoperate at the resource or compute (processing) level but rather at the data streams flowing in and out of independent Grid clouds
•
Data focus is consistent with clear if latter has learnt the Semantic Grid/Web but not usability message of Web 2.0
Lack of detailed standards in Web 2.0 preferable to industry who can get proprietary advantage inside their clouds One needs to share computing, data, people in e moreorlessanything, Grids initially focused on computing data and people are more important but eScience is healthy as is e-moreorlessanything Most Grids are solving wrong problem at wrong point in stack with a complexity that makes friendly usability difficult
Information System Architecture
The Party Line approach to Information Infrastructure is clear – one creates a Cyberinfrastructure consisting of distributed services accessed by portals/gadgets/gateways/RSS feeds Services
•
include: “Original data ”
•
Transformations or filters implementing DIKW (Data Information Knowledge Wisdom) lattice
•
Some filters could correspond to large simulations
• •
Final “ Decision Support ” step converting wisdom into action Generic services such as security, profiles etc.
Infrastructure will be set up as a System of Systems Grids) (Grids of Services and/or Grids just accept some form of DIKW produce another form of DIKW
•
“Original data” has no explicit input; just output and e-moreorlessanything Interoperability at DIKW interface not details of computing and repository resources at
Raw Data Another Grid
S S Data S S
S S Information
Another Grid S S FS SS SS Another Service SS FS FS FS FS Filter Service FS FS SS FS SS FS SS Another Grid SS S S FS S S FS S S FS FS Compute Cloud S S Database S S FS FS S S FS S S F S FS FS Knowledge
Wisdom Decisions S S FS FS Storage Cloud S S F S FS S S Filter Service Data in Data out Sensor or Data Interchange Service
Some Web 2.0 Activities at IU
Use of Blogs , RSS feeds, Wikis etc.
Use of Mashups for Cheminformatics Grid workflows Moving from Portlets supporting both) to Gadgets in portals (or at least Use of Connotea such as to produce tagged document collections http://www.connotea.org/user/crmc computing for parallel Semantic Research Grid search systems and copes with overlapping inconsistent annotations integrates multiple tagging and MSI-CIEC portal URL and URI’s e.g. NSF TeraGrid use, PI’s and Proposals
•
augments Connotea to tag a mix of Hopes to support collaboration (for Minority Serving Institution faculty) Multicore SALSA project using for Parallel Programming 2.0
Display blog RSS feed in MediaWiki.
Use blog to create posts.
Semantic Research Grid (SRG)
Integrates tagging multiple sites and search and consistently system that allows users to integrate them with use traditional citation databases We built a mashup linking to del.icio.us
, CiteULike , Connotea allowing exchange of tags between sites and between local repositories Repositories also link to local sources ( PubsOnline ) and Google Scholar ( GS ) and Windows Academic Live ( WLA )
• •
GS has number of cited publications. WLA has Digital Object Identifier (DOI) We implement a rather more powerful access control mechanism We build We have an “ event ” based architecture (consistency model) allowing change actions to be preserved and selectively changed
•
heuristic tools to mine “web lists” for citations Supports integrating different inconsistent views its updates on different tagging systems of a given document and
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Export: RSS, Bibtex Endnote etc.
Bibliographic Database
Semantic Scholars Grid
Traditional Grid Cyberinfrastructure Windows Live Academic Search Google Scholar MyResearch Database Generic Document Tools Community Tools Integration/ Enhancement User Interface
M A S H U P Web 2.0
MySpace Del.icio.us
CiteULike Citeseer Science.gov
PubChem PubMed Manuscript Central etc.
Connotea Bibsonomy Biolicious CMT Conference Management Existing User Interface
New Document-enhanced Research Tools Web service Wrappers Existing Document based Tools
Example
Parallel Computing Collection selected on Cell Tag So far no clear “winner” in tagging space Maybe CiteUlike with different metadata better How do I preserve investment?
Download to Local System
MSI-CIEC Portal
MSI-CIEC Minority Serving Institution CyberInfrastructure Empowerment Coalition
NSF Grants Tag System
NSF has the ability to get information (in XML) on all of the grants a particular person worked on We downloaded, parsed, and bookmarked this info using a little scavenger robot.
•
Each grant is represented by a bookmark and tagged with relevant information in MSI-CIEC Portal
•
Grant tags point to URLs of the NSF award page.
The investigators are imported as users Each has a bookmark for each project they worked on
•
They are also represented in the tags of these projects.
Can now form research collaborations by linking researchers with common tags
Hopefully will enable broader collaborations just those between “usual suspects” and not
Superior (from broad usage) technologies of Web 2.0
Mash-ups can replace Workflow Gadgets can replace Portlets UDDI replaced by user generated registries
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 distributed of services scripting in PHP, Python, sh etc. as both implement programming at level Mashups robustness use all types of service interfaces and perhaps do not have the potential (security) of Grid service approach Mashups typically “pure” HTTP ( REST )
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Grid Workflow Datamining in Earth Science
Work with Scripps Institute NASA GPS
Grid services California controlled by scripting workflow process real time data from ~70 GPS Sensors in Southern Earthquake Streaming Data Support Transformations Data Checking Hidden Markov Datamining (JPL) Display (GIS) Archival Real Time
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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
Web 2.0 Mashups and APIs
http://www.programmable
web.com/apis has (Sept 12 2007) 2312 Mashups and 511 Web 2.0 APIs and with GoogleMaps the most often used in Mashups
This is the Web 2.0 UDDI (service registry)
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 ( 49 API’s used in 10 or more mashups )
RSS feed of new APIs
Google maps dominates but Amazon S3 growing in popularity
Grid-style portal as used in Earthquake Grid
QuakeSim has a typical Grid technology portal The Portal is built from portlets – providing user interface fragments for each service that are composed into the full interface – uses OGCE technology as does planetary science VLAB portal with side gadgets from Web 2.0
Portlets aggregated on server using Java analogous to JSP, JSF Gadgets aggregated on client using Javascript analogous to “classic” DHTML Mashups can still be totally server side like workflow Note Web 2.0 more than a user interface Now to Portals
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Note the many competitions powering Web 2.0 Mashup and Gadget Development
Portlets v. Google Gadgets
Portals for Grid Systems are built using portlets with software like GridSphere integrating these on the server-side into a single web-page
Google (at least) offers the Google sidebar and Google home page which support Web 2.0 services and do not use a server side aggregator
Google is more user friendly!
The many Web 2.0 competitions is an interesting model for promoting development in the world-wide distributed collection of Web 2.0 developers
I guess Web 2.0 model will win!
34
Typical Google Gadget Structure
Google Gadgets are an example of Start Page (Web 2.0 term for portals) technology See http://blogs.zdnet.com/Hinchcliffe/?p=8
… Lots of HTML and JavaScript Portlets build User Interfaces by combining fragments in a standalone Java Server Google Gadgets build User Interfaces by combining fragments with JavaScript on the client
The Ten areas covered by the 60 core WS-* Specifications
WS-* Specification Area Typical Grid/Web Service Examples
1: Core Service Model
XML, WSDL, SOAP
2: Service Internet 3: Notification 4: Workflow and Transactions 5: Security 6: Service Discovery 7: System Metadata and State 8: Management
WS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM WS-Notification, WS-Eventing (Publish Subscribe) BPEL, WS-Choreography, WS-Coordination WS-Security, WS-Trust, WS-Federation, SAML, WS-SecureConversation UDDI, WS-Discovery WSRF, WS-MetadataExchange, WS-Context WSDM, WS-Management, WS-Transfer
9: Policy and Agreements 10: Portals and User Interfaces
WS-Policy, WS-Agreement WSRP (Remote Portlets)
WS-* Areas and Web 2.0
WS-* Specification Area Web 2.0 Approach
1: Core Service Model 2: Service Internet 3: Notification 4: Workflow and Transactions (no Transactions in Web 2.0) 5: Security 6: Service Discovery 7: System Metadata and State 8: Management==Interaction 9: Policy and Agreements 10: Portals and User Interfaces
XML becomes optional but still useful SOAP becomes JSON RSS ATOM WSDL becomes REST with API as GET PUT etc.
Axis becomes XmlHttpRequest No special QoS. Use JMS or equivalent?
Hard with HTTP without polling– JMS perhaps? Mashups, Google MapReduce Scripting with PHP JavaScript ….
SSL, HTTP Authentication/Authorization, OpenID is Web 2.0 Single Sign on http://www.programmableweb.com
Processed by application – no system state – Microformats are a universal metadata approach WS-Transfer style Protocols GET PUT etc.
Service dependent. Processed by application Start Pages, AJAX and Widgets(Netvibes) Gadgets
Web 2.0 can also help address long standing difficulties with parallel programming environments
Too much computing addresses too much data implies need for multicore datamining and algorithms Clustering Principal Component Analysis ( SVD ) Expectation-Maximization EM ( mixture models ) Hidden Markov Models HMM
Multicore SALSA at CGL
S ervice A ggregated L inked S equential A ctivities 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 Developing set of services (library) of mining algorithms multicore parallel data 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
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 CCR http://msdn.microsoft.com/robotics/ as it supports both MPI and dynamic threading style of parallelism
There is MPI style messaging and ..
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
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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 Pipeline Pilot (from SciTegic) and the LEAD environment built at Indiana University. like Taverna, Kepler, InforSense KDE, 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 supported.
HPCS program support task parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be
“Service Aggregation” in SALSA
Kernels and Composition must be supported both inside chips (the multicore problem) and Grids. between machines in clusters (the traditional parallel computing problem) or 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 natural service (composition) model.
represents the
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
CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure Cheminformatics Services Statistics Services Database Services
Core functionality
Fingerprints Similarity Descriptors 2D diagrams File format conversion
Computation functionality
Regression Classification Clustering Sampling distributions
3D structures by
CID SMARTS 3D Similarity
Applications
Docking Filtering Druglikeness
Applications
Predictive models Feature selection Toxicity predictions Mutagenecity predictions 2D plots Arbitrary R code (PkCell) Anti-cancer activity predictions Pharmacokinetic parameters OSCAR Document Analysis
Need to make all this parallel
InChI Generation/Search Computational Chemistry (Gamess, Jaguar etc.)
Docking scores/poses by
CID SMARTS Protein Docking scores
PubChem related data by
CID, SMARTS
Varuna.net
Quantum Chemistry
Core Grid Services
Service Registry Job Submission and Management
Local Clusters IU Big Red, TeraGrid, Open Science Grid
Portal Services
RSS Feeds User Profiles Collaboration as in Sakai
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 properties (binary properties) with real-valued GIS Census aggregated in 200,000 Census Blocks covering Indiana
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uses publicly available Census data; in particular the 2000 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 do this (is there a large Windows quad core cluster on TeraGrid?)
•
This would also be efficient on large problems 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 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 efficiency but dimensions visualization harder and have parallel as in 150-1024 (or more) Will develop a family of such parallel annealing data-mining tools
• • •
where basic approach known for Clustering Gaussian Mixtures and possibly (Expectation Maximization) 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/ 49
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!)
MPI Exchange Latency in µs (20-30 µs computation between messaging)
Machine Intel8c:gf12
(8 core 2.33 Ghz) (in 2 chips)
Intel8c:gf20 OS
Redhat Fedora
Runtime
MPJE (Java) MPICH2 (C) MPICH2: Fast MPJE
Grains
Process Process Process Process Process
Parallelism
8 8 8 8 8
MPI Exchange Latency
181 (8 core 2.33 Ghz) mpiJava MPICH2 Process Process 8 The macroscopic inter-service DSS Overhead is about 35µs 8 8 40.0
39.3
4.21
157 111 64.2
170
Intel8b
Vista MPJE Process DSS is composed from CCR threads that have Fedora MPJE Process 4µs mpiJava Process 20µs CCR (C#) Thread
AMD4
(4 core 2.19 Ghz) XP Redhat MPJE MPJE Process Process
Intel4 (4 core 2.8 Ghz)
XP XP mpiJava MPICH2 CCR CCR Process Process Thread Thread 8 8 8 4 4 4 4 4 4 142 100 20.2
185 152 99.4
39.3
16.3
25.8
0.25
0.2
0.15
0.1
0.05
0 0 0.45
Parallel Multicore Deterministic Annealing Clustering
Parallel Overhead on 8 Threads Intel 8b 10 Clusters
0.4
Overhead = Constant1 + Constant2/n Speedup = 8/(1+Overhead)
0.35
Constant1 = 0.05 to 0.1 (Client Windows) due to thread runtime fluctuations
0.3
0.5
1 1.5
20 Clusters 10000/(Grain Size n = points per core)
2 2.5
3 3.5
4
Total Clustering is typical of data mining methods that are needed for tomorrow’s clients or servers bathed in a data rich environment Asian Hispanic Renters Clustering Census data in Indiana on dual quadcore processors Implemented with CCR and DSS Hispanic Use deterministic annealing that uses multiscale method to avoid local minima Efficiency is 90% Purdue limited by peculiar Windows thread scheduling effects IUB 30 Clusters 10 Clusters
Parallel Multicore Deterministic Annealing Clustering
0.250
Parallel Overhead for large (2M points) Indiana Census clustering on 8 Threads Intel 8b This fluctuating overhead due to 5-10% runtime fluctuations between threads
0.200
“Constant1”
0.150
0.100
0.050
0.000
0 5
Increasing number of clusters decreases communication/memory bandwidth overheads
10 15 #cluster 20 25 30 35
Parallel Multicore Deterministic Annealing Clustering
0.200
0.180
0.160
0.140
0.120
0.100
0.080
0.060
0.040
0.020
0.000
0 2
Parallel Overhead for subset of PubChem clustering on 8 Threads (Intel 8b) “Constant1” as bits not doubles ) 40,000 points with 1052 binary properties (Census is 2 real valued properties) Increasing number of clusters decreases communication/memory bandwidth overheads
4 6 8 #cluster 10 12 14 16 18
Intel 8-core C# with 80 Clusters: Vista Run Time Fluctuations for Clustering Kernel
• 2 Quadcore Processors 0.1
between messaging synchronization points
Standard Deviation/Run Time
0.05
10,000 Datpts 50,000 Datapts 500,000 Datapts 0 0 1 2 3 4 thread 5
Number of Threads
6 7 8
Intel 8 core with 80 Clusters: Redhat Run Time Fluctuations for Clustering Kernel
• This is average of standard deviation of run time of the
80 Cluster(ratio of std to time vs #thread)
8 threads between messaging synchronization points 0.006
Standard Deviation/Run Time
0.004
0.002
10,000 Datapts 50,000 Datapts 500,000 Datapts 0 1 2 3 4 5
Number of Threads
6 7 8
Looking to the Future
Web 2.0 has momentum as it is driven by success of social web sites and the user friendly protocols attracting many developers of mashups Grids momentum driven by the success of eScience commercial web service and the thrusts largely aimed at Enterprise We expect applications such as business and military where predictability and robustness Service ( Narrow Grid ) core important might be built on a Web with perhaps Web 2.0 functionality enhancements
•
But even this Web Service application may not survive Multicore usability driving Parallel Programming 2.0
Simplicity , supporting many developers Grids!
are forces pressuring Robustness and coping with flowers are forces unstructured blooming of a 1000 pressuring Web 2.0
Need work on Grid Cloud Data Interchange standards multicore programming and