Mission Possible — Greening the HPC Data Center

By Nicole Hemsoth

August 31, 2009

The Cray XT5 Jaguar supercomputer at Oak Ridge National Laboratory (ORNL) is big in every respect. It’s big physically – the machine’s 284 cabinets sprawl across 5,700 square feet, a space slightly larger than a college basketball court. And it’s powerful – with its 182,000 processing cores, 362 terabytes of memory and a 10 petabyte file system, Jaguar is rated at 1.64 petaflops; making it the fastest machine in the world today for open science.
Cray XT5 Jaguar Supercomputer at ORNL

These impressive computational capabilities also make for a supercomputer that is ravenously hungry for power. Jaguar consumes up to seven megawatts, enough to power a town or small city. About half the power is used to operate the system, the other half to cool it. Right now power bills at supercomputing centers around the world are running into the tens of million dollars annually. But as planned upgrades move these centers into the exascale range, potential spending on power goes off the chart.

You might think that power and cooling problems of this sort is the exclusive domain of the big high performance computing (HPC) machines at major government scientific laboratories. That’s not the case. Smaller clusters operating in the terabyte range are just as prone to power consumption problems. This makes the greening of the data center a daunting challenge that must be addressed.

It’s no wonder that Sumit Gupta, senior manager of the Tesla™ GPU computing business unit at NVIDIA® reports: “Power consumption and the need for more energy efficient computing systems is top of mind for most of our customers.”

In fact, he emphasizes that the reason for the current intense interest in green computing is that data centers supporting scientific computation and enterprise HPC are in a power crisis. Gupta estimates that between 40% and 60% of the energy costs in these data centers is attributable to cooling (see chart below).
Breakout of HPC data center energy costs

The fact is that traditional architecture for supercomputing just does not scale well. Back in the early 1980s, the first gigaflop machine, a Cray X-MP, required a modest 60 kW. By the mid-1990s, a one teraflop system required 850 kW. Today’s petaflop supercomputers with their megawatt appetites will look positively dainty compared to the 25 MW or more that the next decade’s exascale machines will need if current architectural trends continue unabated.

Supercomputing is a highly skewed field. Of the top 500 supercomputers, only a few, such as the machines at ORNL and Los Alamos National Laboratory, operate in the petaflop range. The vast majority of HPC clusters are all cruising along at less than 30 teraflops. But, pound for pound, their power challenges are just as daunting. For example, a small research cluster with 32 CPU servers valued at about $120,000 requires 21 kW of power for the servers alone. The annual cost for power and cooling runs almost $40,000 annually. The bottom line: In three years, you will have spent as much on these operating costs as you initially paid for the servers themselves.

Gupta notes that the new data center economics are having an impact on the academic community. Professors writing grants for additional HPC horsepower for their investigations are increasingly being asked by the university computing facilities to include power and cooling costs in their grant request. In the past, IT picked up the tab as an overhead expense.

Compounding the power/cooling problem is the fact that the computational capabilities of traditional HPC CPUs – including the latest multi-core CPUs – have not kept pace with the demand for computing performance on the part of researchers in science and industry. The more multi-core CPUs are added to a system, the greater the power and cooling requirements. Given this situation, being green is not only not easy, it’s almost impossible. Fortunately, recent advances in computer architecture point to a way out of this dilemma.

Going Green with GPUs

A promising solution that has recently become very popular is the use of GPUs for scientific computing. Although GPUs were initially designed as fixed function graphics chips, they have, over the years, become increasingly programmable.

In recognizing both a need and an opportunity, NVIDIA introduced a completely new GPU architecture that was designed from the ground up to be fully programmable; and function as both a graphics engine and a general purpose scientific processor. NVIDIA debuted the first GPU based on this new massively parallel architecture, called CUDA™, in 2006. This new architecture also allows developers to program to the GPU using traditional and high-level languages like C and Fortran. Since 2006, NVIDIA’s C language with CUDA extensions has been widely adopted with hundreds of applications and research papers published, many of which can be found on CUDA Zone.

The combination of advanced, programmable GPUs and CUDA has allowed NVIDIA to design processors and entire systems that provide supercomputing capabilities with an outstanding green performance per watt.

For example, one green computing powerhouse is the NVIDIA Tesla Personal Supercomputer (PSC). This supercomputer sits on your desktop and plugs into a standard office power socket. Containing up to four Tesla C1060 GPU computing processors, a Tesla PSC delivers nearly four teraflops of compute capability and provides application performance that is 250 times faster than traditional CPU-based PCs or workstations. This gives computational researchers and technical professionals a dedicated computing resource at their deskside that is much faster and more energy-efficient than a shared cluster in the data center.

The same high performance can be scaled to the data center by building clusters using the Tesla S1070 1U GPU systems. Whether building small research clusters or building out a petaflop cluster, Tesla S1070 GPU-based systems deliver unprecedented performance per watt.

GPU computing provides a co-processing environment that mixes multi-core CPUs and many-core GPUs for optimized performance and energy efficiency. Scientists, engineers and business users can handle the next generation of computing problems using advanced algorithms. The system’s incredible performance per watt means that IT managers can upgrade data center performance without expensive infrastructure modifications for power and cooling, and a big jump in energy bills.

Hybrid clusters with CPUs and GPUs can handle an extensive range of computationally intensive applications for a wide variety of industries. Just a few of the applications areas that can benefit from GPUs include: computational chemistry, fluid dynamics, digital content creation, financial market modeling, genomics, medical imaging, oil and gas exploration, and research and scientific computing.

Tesla Systems at Work – Greening the Data Center

Here are just a few examples of energy efficient NVIDIA Tesla systems at work:

  • Temple University – An excellent example of energy efficient supercomputing that leverages a GPU-based solution is provided by Temple University. Researchers are running complex molecular dynamics simulations to devise better shampoos and detergents by discovering surfactants that more effectively attract dirt. Using a workstation powered by Tesla GPUs, they are achieving the same performance as a cluster of 32 dual-socket, quad-core CPU servers. The use of the NVIDIA system is not only generating savings in cost, space and power – in addition, the researchers are enjoying a major boost in productivity by being able to access a personal workstation at all times and reduce their dependency on the HPC cluster.
     
  • Hess Corporation – The Hess Corporation is a leading global independent energy company. Its geophysicists use seismic data collected from the surface of oceans to “read” and interpret the sound waves, which travel at varying velocities as they pass through the different densities of rock, sand, salt, oil and gas.

    “The challenge to geophysicists is how to accelerate the very time consuming task of analyzing data presented in flat, two-dimensional images to better understand the three dimensional subsurface geology,” says Mike Zebrowski, manager of Geoscience Development for Hess. “By using more of the data in a 3D visualization manner we can achieve a better understanding of seismic information.”

    Using the CUDA parallel architecture, Hess developers were able to port 2D seismic code to a NVIDIA 32-node GPU-based cluster to speed up their explorations. The cluster replaced 2,000 CPU servers, a 20 times saving in capital expense. Savings also extended to the system’s power requirements, with server power requirements dropping from 1340 kWatts to 47 kWatts, a 28 times reduction. Power costs dropped accordingly, making for a much greener data center.

    According to Scott Morton, Hess’s manager of geophysical technology, they plan to add another 80 GPU nodes to the cluster.

  • BNP Paribas – This European leader in global banking and financial services relies on Tesla S1070 GPUs for their pricing algorithms. With eight Tesla GPUs, the performance matches a 500 CPU server cluster. But according to BNP IT managers, they are most excited about the energy saving that are achieving – one of their primary areas of concern.

    A significant portion of the calculations – about one teraflop – performed for Global Equities and Commodity Derivatives is transferred to the NVIDIA GPU-based platform. This allows a 100-fold increase in the amount of calculation achieved per watt. The new NVIDIA platform consuming 2 kW replaces more than 500 traditional CPU cores that required 24 kW for power and cooling.

    The massively-parallel, many-core architecture of Tesla GPUs delivers higher performance, and allow researchers and engineers in academia, government and business to tackle some of today’s most demanding applications – applications that previously could only be handled by massive supercomputers or clusters. But an added bonus is that these users can get the results they need without creating a major burden on the environment or the data center’s power and cooling budget.

High density computing allows data center managers to meet the constantly increasing performance demands of their users without corresponding demands on the center’s electrical and thermal capabilities. Because GPU-based systems combine supercomputer-level performance with significantly lower power and cooling costs, the goal of greening the data center is now mission possible.

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!

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays 2017 Wraps Up in Barcelona

May 18, 2017

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth loo Read more…

By Kim McMahon

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

NSF, IARPA, and SRC Push into “Semiconductor Synthetic Biology” Computing

May 18, 2017

Research into how biological systems might be fashioned into computational technology has a long history with various DNA-based computing approaches explored. N Read more…

By John Russell

DOE’s HPC4Mfg Leads to Paper Manufacturing Improvement

May 17, 2017

Papermaking ranks third behind only petroleum refining and chemical production in terms of energy consumption. Recently, simulations made possible by the U.S. D Read more…

By John Russell

PRACEdays 2017: The start of a beautiful week in Barcelona

May 17, 2017

Touching down in Barcelona on Saturday afternoon, it was warm, sunny, and oh so Spanish. I was greeted at my hotel with a glass of Cava to sip and treated to a Read more…

By Kim McMahon

NSF Issues $60M RFP for “Towards a Leadership-Class” System

May 16, 2017

In case you missed it, the National Science Foundation issued the request for proposals (RFP) for the next ‘Towards a Leadership-Class Computing Facility – Read more…

By John Russell

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

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

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Bright Computing 8.0 Adds Azure, Expands Machine Learning Support

May 9, 2017

Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The Read more…

By John Russell

Microsoft Azure Will Debut Pascal GPU Instances This Year

May 8, 2017

As Nvidia's GPU Technology Conference gets underway in San Jose, Calif., Microsoft today revealed plans to add Pascal-generation GPU horsepower to its Azure clo Read more…

By Tiffany Trader

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

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

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

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

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Leading Solution Providers

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

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

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