Grids or Clouds for HPC?

By Wolfgang Gentzsch

November 2, 2009

Time and again, people ask questions like “Will HPC move to the cloud?” or “Now that cloud computing is accepted, are grids dead?” or even “Should I now build my grid in the cloud?” Despite all the promising developments in the grid and cloud computing space, and the avalanche of publications and talks on this subject, many people still seem to be confused and hesitant to take the next step. I think there a number of issues driving this uncertainty.

Grids didn’t keep all their promises

Grids did not evolve (as some of us originally thought) into the next fundamental IT infrastructure for everything and for everybody. Because of the diversity of computing environments we had to develop different middleware stacks (department, enterprise, global, compute, data, sensors, instruments, etc.), and had to face different usage models with different benefits. Enterprise grids were (and are) providing better resource utilization and business flexibility, while global grids are best suited for complex R&D application collaboration with resource sharing. For enterprise usage, setting up and operating grids was often complicated. For researchers this characteristic was seen to be a necessary evil. Implementing complex applications on HPC systems has never been easy. So what.

Grid: the way station to the cloud

After 40 years of dealing with HPC, grid computing was indeed the next big thing for the grand challenge, big-science researcher, while for the enterprise CIO, the grid was a way station on its way to the cloud model. For the enterprise today, clouds are providing all the missing pieces: easy to use, economies of scale, business elasticity up and down, and pay-as you go (thus getting rid of some CapEx). And in cases where security matters, there is always the private cloud. In more complex enterprise environments, with applications running under different policies, private clouds can easily connect to public clouds — and vice versa — into a hybrid cloud infrastructure, to balance security with efficiency.

Different policies, what does that mean?

No application job is alike. Jobs differ by priority, strategic importance, deadline, budget, IP and licenses. In addition, the nature of the code often necessitates a specific computer architecture, operating system, memory, and other resources. These important differentiating factors strongly influence where and when a job is running. For any new type of job, a set of specific requirements decide on the set of specific policies that have to be defined and programmed, such that any of these jobs will run just according to these policies. Ideally, this is guaranteed by a dynamic resource broker that controls submission to grid or cloud resources, be they local or global, private or public.

Grids or clouds?

One important question is still open: how do I find out, and then tell the resource broker, whether my application should run on the grid or in the cloud? The answer, among others, depends on the algorithmic structure of the compute-intensive part of the program, which might be intolerant of high latency and low bandwidth. This has been observed with benchmark results. The performance limitations are exhibited mainly by parallel applications with tightly-coupled, data-intensive inter-process communication, running on hundreds or even thousands of processors or cores.

The good news is, however, that many HPC applications do not require high bandwidth and low latency. Examples are parameter studies often seen in science and engineering, with one and the same application executed for many parameters, resulting in many independent jobs, such as analyzing the data from a particle physics collider, identifying the solution parameter in optimization, ensemble runs to quantify climate model uncertainties, identifying potential drug targets via screening a database of ligand structures, studying economic model sensitivity to parameters, and analyzing different materials and their resistance in crash tests, to name just a few.

A Grid in the cloud 

One great example of a project that has built a grid in the cloud is Gaia, a European Space Agency funded mission which aims to monitor one billion stars. Amazon Machine Images (AMIs) were configured for the Oracle database grid and processing software (AGIS). The result is an Oracle grid running inside the Amazon Elastic Compute Cloud (EC2). To process five years of data for 2 million stars, 24 iterations of 100 minutes each translates into 40 hours of 20 EC2 CPU instances. Benefits include reduced costs (50 percent compared to the in-house solution) and massive scalability on demand without having to invest in new
infrastructure or train new personnel. And only a single line of source code was changed in order to get it to run in the cloud.

HPC needs grids and clouds

According to the DEISA Extreme Computing Initiative (DECI), there are still plenty of grand challenge science and engineering applications that can only run effectively on the largest and most expensive supercomputers. In DEISA, a European HPC grid, also called the HPC Ecosystem, is made up of 11-teraflops nodes.

Today, nobody would build an HPC cloud for these particular applications. It simply wouldn’t be a profitable business, the “market” (i.e., the HPC users) is far too small and thus lacks economy of scale. In some specific science application scenarios, with complex workflows of different tasks (nodes), a hybrid infrastructure might make sense: cloud capacity resources and HPC capability nodes, providing the best of both worlds.

About the Author

Wolfgang Gentzsch is Dissemination Advisor for the DEISA Distributed European Initiative for Supercomputing Applications. He is an adjunct professor of computer science at Duke University in Durham, and a visiting scientist at RENCI Renaissance Computing Institute at UNC Chapel Hill, both in North Carolina. From 2005 to 2007, he was the Chairman of the German D-Grid Initiative. Recently, he was Vice Chair of the e-Infrastructure Reflection Group e-IRG; Area Director of Major Grid Projects of the OGF Open Grid Forum Steering Group; and he is a member of the US President’s Council of Advisors for Science and Technology (PCAST-NIT).

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

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

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

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

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

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

Leading Solution Providers

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

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