Tag: Hardware

Intel® Threading Challenge 2011 Software Contest

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One more software contests for you, but in the sub million dollar prize range

http://software.intel.com/en-us/contests/intel-threading-challenge-2011/contests.php

Intel® Threading Challenge 2011 – Win a Trip to Intel Developer Forum in San Francisco

Intel® Threading Challenge 2011 is going BIG this year! After three exciting threading competitions, our fourth Threading Challenge is stepping up the excitement with a BIG Grand Prize, a trip to the Intel Developer Forum (IDF) in San Francisco (September 13-15, 2011).

Since 2008, the Intel® Threading Challenge has attracted developers of varying experience from around the world. The active participation from the community has made the Threading Challenge not only a great programming competition, but a great way for community members to engage with each other, trade threading tips, and discover new parallel programming resources.

Last year’s format of two competition levels, Master and Apprentice, generated great excitement and opened the Threading Challenge to a new group of participants. So, we are going to continue the competition with a Master level and Apprentice level, each competing for the Grand Prize for their level, as well as individual problem awards. We know you love a great challenge and great prizes, so our Threading Challenge Team is putting together some exciting threading problems for you.

Monday, April 18, 2011 – Threading Challenge 2011 (Phase 1) Launches (both levels) at 12:00 PM (noon PDT)– The competition for 2011 is very similar to last year’s, but read on whether you’re a previous participant or new to the Threading Challenge, so you will be aware of all elements of the competition and how to compete. Then, you can start threading your way to prizes today!

Choose the right level for you!

 

Threading Challenge 2011:

• Two levels available for entry: Apprentice & Master
• Phase 1: 3 problems in each level
• Phase 2: Stay tuned for details, coming in Autumn 2011
• We will award 1st, 2nd & 3rd place prizes for each problem in each level
• No overlap of problems and each level’s problems will be offered consecutively
• Participants have the option to use the Intel® Manycore Testing Lab (MTL), consisting of 40 cores, 80 threads
• To enter the Threading Challenge 2011, please read the Official Rules and register for the competition with link in the “To Enter” Section.

The Threading Challenge will be implemented in two phases, with the 1st Phase consisting of 3 problems in each level. The details of the 2nd Phase will be announced in September 2011. For Phase 1, a new problem in each level will be launched on the days listed below at 12:00 noon (PDT) and will be open for entry for 22 days (inclusive of the problem starting day), until closing on the final problem day at 12:00 noon (PDT).

Problem Start and Closing Dates (both Master and Apprentice levels):

Problem 1:
Starts: Monday, April 18, 2011 at 12:00pm (PDT)
Ends. Monday, May 9, 2011 at 12:00pm (PDT)

Problem 2:
Starts: Monday, May 9, 2011 at 12:00pm (PDT)
Ends: Monday, May 30, 2011 at 12:00pm (PDT)

Problem 3: (Due to U.S. Memorial Day Holiday, Problem 2 will start on Tuesday, May 31, 2011)
Starts: Tuesday, May 31, 2011 at 12:00pm (PDT)
Ends: Tuesday, June 21, 2011 at 12:00pm (PDT)

*All problems start and end at 12:00 noon (Pacific Daylight Time)

Contestants will have 22 days to complete their entry submission (solution only for Apprentice OR solution and write-up for Master) for each problem. You may enter ONLY 1 problem at a time and will need to choose which level (Apprentice or Master) you wish to participate in during each problem cycle. You will be awarded points based on your solution submitted. Be sure to take advantage of our threading resources and tools, and you may validate your solution (optional) using the Intel® Manycore Testing Lab to solve your problems and get involved in the dedicated forums to earn extra points.

Each problems winners will be announced on the site after the problem is closed, and Prizes will be awarded to those problem winners (see official rules for prize distribution information). The Grand Prize, a Trip to Intel® Developer Forum (IDF) in San Francisco, will be awarded for each level to the participant that has the highest total points earned for the three problems in each level (i.e., highest total points for Master level problems and Apprentice level problems).

The Intel® Threading Challenge attracts some of the most talented developers in the world to solve parallelism code challenges. Now is your chance to take multithreading to the next level and possibly win great prizes. Demonstrate your threading expertise today!

More Details:

Intel® Threading Challenge 2011 is organized so any level of developer can have the opportunity to participate. Two levels of participation are available. The Apprentice level gives those just getting started in multithreading development a chance to try out and improve their threading skills. The Master level will be executed similarly to previous threading challenges, providing those with more experience a chance to test their skills and compete against other experienced developers.

Intel® Manycore Testing Lab – Available as Option for Threading Challenge 2011 Participants

This year competitors will have the optional opportunity to develop and validate their code using the Intel® Manycore Testing Lab. This 40-core, 80-thread development environment has the latest hardware and software available and will be used by this year’s judges to test the winning entries in Threading Challenge 2011 Phase 1.

The Intel® Manycore Testing Lab (MTL) will be made available to Threading Challenge 2011 contestants. Use of the MTL will give participants the opportunity to write and test their code on systems exactly configured to what the judges will be using to score submitted entries. No more guessing about if your code will build or how it will run. (There is no requirement to use the MTL for any part of the contest. It is strictly an optional alternative being made available to those that wish to use it.)

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WPS Version 2.5.1 Released – can still run SAS language/data and R

However this is what Phil Rack the reseller is quoting on http://www.minequest.com/Pricing.html

Windows Desktop Price: $884 on 32-bit Windows and $1,149 on 64-bit Windows.

The Bridge to R is available on the Windows platforms and is available for free to customers who
license WPS through MineQuest,LLC. Companies and organizations outside of North America
may purchase a license for the Bridge to R which starts at $199 per desktop or $599 per server

Windows Server Price: $1,903 per logical CPU for 32-bit and $2,474 for 64-bit.

Note that Linux server versions are available but do not yet support the Eclipse IDE and are
command line only

WPS sure seems going well-but their pricing is no longer fixed and on the home website, you gotta fill a form. Ditt0 for the 30 day free evaluation

http://www.teamwpc.co.uk/products/wps/modules/core

Data File Formats

The table below provides a summary of data formats presently supported by the WPS Core module.

Data File Format Un-Compressed
Data		 Compressed
Data
Read Write Read Write
SD2 (SAS version 6 data set)
SAS7BDAT (SAS version 7 data set)
SAS7BDAT (SAS version 8 data set)
SAS7BDAT (SAS version 9 data set)
SASSEQ (SAS version 8/9 sequential file)
V8SEQ (SAS version 8 sequential file)
V9SEQ (SAS version 9 sequential file)
WPD (WPS native data set)
WPDSEQ (WPS native sequential file)
XPORT (transport format)

Additional access to EXCEL, SPSS and dBASE files is supported by utilising the WPS Engine for DB Filesmodule.

and they have a new product release on Valentine Day 2011 (oh these Europeans!)

From the press release at http://www.teamwpc.co.uk/press/wps2_5_1_released

WPS Version 2.5.1 Released 

New language support, new data engines, larger datasets, improved scalability

LONDON, UK – 14 February 2011 – World Programming today released version 2.5.1 of their WPS software for workstations, servers and mainframes.

WPS is a competitively priced, high performance, highly scalable data processing and analytics software product that allows users to execute programs written in the language of SAS. WPS is supported on a wide variety of hardware and operating system platforms and can connect to and work with many types of data with ease. The WPS user interface (Workbench) is frequently praised for its ease of use and flexibility, with the option to include numerous third-party extensions.

This latest version of the software has the ability to manipulate even greater volumes of data, removing the previous 2^31 (2 billion) limit on number of observations.

Complimenting extended data processing capabilities, World Programming has worked hard to boost the performance, scalability and reliability of the WPS software to give users the confidence they need to run heavy workloads whilst delivering maximum value from available computer power.

WPS version 2.5.1 offers additional flexibility with the release of two new data engines for accessing Greenplum and SAND databases. WPS now comes with eleven data engines and can access a huge range of commonly used and industry-standard file-formats and databases.

Support in WPS for the language of SAS continues to expand with more statistical procedures, data step functions, graphing controls and many other language items and options.

WPS version 2.5.1 is available as a free upgrade to all licensed users of WPS.

Summary of Main New Features:

  • Supporting Even Larger Datasets
    WPS is now able to process very large data sets by lifting completely the previous size limit of 2^31 observations.
  • Performance and Scalability Boosted
    Performance and scalability improvements across the board combine to ensure even the most demanding large and concurrent workloads are processed efficiently and reliably.
  • More Language Support
    WPS 2.5.1 continues the expansion of it’s language support with over 70 new language items, including new Procedures, Data Step functions and many other language items and options.
  • Statistical Analysis
    The procedure support in WPS Statistics has been expanded to include PROC CLUSTER and PROC TREE.
  • Graphical Output
    The graphical output from WPS Graphing has been expanded to accommodate more configurable graphics.
  • Hash Tables
    Support is now provided for hash tables.
  • Greenplum®
    A new WPS Engine for Greenplum provides dedicated support for accessing the Greenplum database.
  • SAND®
    A new WPS Engine for SAND provides dedicated support for accessing the SAND database.
  • Oracle®
    Bulk loading support now available in the WPS Engine for Oracle.
  • SQL Server®
    To enhance existing SQL Server database access, a new SQLSERVR (please note spelling) facility in the ODBC engine.

More Information:

Existing Users should visit www.teamwpc.co.uk/support/wps/release where you can download a readme file containing more information about all the new features and fixes in WPS 2.5.1.

New Users should visit www.teamwpc.co.uk/products/wps where you can explore in more detail all the features available in WPS or request a free evaluation.

and from http://www.teamwpc.co.uk/products/wps/data it seems they are going on the BIG DATA submarine as well-

Data Support 

Extremely Large Data Size Handling

WPS is now able to handle extremely large data sets now that the previous limit of 2^31 observations has been lifted.

Access Standard Databases

Use I/O Features in WPS Core

  • CLIPBOARD (Windows only)
  • DDE (Windows only)
  • EMAIL (via SMTP or MAPI)
  • FTP
  • HTTP
  • PIPE (Windows and UNIX only)
  • SOCKET
  • STDIO
  • URL

Use Standard Data File Formats

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SAS to R Challenge: Unique benchmarking

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An interesting announcemnet from Revolution Analytics promises to convert your legacy code in SAS language not only cheaper but faster. It’ s a very very interesting challenge and I wonder how SAS users ,corporates, customers as well as the Institute itself reacts

http://www.revolutionanalytics.com/sas-challenge/

Take the SAS to R Challenge

Are you paying for expensive software licenses and hardware to run time-consuming statistical analyses on big data sets?

If you’re doing linear regressions, logistic regressions, predictions, or multivariate crosstabulations* there’s something you should know: Revolution Analytics can get the same results for a substantially lower cost and faster than SAS®.

For a limited time only, Revolution Analytics invites you take the SAS to R Challenge. Let us prove that we can deliver on our promise of replicating your results in R, faster and cheaper than SAS.

Take the challenge

Here’s how it works:

Fill out the short form below, and one of our conversion experts will contact you to discuss the SAS code you want to convert. If we think Revolution R Enterprise can get the same results faster than SAS, we’ll convert your code to R free of charge. Our goal is to demonstrate that Revolution R Enterprise will produce the same results in less time. There’s no obligation, but if you choose to convert, we guarantee that your license cost for Revolution R Enterprise will be less than half what you’re currently paying for the equivalent SAS software.**

It’s that simple.

We’ll show you that you don’t need expensive hardware and software to do high quality statistical analysis of big data. And we’ll show that you don’t need to tie up your computing resources with long running operations. With Revolution R Enterprise, you can run analyses on commodity hardware using Linux or Windows, scale to terabyte-class data problems and do it at processing speeds you would never have thought possible.

Sign up now, and we will be in touch shortly.

Take the challenge

 

—————————-

SAS is a registered trademark of the SAS Institute, Cary, NC, in the US and other countries.

*Additional statistical algorithms are being rapidly added to Revolution R Enterprise. Custom development services are also available.

**Revolution Analytics retains the right to determine eligibility for this offer. Offer available until March 31, 2011.

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Choosing R for business – What to consider?

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Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      2. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  2. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  3. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  4. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

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2011 Forecast-ying

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I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.

Anyways- I take note of the view of forecasting from

http://www.uiah.fi/projekti/metodi/190.htm

The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.

Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before

and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at

http://iianalytics.com/2010/12/2011-predictions-for-the-analytics-industry/

Regulatory and privacy constraints will continue to hamper growth of marketing analytics.

(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)

Anyway- back to the question I asked-

What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.

I define my industry as being online technology writing- research (with a heavy skew on stat computing)

My top 5 events for 2010 were-

1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more.  The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.

 

2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.

3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.

Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.

4) We didnt see that coming –

Technology continued to surprise with events (thats what we love! the surprises)

Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz,  Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).

people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too

5) Money-

SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.

 

2011 Top Three Forecasts

1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-

more unstructured data  is created (esp for marketing analytics) leading to enhanced volatility

2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.

False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.

3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.

 

 

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Test drive a Chrome notebook.

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Wanna test out the new Chrome OS.

Go to https://services.google.com/fb/forms/cr48basic/

and fill the form

Chrome

Test drive a Chrome notebook.

We have a limited number of Chrome notebooks to distribute, and we need to ensure that they find good homes. That’s where you come in. Everything is still very much a work in progress, and it’s users, like you, that often give us our best ideas about what feels clunky or what’s missing. So if you live in the United States, are at least 18 years old, and would like to be considered for our small Pilot program, please fill this out. We’ll review the requests that come in and contact you if you’ve been selected.

https://services.google.com/fb/forms/cr48basic/

 

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Using SAS/IML with R

Analyze That
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SAS just released an updated documentation to SAS/IML language with a special chapter devoted to using R

Here is an example-

CALL EXPORTMATRIXTOR( IMLMatrix, RMatrix ) ;

CALL IMPORTMATRIXFROMR( IMLMatrix, RExpr ) ;

If you have existing SAS licences and existing hardware and loots of data -this may be the best of both worlds- without getting into the mess of technically learning MKL threads/BLAS/Premium Packages/Cloud

Another thought- its a good professional looking help book, which is what more R packages can do (work on improving ease of their help/update vignettes)

 

Link-http://support.sas.com/documentation/cdl/en/imlug/63541/HTML/default/viewer.htm#r_toc.htm

 

Calling Functions in the R Language

[continuerule]

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