Grid Computing Done Right

By John Barr

November 2, 2009

Writing and implementing high performance computing applications is all about efficiency, parallelism, scalability, cache optimizations and making best use of whatever resources are available — be they multicore processors or application accelerators, such as FPGAs or GPUs. HPC applications have been developed for, and successfully run on, grids for many years now.

HPC on Grid

A good example of a number of different components of HPC applications can be seen in the processing of data from CERN’s Large Hadron Collider (LHC). The LHC is a gigantic scientific instrument (with a circumference of over 26 kilometres), buried underground near Geneva, where beams of subatomic particles — called Hadrons, either protons or lead ions — are accelerated in opposite directions and smashed into each other at 0.999997828 the speed of light. Its goal is to develop an understanding of what happened in the first 10-12 of a second at the start of the universe after the Big Bang, which will in turn confirm the existence of the Higgs boson, help to explain dark matter, dark energy, anti-matter, and perhaps the fundamental nature of matters itself.

Data is collected by a number of “experiments.” each of which is a large and very delicate collection of sensors able to capture the side effects caused by exotic, short lived particles that result from the particle collisions. When accelerated to full speed, the bunches of particles pass each other 40 million times a second, each bunch contains 10^11 particles, resulting in one billion collision events being detected every second. This data is first filtered by a system build from custom ASIC and FPGA devices. It is then processed by a 1,000 processor compute farm, and the filtering is completed by a 3,400 processor farm. After the data has been reduced by a factor of 180,000, it still generates 3,200 terabytes of data a year. And the HPC processing undertaken to reduce the data volume has hardly scratched the surface of what happens next.

Ten major compute sites around the world comprising many tens of thousands of processors (and many smaller facilities) are then put to work to interpret what happened during each “event.” The processing is handled, and the data distribution managed, by the LHC Grid, which is based on grid middleware called gLite that was developed by the major European project, Enabling Grids for E-sciencE (EGEE). High performance is achieved at every stage because the programs have been developed with a detailed knowledge and understanding of the grid, cluster or FPGA that they target.

From Grid to Cloud

Grid computing isn’t dead, but long live cloud computing. As far as early-adopter end users in our 451 ICE program are concerned, cloud computing is now seen very much as the logical endpoint for combined grid, utility, virtualization and automation strategies. Indeed, enterprise grid users see grid, utility and cloud computing as a continuum: cloud computing is grid computing done right; clouds are a flexible pool, whereas grids have a fixed resource pool; clouds provision services, whereas grids are provisioning servers; clouds are business, and grids are science. And so the comparisons go on, but through cloud computing, grids now appear to be at the point of meeting some of their promise.

One obvious way to regard cloud computing is as the new marketing-friendly name for utility computing, sprinkled with a little Internet pixie dust. In many respects, its aspirations match the original aspirations of utility computing — the ability to turn on computing power like a tap and pay on a per-drink basis. “Utility” is a useful metaphor, but it’s ambiguous because IT is simply not as fungible as electrical power, for example. The term never really took off. Grid computing, in the meantime, has been hung up on the pursuit of interoperability and the complexity of standardization. Taking the science out of grids has proved to be fairly intractable for all but high performance computing and specialist application tasks.

Clouds usefully abstract away the complexity of grids and the ambiguity of utility computing, and they have been adopted rapidly and widely. Since then everyone has been desperately trying to work out what cloud computing means and how it differs from utility computing. It doesn’t, really. Cloud computing is utility computing 2.0 with some refinements, principally, that it is delivered in ways we think are very likely to catch on.

But as cloud abstracts away the complexity, it also abstracts away visibility of the detail underlying execution platform. And without a deep understanding of how to optimize for the target platform, high performance computing becomes, well, just computing.

Building Applications

Human readable programs are translated into ones that can be executed on a computer by a program called a compiler. A compiler’s first step is that of lexical analysis, which converts a program into its logical components (i.e., language keywords, operators, numbers and variables). Next, the syntax analysis phase checks that the program complies with the grammar rules of the languages. The final two phases of optimization and code generation are often tightly linked so as to be one and the same thing (although some generic optimizations such as common sub-expression elimination are independent of code generation). The more the compiler knows about the target systems, the more sophisticated the optimizations it can perform, and the higher the performance of the resulting program.

But if a program is running in the cloud, the compiler doesn’t know any detail of the target architecture, and so must make lowest common denominator assumptions such as an x86 system with up to 8 cores. But much higher performance may be achieved by compiling for many more cores, or an MPI-based cluster, or GPU or FPGA.

Such technology has become a hot commodity. Google bought PeakStream, Microsoft bought the assets of Interactive Supercomputing and Intel bought RapidMind and Cilk Arts. So the major IT companies are buying up this parallel processing expertise.


Multicore causes mainstream IT a problem in that most applications will struggle to scale as fast as new multicore systems do, and most programmers are not parallel processing specialists. And this problem is magnified many times over when running HPC applications in the cloud, since even if the programmer and the compilers being used could do a perfect job of optimizing and parallelizing an application, the detail target architecture is unknown.

Is there a solution? In the long term new programming paradigms or languages are required, perhaps with a two-stage compilation process that compiles to an intermediate language but postpones the final optimization and code generation until the target system is known. And no, I don’t think Java is the answer.

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!

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

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

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--and a refresh of its inference server software packaged as Read more…

By George Leopold

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…

NSF Highlights Expanded Efforts for Broadening Participation in Computing

September 13, 2018

Today, the Directorate of Computer and Information Science and Engineering (CISE) of the NSF released a letter highlighting the expansion of its broadening participation in computing efforts. The letter was penned by Jam Read more…

By Staff

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


AMD @ SC17


ASRock Rack @ SC17

ASRock Rack



DDN Storage @ SC17

DDN Storage

Huawei @ SC17


IBM @ SC17


IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17


Lenovo @ SC17


Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17


Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17


Tyan @ SC17


Univa @ SC17


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

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

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

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

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

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

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