Storage at Exascale: Some Thoughts from Panasas CTO Garth Gibson

By Nicole Hemsoth

May 25, 2011

Exascale computing is not just about FLOPS. It will also require a new breed of external storage capable of feeding these exaflop beasts. Panasas co-founder and chief technology officer Garth Gibson has some ideas on how this can be accomplished and we asked him to expound on the topic in some detail.

HPCwire: What kind of storage performance will need to be delivered for exascale computing?

Garth Gibson: The top requirement for storage in an exascale supercomputer is the capability to store a checkpoint in approximately 15 minutes or less so as to keep the supercomputer busy with computational tasks most of the time. If you do a checkpoint in 15 minutes, your compute period can be as little as two and a half hours and you still spend only 10 percent of your time checkpointing. The size of the checkpoint data is determined by the memory sizing; something that some experts expect will be approximately 64 petabytes based on the power and capital costs involved. Based on that memory size, we estimate the storage system must be capable of writing at 70 terabytes per second to support a 15 minute checkpoint.

HPCwire: Given the slower performance slope of disk compared to compute, what types of hardware technologies and storage tiering will be required to provide such performance?

Gibson: While we have seen peak rates of throughput on the hundreds of gigabytes per second range today, we have to scale 1000x to get to the required write speed for exascale compute. The challenge with the 70 terabyte-per-second write requirement is that traditional disk drives will not get significantly faster over the coming decade so it will require almost 1000x the number of spindles to sustain this level of write capability.

After all, we can only write as fast as the sum of the individual disk drives. We can look at other technologies like flash storage — such as SSDs — with faster write capabilities. The challenge with this technology, however, is the huge cost delta between flash-based solutions compared to ones based on traditional hard drives. Given that the scratch space will likely be at least 10 times the size of main memory, we are looking at 640 petabytes of scratch storage which translates to over half a billion dollars of cost in flash based storage alone.

The solution is a hybrid approach where the data is initially copied to flash at 70 terabytes per second but the second layer gets 10 times as much time to write from flash to disk, lowering storage bandwidth requirements to 7 terabytes per second, and storage components to only about 100x today’s systems. You get the performance out of flash and the capacity out of spinning disk. In essence, the flash layer is really temporary “cheap memory,” possibly not part of the storage system at all, with little of no use of its non-volatility, and perhaps not using a disk interface like SATA.

HPCwire: What types of software technologies will have to be developed?

Gibson: If we solve the performance/capacity/cost issue with a hybrid model using flash as a temporary memory dump before data is written off to disk, it will require a significant amount of intelligent copy and tiering software to manage the data movement between main memory and the temporary flash memory and from there on to spinning disks. It is not even clear what layers of the application, runtime system, operating system or file system manage this flash memory.

Perhaps more challenging, there will have to be a significant amount of software investment in building reliability into the system. An exascale storage system is going to have two orders of magnitude more components than current systems. With a lot more components comes a significantly higher rate of component failure. This means more RAID reconstructions needing to rebuild bigger drives and more media failures during these reconstructions.

Exascale storage will need higher tolerance for failure as well as the capability for much faster reconstruction, such as is provided by Panasas’ parallel reconstruction, in addition to improved defense against media failures, such as is provided by Panasas’ vertical parity. And more importantly, end to end data integrity checking of stored data, data in transit, data in caches, data pushed through servers and data received at compute nodes, because there is just so much data flowing that detection of the inevitable flipped bit is going to be key. The storage industry is started on this type of high reliability feature development, but exascale computing will need exascale mechanisms years before the broader engineering marketplaces will require it.

HPCwire: How will metadata management need to evolve?

Gibson: At Carnegie Mellon University we have already seen with tests run at Oak Ridge National Laboratory that it doesn’t take a very big configuration before it starts to take thousands of seconds to open all the files, end-to-end. As you scale up the supercomputer size, the increased processor count puts tremendous pressure on your available metadata server concurrency and throughput. Frankly, this is one of the key pressure points we have right now – just simply creating, opening and deleting files can really eat into your available compute cycles. This is the base problem with metadata management.

Exascale is going to mean 100,000 to 250,000 nodes or more. With hundreds to thousands of cores per node and many threads per core — GPUs in the extreme — the number of concurrent threads in exascale computing can easily be estimated in the billions. With this level of concurrent activity, a highly distributed, scalable metadata architecture is a must, with dramatically superior performance over what any vendor offers today. While we at Panasas believe we are in a relatively good starting position, it will nevertheless require a very significant software investment to adequately address this challenge.

HPCwire: Do you believe there is a reasonable roadmap to achieve all this? Do you think the proper investments are being made?

Gibson: I believe that there is a well reasoned and understood roadmap to get from petascale to exascale. However it will take a lot more investment than is currently being put into getting to the roadmap goals. The challenge is the return on investment for vendors. When you consider that the work will take most of the time running up to 2018, when the first exascale systems will be needed, and that there will barely be more than 500 publicly known petascale computers at that time, based on TOP500.org’s historical 7-year lag on the scale of the 500th largest computer.

It is going to be hard to pay for systems development on that scale now, knowing that there is going to be only a few implementations to apportion the cost against this decade and that it will take most of the decade after that for the exascale installed base to grow to 500. We know that exascale features are a viable program at a time far enough down the line to spread the investment cost across many commercial customers such as those in the commercial sector doing work like oil exploration or design modeling.

However, in the mean time, funding a development project like exascale storage systems could sink a small company and it would be highly unattractive on the P&L of a publicly traded company. What made petascale storage systems such as Panasas and Lustre a reality was the investment that the government made with DARPA in the 1990’s and with the DOE Path Forward program this past decade. Similar programs are going to be required to make exascale a reality. The government needs to share in this investment if it wants production quality solutions to be available in the target exascale timeframe.

HPCwire: What do you think is the biggest hurdle for exascale storage?

Gibson: The principal challenge for this type of scale will be the software capability. Software that can manage these levels of concurrency, streaming at such high levels of bandwidth without bottlenecking on metadata throughput, and at the same time ensure high levels of reliability, availability, integrity, and ease-of-use, and in a package that is affordable to operate and maintain is going to require a high level of coordination and cannot come from stringing together a bunch of open-source modules. Simply getting the data path capable of going fast by hooking it together with bailing wire and duct tape is possible but it gives you a false confidence because the capital costs look good and there is a piece of software that runs for awhile and appears to do the right thing.

But in fact, having a piece of software that maintains high availability, doesn’t lose data, and has high integrity and a manageable cost of operation is way harder than many people give it credit for being. You can see this tension today in the Lustre open source file system which seems to require a non-trivial, dedicated staff trained to keep the system up and effective.

HPCwire: Will there be a new parallel file system for exascale?

Gibson: The probability of starting from scratch today and building a brand new production file system deployable in time for 2018 is just about zero. There is a huge investment in software technology required to get to exascale and we cannot get there without significant further investment in the parallel file systems available today. So if we want to hit the timeline for exascale, it is going to have to take investment in new ideas and existing implementations to hit the exascale target.

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

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 advanced supercomputers. Read more…

By Tiffany Trader

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 phase of neural networks (NN). 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. Read more…

By John Russell

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 campaign. 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 assets. 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

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

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

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 new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

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 will be Japan’s “fastest AI supercomputer,” 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 was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

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 offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

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

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

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 network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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