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!

Migration Tools Needed to Shift ML to Production

September 20, 2018

The confluence of accelerators like cloud GPUs along with the ability to handle data-rich HPC workloads will help push more machine learning projects into production, concludes a new study that also stresses the importan Read more…

By George Leopold

Kyoto University ACCMS Implements Fine-grained Power Management

September 19, 2018

Data center power management is a ubiquitous challenge and in few places is it more so than at Kyoto University Academic Center for Computing and Media Studies (ACCMS)) where power consumption limits were imposed followi Read more…

By Staff

What’s New in HPC Research: September (Part 1)

September 18, 2018

In this new bimonthly feature, HPCwire will highlight newly published research in the high-performance computing community and related domains. From exascale to quantum computing, the details are here. Check back every Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

A Crystal Ball for HPC

People are notoriously bad at predicting the future.  This very much includes experts. In the Forbes article “Why Most Predictions Are So Bad” Philip Tetlock discusses the largest and best-known test of the accuracy of expert predictions which show that any experts would do better if they make random guesses. Read more…

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and development. Among other things it would establish a National Quantu Read more…

By John Russell

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

No Go for GloFo at 7nm; and the Fujitsu A64FX post-K CPU

September 5, 2018

It’s been a news worthy couple of weeks in the semiconductor and HPC industry. There were several HPC relevant disclosures at Hot Chips 2018 to whet appetites Read more…

By Dairsie Latimer

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc 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