R on Windows HPC Server

From HPC Wire, the newsletter/site for all HPC news-

Source- Link

PALO ALTO, Calif., Sept. 20 — Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language, today announced it will deliver Revolution R Enterprise for Microsoft Windows HPC Server 2008 R2, released today, enabling users to analyze very large data sets in high-performance computing environments.

R is a powerful open source statistics language and the modern system for predictive analytics. Revolution Analytics recently introduced RevoScaleR, new “Big Data” analysis capabilities, to its R distribution, Revolution R Enterprise. RevoScaleR solves the performance and capacity limitations of the R language by with parallelized algorithms that stream data across multiple cores on a laptop, workstation or server. Users can now process, visualize and model terabyte-class data sets at top speeds — without the need for specialized hardware.

“Revolution Analytics is pleased to support Microsoft’s Technical Computing initiative, whose efforts will benefit scientists, engineers and data analysts,” said David Champagne, CTO at Revolution. “We believe the engineering we have done for Revolution R Enterprise, in particular our work on big-data statistics and multicore computing, along with Microsoft’s HPC platform for technical computing, makes an ideal combination for high-performance large scale statistical computing.”

“Processing and analyzing this ‘big data’ is essential to better prediction and decision making,” said Bill Hamilton, director of technical computing at Microsoft Corp. “Revolution R Enterprise for Windows HPC Server 2008 R2 gives customers an extremely powerful tool that handles analysis of very large data and high workloads.”

To learn more about Revolution R Enterprise and its Big Data capabilities, download thewhite paper. Revolution Analytics also has an on-demand webcast, “High-performance analytics with Revolution R and Windows HPC Server,” available online.

AND from Microsoft’s website

http://www.microsoft.com/hpc/en/us/solutions/hpc-for-life-sciences.aspx

REvolution R Enterprise »

REvolution Computing

REvolution R Enterprise is designed for both novice and experienced R users looking for a production-grade R distribution to perform mission critical predictive analytics tasks right from the desktop and scale across multiprocessor environments. Featuring RPE™ REvolution’s R Productivity Environment for Windows.

Of course R Enterprise is available on Linux but on Red Hat Enterprise Linux- it would be nice to see Amazom Machine Images as well as Ubuntu versions as well.

An Amazon Machine Image (AMI) is a special type of virtual appliance which is used to instantiate (create) a virtual machine within the Amazon Elastic Compute Cloud. It serves as the basic unit of deployment for services delivered using EC2.[1]

Like all virtual appliances, the main component of an AMI is a read-only filesystem image which includes an operating system (e.g., Linux, UNIX, or Windows) and any additional software required to deliver a service or a portion of it.[2]

The AMI filesystem is compressed, encrypted, signed, split into a series of 10MB chunks and uploaded into Amazon S3 for storage. An XML manifest file stores information about the AMI, including name, version, architecture, default kernel id, decryption key and digests for all of the filesystem chunks.

An AMI does not include a kernel image, only a pointer to the default kernel id, which can be chosen from an approved list of safe kernels maintained by Amazon and its partners (e.g., RedHat, Canonical, Microsoft). Users may choose kernels other than the default when booting an AMI.[3]

[edit]Types of images

  • Public: an AMI image that can be used by any one.
  • Paid: a for-pay AMI image that is registered with Amazon DevPay and can be used by any one who subscribes for it. DevPay allows developers to mark-up Amazon’s usage fees and optionally add monthly subscription fees.

Using Facebook Analytics (Updated)

People sceptical of any analytical value of Facebook should see the nice embedded analytics, which is a close rival and even more to Google Analytics for websites. It has recently been updated as well.

It is right there on the button called Insights on left margin of your Facebook Page

Like for the Facebook Page

http://facebook.com/Decisionstats

You can also use Export Data function to run customized analytical and statistical testing on your Corporate Page.

Older View———————————————————————————-

see screenshot of Demographics of 213 Decisionstats fans on Facebook ( FB doesnot allow individual views but only aggregate views for Privacy Reasons)

fb

AsterData gets $30 mill in funding

From the press release, the maker of Map Reduce based BI software gets 30 mill $ as Series C funding. Given the valuation recently by IBM to Netezza, AsterData seems set to cross the Billion Dollar valuation within the next 18-24 months IMO

Aster Data Closes $30 Million Series C Financing

Explosive Growth and Market Leadership Attracts New and Existing Investors

San Carlos, CA – September 22, 2010 – Aster Data, a market leader in big data management and advanced analytics, today announced that it has closed a $30 million Series C round of financing led by both new and existing investors. The company will use the new funding to accelerate growth, scale operations, and expand its global market share in the $20 billion database market – a market that is experiencing rapid growth as a result of both the explosion in data volumes across organizations and the urgent need to deliver a new class of analytics and data-driven applications. The Series C round of funding includes previous investors Sequoia Capital, JAFCO Ventures, Institutional Venture Partners, Cambrian Ventures, as well as an additional new strategic investor.  Also investing in this round is early investor David Cheriton, who previously backed high-growth companies including Google and VMware, and co-founded several successful technology companies.

Today’s Series C funding announcement underscores a year of strong innovation, execution, and overall momentum for the analytic database company. Key milestones include:

Strong sales growth: Since 2008, Aster Data has doubled revenue year-over-year and secured key customers that leverage Aster Data’s platform to address the big data management problem including MySpace, comScore, Barnes & Noble, and Akamai. Like so many organizations today,
Aster Data’s customers are experiencing explosive data growth across their organizations and recognize the need for rich, advanced analytics that give them deeper insights from their data.

Key executive hires: Quentin Gallivan, former CEO of both PivotLink and Postini and EVP of worldwide sales at Verisign, recently joined the company as Chief Executive Officer. In addition, earlier this year, John Calonico, previously at Interwoven, BEA, and Autodesk, joined as Chief Financial Officer; and Nitin Donde, formerly an executive at EMC and 3PAR, joined as Executive Vice President Engineering.  The strength and experience of Aster Data’s management team helps further establish a strong operational foundation for growth in 2010 and beyond.

Industry recognition: Aster Data was positioned in the “Visionaries” Quadrant of Gartner, Inc.’s

Data Warehouse Database Management Systems Magic Quadrant, published 2010 *; was recently named 2011 Tech Pioneer by the World Economic Forum; was named “Company to Watch” in the Information Management category of TechWeb’s Intelligent Enterprise 2010 Editors’ Choice Awards; and was awarded the 2010 San Francisco Business Times Technology and Innovation Award in the Best Product and Services Category.

Product Innovation: Aster Data continues to deliver ground-breaking capabilities to address the big data management and advanced analytics market need. Its recent announcement of
Aster Data nCluster 4.6 includes a column data store, making it the first hybrid row and column MPP DBMS with a unified SQL and MapReduce analytic framework for advanced analytics on large data sets. This year, Aster Data also delivered the most extensive library of pre-packaged MapReduce analytics totaling over 1000 functions, to ease and accelerate delivery of highly advanced analytic applications.

Aster Data’s analytic database, also called a ‘Data-Analytics Server’ is specifically designed to enable organizations to cost effectively store and analyze massive volumes of data. Aster Data leverages the power of commodity, general-purpose hardware, to reduce the cost to scale to support large data volumes and uniquely allows analysis of all data ‘in-database’ enabling richer and faster processing of large data sets. Aster Data’s in-database analytics engine uses the power of MapReduce, a parallel processing framework created by Google.

”The funding we received in our Series C round is a strong endorsement of Aster Data’s market leadership position and the high growth potential of the big data market,” said Quentin Gallivan, Chief Executive Officer, Aster Data. “The Aster Data team has executed exceptionally well to-date and I am excited to have the resources to accelerate the growth of the company as we expand our operations and execute aggressively across all fronts.”

KXEN EMEA User Conference 2010-Success in Business Analytics

KXEN User Conference-Prelim Agenda is out

Source-

http://www.kxen.com/index.php?option=com_content&task=view&id=647&Itemid=1109

THURSDAY, OCTOBER 28, 2010
09:30-10:00 AM Registration & Breakfast

10:00-10:45 AM Welcome & Opening Remarks,
by John Ball, CEO KXEN
10:45-11:30 AM Keynote Session by James Kobielus,
Senior Analyst at Forrester Research, Inc. and author
of “The Forrester WaveTM: Predictive Analytics & Data Mining Solutions, Q1 2010” report 

11:30-12:05 AM Customer Case Study:
The European Commission (Government)
12:05-12:50 PM General Session:
Teradata Advanced Analytics
12:50-02:00 PM Lunch Break & Exhibition
02:00-02:35 PM Customer Case Study: 
Virgin Media
(Communications)
02:35-03:05 PM General Session:
Sponsor Presentation
03:05-03:40 PM
Coffee Break & Exhibition

03:40-04:40 PM General Session:
The Factory Approach to Compete on Analytics
04:40-05:25 PM Customer Case Study: 
Insurance
05:30-06:30 PM Cocktail & Exhibition
07:30-00:00 PM Gala Dinner
FRIDAY, OCTOBER 29, 2010
08:30-09:00 AM
Registration & Breakfast

09:00-10:00 AM Keynote Presentation:
The CTO Talk
10:00-10:30 AM Customer Case Study: 
MonotaRO
(Japan – Retail)
10:30-10:55 AM
Coffee Break & Exhibition

10:55-11:30 AM General Session: 
Sponsor Presentation
11:30-12:05 PM Customer Case Study: 
Aviva
(Poland – Insurance)
12:05-01:00 PM Lunch Break & Exhibition
01:00-01:45 PM General Session: 
How Social Network Analysis Can Boost your Marketing Performance
01:45-02:20 PM Customer Case Study:
Financial Services
02:20-02:45 PM Closing Remarks,
by John Ball, CEO KXEN
02:45-03:00 PM
Coffee Break & Exhibition

Optional: Technical Training (Complimentary to all Attendees)
02:45-04:00 PM Hands-On Training #1: Getting Started with KXEN Analytical Data Management (ADM)
04:00-04:15 PM
Coffee Break

04:15-05:30 PM Hands-On Training #2: Getting Started with KXEN Modeling Factory (KMF)

Windows Azure vs Amazon EC2 (and Google Storage)

Here is a comparison of Windows Azure instances vs Amazon compute instances

Compute Instance Sizes:

Developers have the ability to choose the size of VMs to run their application based on the applications resource requirements. Windows Azure compute instances come in four unique sizes to enable complex applications and workloads.

Compute Instance Size CPU Memory Instance Storage I/O Performance
Small 1.6 GHz 1.75 GB 225 GB Moderate
Medium 2 x 1.6 GHz 3.5 GB 490 GB High
Large 4 x 1.6 GHz 7 GB 1,000 GB High
Extra large 8 x 1.6 GHz 14 GB 2,040 GB High

Standard Rates:

Windows Azure

  • Compute
    • Small instance (default): $0.12 per hour
    • Medium instance: $0.24 per hour
    • Large instance: $0.48 per hour
    • Extra large instance: $0.96 per hour
  • Storage
    • $0.15 per GB stored per month
    • $0.01 per 10,000 storage transactions
  • Content Delivery Network (CDN)
    • $0.15 per GB for data transfers from European and North American locations*
    • $0.20 per GB for data transfers from other locations*
    • $0.01 per 10,000 transactions*

Source –

http://www.microsoft.com/windowsazure/offers/popup/popup.aspx?lang=en&locale=en-US&offer=MS-AZR-0001P

and

http://www.microsoft.com/windowsazure/windowsazure/

Amazon EC2 has more options though——————————-

http://aws.amazon.com/ec2/pricing/

Standard On-Demand Instances Linux/UNIX Usage Windows Usage
Small (Default) $0.085 per hour $0.12 per hour
Large $0.34 per hour $0.48 per hour
Extra Large $0.68 per hour $0.96 per hour
Micro On-Demand Instances Linux/UNIX Usage Windows Usage
Micro $0.02 per hour $0.03 per hour
High-Memory On-Demand Instances
Extra Large $0.50 per hour $0.62 per hour
Double Extra Large $1.00 per hour $1.24 per hour
Quadruple Extra Large $2.00 per hour $2.48 per hour
High-CPU On-Demand Instances
Medium $0.17 per hour $0.29 per hour
Extra Large $0.68 per hour $1.16 per hour
Cluster Compute Instances
Quadruple Extra Large $1.60 per hour N/A*
* Windows is not currently available for Cluster Compute Instances.

http://aws.amazon.com/ec2/instance-types/

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage (150 GB plus 10 GB root partition)
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage (2×420 GB plus 10 GB root partition)
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage (4×420 GB plus 10 GB root partition)
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPUcapacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Also http://www.microsoft.com/en-us/sqlazure/default.aspx

offers SQL Databases as a service with a free trial offer

If you are into .Net /SQL big time or too dependent on MS, Azure is a nice option to EC2 http://www.microsoft.com/windowsazure/offers/popup/popup.aspx?lang=en&locale=en-US&offer=COMPARE_PUBLIC

Updated- I just got approved for Google Storage so am adding their info- though they are in Preview (and its free right now) 🙂

https://code.google.com/apis/storage/docs/overview.html

Functionality

Google Storage for Developers offers a rich set of features and capabilities:

Basic Operations

  • Store and access data from anywhere on the Internet.
  • Range-gets for large objects.
  • Manage metadata.

Security and Sharing

  • User authentication using secret keys or Google account.
  • Authenticated downloads from a web browser for Google account holders.
  • Secure access using SSL.
  • Easy, powerful sharing and collaboration via ACLs for individuals and groups.

Performance and scalability

  • Up to 100 gigabytes per object and 1,000 buckets per account during the preview.
  • Strong data consistency—read-after-write consistency for all upload and delete operations.
  • Namespace for your domain—only you can create bucket URIs containing your domain name.
  • Data replicated in multiple data centers across the U.S. and within the same data center.

Tools

  • Web-based storage manager.
  • GSUtil, an open source command line tool.
  • Compatible with many existing cloud storage tools and libraries.

Read the Getting Started Guide to learn more about the service.

Note: Google Storage for Developers does not support Google Apps accounts that use your company domain name at this time.

Back to top

Pricing

Google Storage for Developers pricing is based on usage.

  • Storage—$0.17/gigabyte/month
  • Network
    • Upload data to Google
      • $0.10/gigabyte
    • Download data from Google
      • $0.15/gigabyte for Americas and EMEA
      • $0.30/gigabyte for Asia-Pacific
  • Requests
    • PUT, POST, LIST—$0.01 per 1,000 requests
    • GET, HEAD—$0.01 per 10,000 requests

Matlab-Mathematica-R and GPU Computing

Matlab announced they have a parallel computing toolbox- specially to enable GPU computing as well

http://www.mathworks.com/products/parallel-computing/

Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.

MATLAB GPU Support

The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

Parallel Computing with MATLAB on Amazon Elastic Compute Cloud (EC2)

Also a video of using Mathematica and GPU

Also R has many packages for GPU computing

Parallel computing: GPUs

from http://cran.r-project.org/web/views/HighPerformanceComputing.html

  • The gputools package by Buckner provides several common data-mining algorithms which are implemented using a mixture of nVidia‘s CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.
  • The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia’s CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.
  • The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.
  • The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).
  • The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).

I tried to search for SAS and GPU and SPSS and GPU but got nothing. Maybe they would do well to atleast test these alternative hardwares-

Also see Matlab on GPU comparison for the product Jacket vs Parallel Computing Toolbox

http://www.accelereyes.com/products/compare

A Google App for Sales- ERPLY

While not quite Salesforce.com, a promising start for the first ERP Google App at https://www.google.com/enterprise/marketplace/viewListing?productListingId=5759+8485502070963042532

An interesting development-maybe there could be some statistical or BI apps on Google App Marketplace soon 😉