Nielsen and Intel Migrate HPC Efficiency and Data Analytics to Big Data

By Rob Farber

May 16, 2016

Nielsen has collaborated with Intel to migrate important pieces of HPC technology into Nielsen’s big-data analytic workflows including MPI, mature numerical libraries from NAG (the Numerical Algorithms Group), as well as custom C++ analytic codes. This complementary hybrid approach integrates the benefits of Hadoop data management and workflow scheduling with an extensive pool of HPC tools and C/C++ capabilities for analytic applications. In particular, the use of MPI reduces latency, permits reuse of the Hadoop servers, and co-locates the MPI applications close to the data.

John Mansour, vice president, Advanced Solutions Group, at Nielsen became interested in the integration of both Hadoop and HPC technology to enable faster, better, and more powerful analysis of the huge volumes of data collected by Nielsen as part of their Consumer Package Goods (CPG) market research. Nielsen is well-known for the ‘Nielsen ratings’ of audience measurement in Television, Radio, and online content. The company also provides Retail Measurement Services (RMS) that track and report on CPG sales around the world to understand sales performance. The success of Nielsen’s efforts are presented in his talk Bridging the Worlds of HPC and Big-Data at Supercomputing 2015.

Nielsen already utilizes the Cloudera Hadoop infrastructure to ingest and manage a daily deluge of data used in their market research. What Nielsen wanted was to make this infrastructure HPC-friendly so the wealth of scientific and data-analytic HPC codes created since the 1960s could be added to the Nielsen set of computational tools. This required integrating MPI (Message Passing Interface), which is the distributed framework utilized by the HPC community, into the Cloudera Hadoop framework. This integration allows Nielsen the choice of using C/C++ MPI in addition to Spark and Map-Reduce for situations that either require the performance or are a team’s preferred language.

Nielsen thinks Integrating Hadoop and MPI brings together the best of two complementary technologies. This integration will provide the data management capabilities of Hadoop with the performance of native MPI applications on the same cluster. Intel and Cloudera plan to provide production support for this integration in future releases of their software while Nielsen continues to explore the possibilities that such an integration will have for their clients.
Nielsen thinks Integrating Hadoop and MPI brings together the best of two complementary technologies. This integration will provide the data management capabilities of Hadoop with the performance of native MPI applications on the same cluster. Intel and Cloudera plan to provide production support for this integration in future releases of their software while Nielsen continues to explore the possibilities that such an integration will have for their clients.

MPI has been designed and refined since the 1990s to remove as much of the communications overhead from distributed HPC applications as possible, while Hadoop and the cloud computing infrastructure in general has been designed to run in a big-way on COTS (Commodity Off The Shelf) hardware where fault- and latency-tolerance is a requirement. A successful integration of the two means that existing MPI and data analytic codes can be ported without having to be re-implemented in another language such as SPARK, and very importantly, the integration can occur without affecting existing operational cloud infrastructure.

The integration, performed in collaboration with Intel, is quite straight-forward from a high-level perspective: simply start a python script that requests resources based on a set of input parameters and writes out a machine file that can be utilized by mpiexec to run the MPI job. The script then starts the MPI run and cleans up resources upon completion.

In actuality, the process is more complicated as it is necessary to ensure the data is in the right place and that errors are correctly handled. Nielsen uses Cloudera’s llama as the application master and yarn as the resource manager.

The performance of MPI in the Nielsen Hadoop framework has been superb and is expected to get even better. In testing with other Hadoop technologies, Nielsen has found MPI to consistently perform better than the others. Speedups come from the use of C/C++, sophisticated numerical libraries such as those offered by the NAG Numerical Algorithms Group and MPI’s design for low-latency communications which help in tightly coupled communications such the reduction operations needed in regressions and machine learning applications. In a future publication Nielsen will provide more detailed performance comparisons but typically see about a factor of between 5 to 10 times in performance compared to SPARK 1.5.1.

All this work to date has been at the proof-of-concept (POC) phase. In particular, high-performance storage I/O has proven to be an issue with significant amounts of runtime – sometimes as much as 85% – being consumed by the data loads. The challenge is that HDFS, which is written in Java, appears to be a bottleneck. Nielsen is experimenting with different technologies including local file systems and new apis such as RecordService and libhdfs3. Unfortunately, there are issues using common MPI data methods like mpiio which present a problem in Hadoop.

In addition to optimizing I/O performance, Nielsen has demonstrated significant performance benefits preloading data into distributed shared memory using BOOST shared memory STL vectors. With a working MPI and ability to integrate existing C/C++ codes, Nielsen has opened the door to a wealth of computational tools and analytic packages. In particular, the NAG library is a well-known, highly-regarded numerical toolkit. For example, NAG offers routines for data cleaning (including imputation and outlier detection), data transformations (scaling, principal component analysis), clustering, classification, regression models and machine learning methods (neural networks, radial basis function, decision trees, nearest neighbors), and association rules plus a plethora of utility functions.

Author Bio:
Rob Farber is a global technology consultant and author with an extensive background in scientific and commercial HPC plus a long history of working with national labs and corporations. He can be reached at info@techenablement.com.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hours at the Argonne Leadership Computing Facility (ALCF). The o Read more…

By HPCwire Staff

STEM-Trekker Badisa Mosesane Attends CERN Summer Student Program

June 27, 2017

Badisa Mosesane, an undergraduate scholar who studies computer science at the University of Botswana in Gaborone, recently joined other students from developing nations around the world in Geneva, Switzerland to particip Read more…

By Elizabeth Leake, STEM-Trek

The EU Human Brain Project Reboots but Supercomputing Still Needed

June 26, 2017

The often contentious, EU-funded Human Brain Project whose initial aim was fixed firmly on full-brain simulation is now in the midst of a reboot targeting a more modest goal – development of informatics tools and data/ Read more…

By John Russell

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a Department of Energy sponsored collaboration between the Univ Read more…

By John Russell

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

UMass Dartmouth Reports on HPC Day 2017 Activities

June 26, 2017

UMass Dartmouth's Center for Scientific Computing & Visualization Research (CSCVR) organized and hosted the third annual "HPC Day 2017" on May 25th. This annual event showcases on-going scientific research in Massach Read more…

By Gaurav Khanna

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hour Read more…

By HPCwire Staff

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a D Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This