Language Flags

Translation Disclaimer

HPCwire Enterprise Tech HPCwire Japan


February 20, 2013

HP Diving Deeper into R Parallelization


Despite R’s popularity as a statistical tool, its single-threaded nature might be a short-coming in scaling its use as a tool for big data applications. 

Not if they can help it, says HP.

In a recent webinar titled, “Unlocking the Massive Potential of Sensor Data and the Internet of Things,” HP discussed the integration of R in their Vertica platform, explaining that plans to expand the parallel capabilities of R are underway.  

HP says that because much of the advanced analysis of sensor data involves statistics, integration with R is a real big plus allowing analysis to be done right in the database.  However, scaling concerns for the statistical language have caused HP to search for solutions to make it's usefulness in big data extend past a single thread.

“[R] is single-threaded and limited by the amount of RAM on the machine it is running on, which makes it challenging to run R programs on big data,” said Lakshimikant Shrinivas, Pratibha Rana, and Mark Waner, software engineers with Vertica Systems in a recent article.  “There are efforts underway to remedy this situation, which essentially fall into one of the following two categories (1) Integrate R into a parallel database, (2) or parallelize R so it can process big data.”

The Vertica teams plan for doing this includes two prongs.  First they plan on running multiple instances of the R algorithm in parallel with queries that chunk the data independently, while relying on SQL to put the operations in proper order for querying partitioned data. 

 “The parallelism comes from processing independent chunks of data simultaneously (referred to as data parallelism),” explains the Vertica engineering trio.  “SQL, being a declarative language, allows database query optimizers to figure out the order of operations, as well as which of them can be done in parallel.”

Secondly, the team explains, they will be leveraging column-store technology for optimized data exchange for querying non-partitioned data. 

“It is important to note that even for non-data parallel tasks (functions that operate on input that is basically one big chunk of non-partitioned data), Vertica’s implementation provides better performance since computation runs on a server instead of client, and we have optimized data flow between DB and R (no need to parse data again),” explains the team.

These developments should come as good news for the expanding user base of the open source R language, which has been growing year over year.  According to the most recent survey by Rexer Analytics, close to half of all data miners (47%) are using R as a part of their toolkit, with historical trends showing year-over-year increases in adoption.

Share Options


Subscribe

» Subscribe to our weekly e-newsletter


Discussion

There are 0 discussion items posted.

 

Most Read Features

Most Read News

Most Read This Just In

Cray Supercomputer

Sponsored Whitepapers

Planning Your Dashboard Project

02/01/2014 | iDashboards

Achieve your dashboard initiative goals by paving a path for success. A strategic plan helps you focus on the right key performance indicators and ensures your dashboards are effective. Learn how your organization can excel by planning out your dashboard project with our proven step-by-step process. This informational whitepaper will outline the benefits of well-thought dashboards, simplify the dashboard planning process, help avoid implementation challenges, and assist in a establishing a post deployment strategy.

Download this Whitepaper...

Slicing the Big Data Analytics Stack

11/26/2013 | HP, Mellanox, Revolution Analytics, SAS, Teradata

This special report provides an in-depth view into a series of technical tools and capabilities that are powering the next generation of big data analytics. Used properly, these tools provide increased insight, the possibility for new discoveries, and the ability to make quantitative decisions based on actual operational intelligence.

Download this Whitepaper...

View the White Paper Library

Sponsored Multimedia

Webinar: Powering Research with Knowledge Discovery & Data Mining (KDD)

Watch this webinar and learn how to develop “future-proof” advanced computing/storage technology solutions to easily manage large, shared compute resources and very large volumes of data. Focus on the research and the application results, not system and data management.

View Multimedia

Video: Using Eureqa to Uncover Mathematical Patterns Hidden in Your Data

Eureqa is like having an army of scientists working to unravel the fundamental equations hidden deep within your data. Eureqa’s algorithms identify what’s important and what’s not, enabling you to model, predict, and optimize what you care about like never before. Watch the video and learn how Eureqa can help you discover the hidden equations in your data.

View Multimedia

More Multimedia

NVIDIA

Job Bank

Datanami Conferences Ad

Featured Events

May 5-11, 2014
Big Data Week Atlanta
Atlanta, GA
United States

May 29-30, 2014
StampedeCon
St. Louis, MO
United States

June 10-12, 2014
Big Data Expo
New York, NY
United States

June 18-18, 2014
Women in Advanced Computing Summit (WiAC ’14)
Philadelphia, PA
United States

June 22-26, 2014
ISC'14
Leipzig
Germany

» View/Search Events

» Post an Event