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!

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

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

UCSD Web-based Tool Tracking CA Wildfires Generates 1.5M Views

October 16, 2017

Tracking the wildfires raging in northern CA is an unpleasant but necessary part of guiding efforts to fight the fires and safely evacuate affected residents. One such tool – Firemap – is a web-based tool developed b 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…

Exascale Imperative: New Movie from HPE Makes a Compelling Case

October 13, 2017

Why is pursuing exascale computing so important? In a new video – Hewlett Packard Enterprise: Eighteen Zeros – four HPE executives, a prominent national lab HPC researcher, and HPCwire managing editor Tiffany Trader Read more…

By John Russell

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

OLCF’s 200 Petaflops Summit Machine Still Slated for 2018 Start-up

October 3, 2017

The Department of Energy’s planned 200 petaflops Summit computer, which is currently being installed at Oak Ridge Leadership Computing Facility, is on track t Read more…

By John Russell

US Exascale Program – Some Additional Clarity

September 28, 2017

The last time we left the Department of Energy’s exascale computing program in July, things were looking very positive. Both the U.S. House and Senate had pas Read more…

By Alex R. Larzelere

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

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

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

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

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

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

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Cente Read more…

By Linda Barney

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