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.

Conclusion

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!

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Researchers Scale COSMO Climate Code to 4888 GPUs on Piz Daint

October 17, 2017

Effective global climate simulation, sorely needed to anticipate and cope with global warming, has long been computationally challenging. Two of the major obstacles are the needed resolution and prolonged time to compute Read more…

By John Russell

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Cluster Competition coverage has come to its natural home: H Read more…

By Dan Olds

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

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

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute 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

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 w Read more…

By John Russell

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

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Leading Solution Providers

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

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

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

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

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