The Convergence of Big Data and Extreme-Scale HPC

By Rob Farber

August 31, 2018

As we are heading towards extreme-scale HPC coupled with data intensive analytics like machine learning, the necessary integration of big data and HPC is a current hot topic of research that is, as Rashid Mehmood notes, “still in its infancy”.[i] Mehmood is the Research Professor of Big Data Systems and the Director for Research, Training and Consultancy at the High Performance Computing Centre, King Abdulaziz University (KAU) in Saudi Arabia.

A driving force to incorporate big data into HPC, Mehmood observed in his presentation at the first Middle East meeting of the Intel Extreme Performance Users Group at KAUST (King Abdullah University of Science and Technology) that, “Increasingly more data is being produced by scientific experiments from areas such as bioscience, physics, and climate, and therefore, HPC needs to adopt data-driven paradigms.”

Mehmood is not alone in his observation. Over the past four years the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that explored ways in which new forms of data-centric discovery might be integrated with the established, simulation-centric paradigm of the high performance computing (HPC) community. [ii]

Looking toward the future of cyberinfrastructure for science and engineering, BDEC produced a whitepaper that highlights the critical problems involved in the diverse patterns of when, where, and how data is to be produced, transformed, shared, and analyzed.  We view the main points of the BDEC whitepaper in light of current efforts in the HPC community, such as the Wrangler data analytics supercomputer at the Texas Advanced Computing Center (TACC), the Argonne lab-wide data service, and data management efforts at NERSC.

Understanding the bifurcation between the two software ecosystems

Comparing HPC to High-end Data Analysis (HDA) people use a different vernacular and focus on different key concepts.

Those who work in HDA speak of the 4Vs of big data which are: volume (scale of the data), velocity (speed of intake particularly with streaming data), variety (different forms of data), and veracity (the uncertainty of the data). Meanwhile HPC scientists tend to speak in terms of performance, scaling, and the power efficiency of a computation.

This difference in focus is reflected in the representative big data and HPC software stacks as summarized by Reed and Dongarra. [iii]

Figure 1: Different software ecosystems for high-end Data Analytics and for traditional computational science stacks (Image source: BDEC white paper)

The BDEC committee attributes this bifurcation in software stacks to the natural evolution of the two separate communities (e.g. scientists vs. academics and commercial software developers) working to address their separate problem domains.

Working over the past four decades, the HPC scientific community focused in increasing the ability of scientists to model and simulate using numerical models. Meanwhile, the data analytics ecosystem has been rapidly developed over the past fifteen to process the torrents of business, industrial process, and social network data now being generated by consumer devices and the burgeoning Internet of Things. For the most part, the data analytics software ecosystem was not developed by the scientific computing community as they work to adapt to the massive increases in data that is being produced by new instruments and sensor systems.

Both paradigms are collapsing from the data deluge

The BDEC whitepaper observes that both HPC and HDA workflows are eroding, if not collapsing under the onslaught of an apparently ever-growing data deluge[iv]. The future, they advocate, is to stop thinking in terms of a “big machine” but rather focus on the many unsolved problems surrounding wide-area, multi-stage workflows.

Figure 2: Current problem of data logistics: The highest concentrations of computing power and storage are in the “center” (i.e., in commercial clouds or HPC Centers), but much of the rapid increase in data volumes and the dramatic proliferation of data generators is occurring in edge environments. (Image source: BDEC whitepaper)

Such workflows represent a remarkable reversal in thinking about data, where the issue is not connecting the edge via “the last mile”. Instead, these workflows present a multidimensional “first mile problem” that is not currently addressed by either cloud-based HDA or on-premises based HPC solutions.  The BDEC whitepaper authors state, “Arguably, the main cyberinfrastructure challenge of the Big Data era is to adapt or replace the legacy paradigm with a new type of distributed services platform (DSP), one that combines computing, communication, and buffer/storage resources in a data processing network that is far more integrated than anything hitherto available”.

Current efforts to address the HPC data challenge

Figure 3: The general problem with multiple high volume generators at the edge: Edge environments (i.e., across network from the centralized facilities) are, or soon will be, experiencing unprecedented increases of data rates from diverse and rapidly proliferating sources. (Image source: BDEC whitepaper)

Both vendors and the HPC community are working to address the big data challenge in a variety of ways – especially with the general acceptance of AI and its dependence on large data sets. One example is how Intel is working with the ecosystems to develop a reference platform to guide the development of future infrastructure to leverage the growing data and the power of HPC supercomputers.

Academic projects such as the ones listed below have shown remarkable success and have provided valuable “lessons learned” to the HPC community.

The Argonne lab-wide data service

At Argonne National Laboratory, researchers are preparing for the exascale era by exploring ways to improve collaboration, eliminate barriers to using next-generation systems like Aurora, and facilitate seamless workflows.

In one example, a team at Argonne’s Data Science and Learning Division is developing a lab-wide service that will make it easier to access, share, analyze, and reuse large-scale datasets.

“Our motivation,” Ian Foster (Argonne Data Science and Learning Division Director and Distinguished Fellow) explains, “is to create increasingly rich data services so people don’t just come to the ALCF for simulation but for simulation and data-centric activities.” Foster also observes that, “It’s becoming increasingly impractical for supercomputing facility users to move their data to their home institution’s system for analysis”.

Aimed at enabling more effective data capture and discovery, as well as association of machine learning models with data collections for improved reproducibility and simpler deployment at scale, the service leverages well-known tools including Globus for research data management and the Argonne’s Petrel storage system.

TACC Wrangler

The Texas Advanced Computing Center (TACC) Wrangler supercomputer is the first of its kind and the most powerful data analysis system allocated in the Extreme Science and Engineering Discovery Environment (XSEDE). [v]

The system is designed to support HDA in an HPC environment. It provides around a half a petabyte (0.5 PB) high speed flash storage system that can be used to handle data analysis and processing workflows not practical on other systems. TACC notes, “Wrangler’s unique architecture handles the many aspects of the volume, velocity, and variety that can make digital data research difficult to handle on standard high performance systems”. [vi]

Very importantly, the system is dynamically provisioned by the users to handle different data workflows, including databases (both relational database systems and the newer noSQL style databases), Hadoop/HDFS based workflows (including MapReduce and Spark), and more custom workflows leveraging the flash-based parallel file system.

The success of Wrangler can be seen in the several hundred projects in the TACC Wrangler Data Portal that range from Advanced 3D Microscopy to a Zebrafish map that identifies recessive mutations in Zebrafish.

Recent research shows TACC at the forefront of deep-learning with a new algorithm that speeds training on the Stampede 2 supercomputer so it only take 11 minutes to train ImageNet.

Addressing the challenge of the two paradigm splits

The end goal, according to the BDEC whitepaper is to, “define a new, common and open Distributed Services Platform (DSP), one that offers programmable access to shared processing, storage and communication resources, and that can serve as a universal foundation for the component interoperability that novel services and applications will require”.[vii]

The following schematic reflects this vision.

Figure 4: Design pattern for a converged HPC and HDA future[viii] [ix] (image courtesy KAUST)
As the future recipient of the nation’s first exascale supercomputer, Argonne National Laboratory is particularly vested in taking a leadership role in testing the wide-area, multi-stage workflows recommended by the BDEC whitepaper. The Argonne Petrel project appears to be a good start. In particular, the ability to ingest data from instruments and simulation as well as collaborate and publish data regardless of the size of the data set are particularly valuable. An experimental effort using Kubernetes containers may help to democratize the software stack as well as data by providing HDA and HPC convergence through applications containers. The ability to dynamically provision the machine is a “lesson learned” from TACC.

Summary

It makes sense to cross-fertilize as much as possible between the HDA and HPC software stacks for big data while looking ahead to an even bigger data future. There is much to be gained as we know that big data is here to stay and exascale supercomputers will certainly play an essential role in helping scientists use this data to make ground-breaking scientific discoveries.

Rob Farber is a global technology consultant and author with an extensive background in HPC and in developing machine learning technology that he applies at national labs and commercial organizations. Rob can be reached at [email protected]

[i]    Usman S., Mehmood R., Katib I. (2018) Big Data and HPC Convergence: The Cutting Edge and Outlook. In: Mehmood R., Bhaduri B., Katib I., Chlamtac I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 224, pp. 11–26. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-94180-6_4

[ii] See http://www.exascale.org/bdec/ and specifically the report which can be downloaded here: http://www.exascale.org/bdec/sites/www.exascale.org.bdec/files/whitepapers/bdec_pathways.pdf.

[iii] The freely available BDEC whitepaper credits Reed and Dongarra citing Daniel A. Reed and Jack Dongarra. Exascale computing and big data. Commun. ACM, 58(7):56–68, June 2015. ISSN 0001-0782. doi: 10.1145/2699414. URL http://doi.acm.org/10.1145/2699414.

[iv] ibid

[v] http://www.dailytexanonline.com/2016/05/04/new-tacc-supercomputer-wrangles-big-data

[vi] https://portal.wrangler.tacc.utexas.edu/

[vii] http://www.exascale.org/bdec/sites/www.exascale.org.bdec/files/whitepapers/bdec_pathways.pdf.

[viii] Usman S., Mehmood R., Katib I. (2018) Big Data and HPC Convergence: The Cutting Edge and Outlook. In: Mehmood R., Bhaduri B., Katib I., Chlamtac I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 224, pp. 11–26. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-94180-6_4

[ix] Sardar Usman, Rashid Mehmood and Iyad Katib HPC & Big Data Convergence: The Cutting Edge & Outlook Poster presented at the first Middle East meeting of the Intel Extreme Performance Users Group, Intel IXPUG, KAUST, April 2018 https://epostersonline.com/ixpug-me2018/node/19

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!

HPC in Life Sciences Part 1: CPU Choices, Rise of Data Lakes, Networking Challenges, and More

February 21, 2019

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HPC (and now AI) use in life sciences. Without HPC writ lar Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized silicon designs catered toward general-purpose cloud computing Read more…

By Tiffany Trader

The Internet of Criminal Things—Trust in the Gods but Verify!

February 20, 2019

“Are we under attack?” asked Professor Elmarie Biermann of the Cyber Security Institute during the recent South African Centre for High Performance Computing’s (CHPC) National Conference in Cape Town. A quick show Read more…

By Elizabeth Leake, STEM-Trek

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

The Perils of Becoming Trapped in the Cloud

Terms like ‘open systems’ have been bandied about for decades. While modern computer systems are relatively open compared to their predecessors, there are still plenty of opportunities to become locked into proprietary interfaces. Read more…

Machine Learning Takes Heat for Science’s Reproducibility Crisis

February 19, 2019

Scientists are raising red flags about the accuracy and reproducibility of conclusions drawn by machine learning frameworks. Among the remedies are developing new ML systems that can question their own predictions, show Read more…

By George Leopold

HPC in Life Sciences Part 1: CPU Choices, Rise of Data Lakes, Networking Challenges, and More

February 21, 2019

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HP Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from th Read more…

By Ken Strandberg

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

Iowa ‘Grows Its Own’ to Fill the HPC Workforce Pipeline

February 13, 2019

The global workforce that supports advanced computing, scientific software and high-speed research networks is relatively small when you stop to consider the magnitude of the transformative discoveries it empowers. Technical conferences provide a forum where specialists convene to learn about the latest innovations and schedule face-time with colleagues from other institutions. Read more…

By Elizabeth Leake, STEM-Trek

Trump Signs Executive Order Launching U.S. AI Initiative

February 11, 2019

U.S. President Donald Trump issued an Executive Order (EO) today launching a U.S Artificial Intelligence Initiative. The new initiative - Maintaining American L Read more…

By John Russell

Celebrating Women in Science: Meet Four Women Leading the Way in HPC

February 11, 2019

One only needs to look around at virtually any CS/tech conference to realize that women are underrepresented, and that holds true of HPC. SC hosts over 13,000 H Read more…

By AJ Lauer

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This