HTC, Big Data and the God Particle

By Miha Ahronovitz

March 29, 2013

Who has seen the wind?

Neither I nor you:

But when the leaves hang trembling,

The wind is passing through.

— Christina Rossetti 1830–1894

The ancient sages say that a prophecy is conveyed in vision, in appearance, in sight, in revelation and in word. I often compare older predictions to what actually happens after the facts are known. For example I looked at IDC HPC predictions for 2010 in 2013 and I found them to be surprisingly accurate. HPCwire does not make prophecies, yet with astonishing intuition it captured all the significant events in performance computing for the last three decades.

The concept of High Throughput Computing (HTC) appeared on June 27, 1997, edition of HPCwire, during an interview with Miron Livny – a professor at University of Wisconsin, Madison – with Alan Beck, the HPCwire editor in chief at the time.

This month, NCSA’s (National Center for Supercomputing Applications) Advanced Computing Group (ACG) will begin testing Condor, a software system developed at the University of Wisconsin that promises to expand computing capabilities through efficient capture of cycles on idle machines. The software, operating within an HTC (High Throughput Computing) rather than a traditional HPC (High Performance Computing) paradigm, organizes machines into clusters, called pools, or collections of clusters called flocks, that can exchange resources. Condor then hunts for idle workstations to run jobs. When the owner resumes computing, Condor migrates the job to another machine.

In response to a question from Alan about the work they are doing at NCSA, Miron described the 1997 HTC plans:

“We play a dual role with respect to NCSA. On one hand, we’re a regional partner, with over 500 workstations here on campus. These will provide a source of cycles to NCSA and a testbed for scientists who would like to see how well their applications work in an HTC environment. On the other hand, we’re also an enabling technology, where our experience in building and maintaining Condor will contribute to the construction of the National Technology Grid. Thus, we hope we can soon move from a campus-wide to a nation-wide HTC system.”

By far the most enthusiastic response for HTC came from the high energy physics community. At that time, Fermilab in Batavia, 20 miles east of Chicago was the leading high energy physics facility. The Large Hadron Collider (LHC) at CERN was built from 1988 to 2008 and started operations in 2009. Before LHC, Fermilab’s Tevatron circular particle collider was the biggest in the world. For about thirty years, it was the number one particle collider serving high energy physics where the need for big data was prevalent long before this term became a media buzzword. On September 30, 2011, the Tevatron was closed during an emotional ceremony:

The Tevatron was the highest energy particle collider in the world until 2009, and is credited with discovering the top quark, two types of sigma baryon, the “Cascade B” Xi Baryon, and the “doubly strange” Omega-sub-b particle, as well as embarking on the hunt for the elusive “God particle,” throughout its storied history.

The Large Hadron Collider (LHC), which so far has cost nine billion dollars, just shut down for the next two years starting February 2013. This is called the long shut down (LS1) When it will reopen in 2015, it will start colliding protons at an unprecedented 14 TeV, (teraelectronvolt)

Scientists won’t be idle during the tunnel’s shutdown: CERN’s mass-storage systems are hanging onto 100 quadrillion bytes of data to analyze, most of which was acquired over the past year. This is equivalent to 700 years of high definition movies.

CERN Research Director Sergio Bertolucci says: “Other experiments here have ongoing analyses, so I’m looking forward to many interesting results emerging as LS1 progresses.”

OSG was created in response to the vast experience accumulated at Fermi Lab answering at the time the needs of high energy physics researchers. Since 2004, OSG and its underlying HTC technology proved itself “magna cum laude.” It directly contributed to the creation of the world’s largest computational grid at CERN, powerful enough to make the search for Higgs successful.

According to Lothar Bauerdick – OSG Executive Director – the name “Open Science Grid” was coined by Ruth Pordes during a walk in the vineyards around CERN in 2003. She is an associate head computing at Fermilab. An Oxford trained physicist – no matter what official title she holds – Ruth is recognized as the soul of OSG. Her 2004 slides from Condor Week sketched the first portrait of OSG to the outside world.

Another key contributor is Frank Wuerthwein, a star physics researcher from UC San Diego. He is “developing, deploying, and now operating a worldwide distributed computing system for high throughput computing with large data volumes expect to grow to Exabytes by 2020.” He is one of the most active evangelists for HTC and OSG and has the credibility because he himself is involved in the day-to-day search for dark matter.

The Open Science Grid (OSG) has access to over 60,000 processors without owning any one of them. The OSG is the real life National Science Grid, predicted in the HPCwire article sixteen years ago.

Next >>

In an interview with ATKearney – the management consulting group – Fermilab’s head of the Scientific Computing Division and a leading physicist searching for Higgs in America – Rob Roser said: “2012 has been the most exciting year in physics in the last 50 years with the confirmation of the God particle, but the computing advancements contributed significantly to this excitement.”

Roser says that big data is hard:

People who are successful at big data are those who are not overwhelmed by it. They are managing the data – the data’s not managing them.

The Higgs particle discovery is clearly explained in the abstract of this CERN paper from December 2012 signed by 2,932 Atlas collaborators:

Nearly 50 years ago, theoretical physicists proposed that a field permeates the universe and gives energy to the vacuum. This field was required to explain why some, but not all, fundamental particles have mass. The ATLAS experiment at the Large Hadron Collider at CERN has now observed the production of a new particle with a mass of 126 giga-electron volts and decay signatures consistent with those expected for the Higgs particle.

We are used to judge organizations in terms of ROI – return on investment. I would argue that the (ROI) of Higgs boson discovery is infinite. The relativity theory or Maxwell’s electromagnetic laws – just to mention randomly a few scientific breakthroughs – have infinite ROI. They generate wealth for many generations to come and for every citizen of the planet.

Figure 1 Open Science Grid real time usage http://display.grid.iu.edu/

Higgs particle research at the Large Hadron Collider generated in 2012, twenty five millions of gigabytes (23.8 petabytes which is approximated to 25 petabytes in popular media). The forecast increase is 20 percent annually.

Figure 2

“Nothing shocks me. I’m a scientist,” said Harrison Ford acting as Indiana Jones in Steven Spielberg’s 1981 movie Raiders of the Lost Ark.

Like Indiana Jones, CERN’s computer scientists tackled the challenge. They built an unprecedented massive grid, using the same HTC technology described in the 1997 article from HPCwire. The Worldwide LHC Computing Grid (WLCG) “is the world’s largest computing grid. It is based on two main grids – the European Grid Infrastructure in Europe, and Open Science Grid in the US – but has many associated regional and national grids (such as TWGrid in Taiwan and EU-IndiaGrid, which supports grid infrastructures across Europe and Asia).” CERN explains why this solution was selected:

This grid-based infrastructure is the most effective solution to the data-analysis challenge of the LHC, offering many advantages over a centralized system. Multiple copies of data can be kept at different sites, ensuring access for all scientists independent of geographical location; there is no single point of failure; computer centers in multiple time zones ease round-the-clock monitoring and the availability of expert support; and resources can be distributed across the world, for funding and sociological reasons.

It is about time to give HTC the credit. Without this technology, the Higgs boson particle would have been relegated to an elegant theory. Sooner or later, technological advances in computer science ought to be successful to prove the God particle exists. It so happened that the first breakthrough technology to get there is HTC.

Great physicists are like great prophets. Professor Higgs and his colleagues are among them. They detect “the unrevealed,” those whispers requiring great effort to comprehend. WLCG delivered the proof of their thought experiments, while they are still alive. Never mind Nobel prize speculations, this by itself is a huge reward. High Throughput Computing (HTC) and High Performance Computing (HPC) complement each other. This is a comment of Damien Hocking, the CTO of Corelium Software:

The logical progression of HTC is to keep improving until it can effectively process HPC workloads, and that’s where portability will come from. CPUs evolved as HTC devices and current software design presents HTC-like workloads to the OS (which is itself HTC software by design), but on a much shorter timescale. HPC workloads run today because the HTC architecture underneath is so efficient.

I asked Damien to elaborate:

My workstation has eight cores, and right now is running 110 processes and over 1,500 threads and is still responding quickly, even though I’m editing code, running a simulation, committing to Github and typing this, all at the same time. I’d argue that’s HTC, and that HPC today is realized by building and running it on top of the HTC platform that modern hardware and OS-es provide.

The Center for High Throughput Computing at University of Wisconsin is continuously adding new nodes. The latest addition is an HPC-capable node with 768 cores cluster with InfiniBand, using a SLURM scheduler and with HTCondor backfilling unused HPC cores with HTC jobs.

Many ask: “How do you determine if an application is suitable for HTC or HPC?” For the technical minded, there is a detailed presentation by Zach Miller from the University of Wisconsin. If you really are serious to start, there is an 2013 OSG User School. The website says:

“… you will learn to use high-throughput computing (HTC) systems – at your own campus or using the national Open Science Grid (OSG) – to run large-scale computing applications that are at the heart of today’s cutting-edge science … you will also go to the XSEDE13 Conference, July 22–25, in San Diego. This is a premier event for the high-performance computing (HPC) community, offering students a view into another approach to large-scale computing.”

CHTC and OSG leaderships executed the plan consistently and with remarkable skill. The 1997 dream of a 500 desktop grid transformed into the reality of WLCG grid designed to meet CERN’s unique needs. WLCG has in excess of 150,000 cores and growing.

Quoting the science fiction writer Arthur C. Clarke:

Every revolutionary idea seems to evoke three stages of reaction. They may be summed up by the phrases: (1) It’s completely impossible. (2) It’s possible, but it’s not worth doing. (3) I said it was a good idea all along.

And…

Any sufficiently advanced technology is indistinguishable from magic.

Will HTC technology for big data generate a new business like FaceBook or Google? Rob Roser says yes. The World Wide Web was invented by physicists at CERN, but other people took over. “I am perfectly happy to let corporate America and others take over,” he says, “and develop technology that we can use.”

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!

Geospatial Data Research Leverages GPUs

August 17, 2017

MapD Technologies, the GPU-accelerated database specialist, said it is working with university researchers on leveraging graphics processors to advance geospatial analytics. The San Francisco-based company is collabor Read more…

By George Leopold

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Centers (IPCCs) has resulted in a new Big Data Center (BDC) that Read more…

By Linda Barney

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last week the cloud giant released deeplearn.js as part of that in Read more…

By John Russell

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Spoiler Alert: Glimpse Next Week’s Solar Eclipse Via Simulation from TACC, SDSC, and NASA

August 17, 2017

Can’t wait to see next week’s solar eclipse? You can at least catch glimpses of what scientists expect it will look like. A team from Predictive Science Inc. (PSI), based in San Diego, working with Stampede2 at the Read more…

By John Russell

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

IBM Raises the Bar for Distributed Deep Learning

August 8, 2017

IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scal Read more…

By Tiffany Trader

IBM Storage Breakthrough Paves Way for 330TB Tape Cartridges

August 3, 2017

IBM announced yesterday a new record for magnetic tape storage that it says will keep tape storage density on a Moore's law-like path far into the next decade. Read more…

By Tiffany Trader

AMD Stuffs a Petaflops of Machine Intelligence into 20-Node Rack

August 1, 2017

With its Radeon “Vega” Instinct datacenter GPUs and EPYC “Naples” server chips entering the market this summer, AMD has positioned itself for a two-head Read more…

By Tiffany Trader

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

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

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

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

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

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

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

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

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

Leading Solution Providers

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

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

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

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

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

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

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

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