Titan Sets High Water Mark for GPU Supercomputing

By Michael Feldman

October 29, 2012

Oak Ridge National Laboratory (ORNL) has officially launched its much-anticipated Titan supercomputer, a Cray XK7 machine that will challenge IBM’s Sequoia for petaflop supremacy. With Titan, ORNL gets a system that is 10 times as powerful as Jaguar, the lab’s previous top system upon which the new machine is based. With a reported 27 peak petaflops, Titan now represents the most powerful number-cruncher in the world.

The 10-fold performance leap from Jaguar to Titan is courtesy of NVIDIA’s brand new K20 processors – the Kepler GPU that will be formally released sometime before the end of the year. Although the Titan upgrade also includes AMD’s latest 16-core Opteron CPUs, the lion’s share of the FLOPS will be derived from the NVIDIA chips.

In the conversion from Jaguar, a Cray XT5, ORNL essentially gutted the existing 200 cabinets and retrofitted them with nearly ten thousand XK7 blades. Each blade houses two nodes and each one of them holds a 16-core Opteron 6274 CPU and a Tesla K20 GPU module. The x86 Opteron chips run at a respectable 2.2 GHz, while the K20 hums along at a more leisurely 732 MHz. But because to the highly parallel nature of the GPU architecture, the K20 delivers around 10 times the FLOPS as its CPU companion. (Using the 27 peak PF value for Titan, a back-of-the-envelope calculation puts the new K20 at about 1.2-1.3 double precision teraflops.)

Thanks to the energy efficiency of the K20, which NVIDIA claims is going to three times as efficient its previous-generation Fermi GPU, Titan draws a mere 12.7 MW to power the whole system. That’s especially impressive when you consider that the x86-only Jaguar required 7 megawatts for a mere tenth of the FLOPS.

It would appear, though, that IBM’s Blue Gene/Q may retain the crown for energy-efficient supercomputing. The Sequoia system at Lawrence Livermore Lab draws just 7.9 MW to power its 20 peak petaflops. However, it’s a little bit of apples and oranges here. That 7.9 MW is actually the power draw for Sequoia’s Linpack run, which topped out at 16 petaflops. Since we don’t have the Linpack results for Titan just yet, it’s hard to tell if the GPU super will be able to come out ahead of Blue Gene/Q platform.

For multi-petaflopper, Titan is a little shy on memory capacity, claiming just 710 terabytes – 598 TB on the CPU side and 112 TB for the GPUs. The FLOPS-similar Sequoia has more than twice that – nearly 1.6 petabytes. Back in the day, the goal for balanced supercomputing was at least one byte of memory for every FLOP, but that era is long gone.

Titan provides around 1/40 of a byte per FLOP and from the GPU’s point of view, most of the memory on the wrong side of the PCIe bus – that is, next to the CPU. Welcome to the new normal.

Titan is more generous with disk space though, 13.6 PB in all, although again, a good deal less than that of its Sequoia cousin at 55 PB. Apparently disk storage is being managed by 192 Dell I/O servers, which, in aggregate, provide 240 GB/second of bandwidth to the storage arrays.
Titan’s big claim to fame is that it’s the first GPU-accelerated supercomputer in the world that’s has been scaled into the multi-petaflop realm. IBM’s Blue Gene/Q and Fujitsu’s K computer — both powered by custom CPU SoCs — are the only other platforms that have broken the 10-petaflop mark. Titan is also the first GPU-equipped machine of any size in the US. As such, it will provide a test platform for a lot of big science codes that have yet to take advantage of accelerators at scale.

Acceptance testing is already underway at Oak Ridge and users are in the process of porting and testing a variety of DOE-type science applications to the CPU-GPU supercomputer. These include codes in climate modeling (CAM-SE), biofuels (LAMMPS), astrophysics (NRDF), combustion (S3D), material science (WL-LSMS), and nuclear energy (Denovo).

According to Markus Eisenbach, his team has already been able to run the WL-LSMS code above the 10-petaflop mark on Titan. He says that level of performance will allow them to study the behavior of materials at temperatures above the point where they lose their magnetic properties.

At the National Center for Atmospheric Research (NCAR), they are already using the new system to speed atmospheric modeling codes. With Titan, Warren Washington’s NCAR team has been able to execute high-resolution models representing one to five years of simulations in just one computing day. On Jaguar, a computing day yielded only three months worth of simulations.

ORNL’s Tom Evans is using Titan cycles to model nuclear energy production. The simulations are for the purpose of improving the safety and performance of the reactors, while reducing the amount of waste. According to Evans, they’ve been able to run 3D simulations of a nuclear reactor core in hours, rather than weeks.

The machine will figure prominently into the upcoming INCITE awards. INCITE, which stands for Innovative and Novel Computation Impact on Theory of Experiment, is the DOE’s way of sharing with  the FLOPS with scientists and industrial users on the agency’s fastest machines. The program only accepts proposals for end users with “grand challenge”-type problems worthy of top tier supercomputing.

With its 20-plus-petaflop credentials, Titan will be far and away the most powerful system available for open science. (Sequoia belongs to the NNSA and spends most its cycles on classified nuclear weapons codes.) The DOE has received a record number of proposals for the machine, representing three times what Titan will be able to donate to the INCITE program.

Undoubtedly some of that pent-up demand is a result of the delayed entry of the US into GPU-accelerated supers. Over the past three years, American scientists and engineers have watched heterogeneous petascale systems being built overseas. China (with Tianhe-1A, Nebulae, and Mole 8.5), Japan (with TSUBAME 2.0), and even Russia (with Lomonosov) all managed to deploy ahead of the US.

Some of that is due to the slow uptake of GPU computing by IBM and Cray, the US government’s two largest providers of top tier HPC machinery. IBM offers GPU-accelerated gear on it x86 cluster offerings, but its flagship supercomputers are based on their in-house Blue Gene and Power franchises. Cray waited until May 2011 to deliver its first GPU-CPU platform, the XK6 (with Fermi Tesla GPUs), preferring to skip the earlier renditions of NVIDIA technology.

While Titan could be viewed as just another big supercomputer, there is a lot on the line here, especially for NVIDIA. If the system can be a productive petascale machine, it will go a long way toward establishing the company’s GPU computing architecture as the go-to accelerator technology for the path to exascale. The development that makes this less than assured is the imminent emergence of Intel’s Xeon Phi manycore coprocessor, and to a lesser extent, AMD’s future GPU and APU platforms.

Intel will get its initial chance to prove Xeon Phi’s worth as an HPC accelerator with Stampede, a 10 petaflop supercomputer that will be installed at the Texas Advanced Computing Center (TACC) before the end of the year. That Dell cluster will have 8 of those 10 petaflops delivered by Xeon Phi silicon and, as such, the system will represent the first big test case for Intel’s version of accelerated supercomputing.

It also represents the first credible challenge to NVIDIA on this front since the GPU-maker got into the HPC business in 2006. Whichever company is more successful at delivering HPC on a chip, the big winners will be the users themselves, who will soon have two vendors offering accelerator cards with over a teraflop of double precision performance. At a few thousand dollars per teraflop, supercomputing has never been so accessible.

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!

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research Read more…

By John Russell

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

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 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm 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

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

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

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

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

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

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

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

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 the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

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

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

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

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 Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

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

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

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

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