Seagate-led SAGE Project Delivers Update on Exascale Goals

By John Russell

November 29, 2016

Roughly a year and a half after its launch, the SAGE exascale storage project led by Seagate has delivered a substantive interim report – Data Storage for Extreme Scale. It outlines technical details of progress to date and architectural plans moving forward. Of particular note is progress on co-design for use cases and applications expected to benefit most from exascale. There’s also been a fair amount of work to be able to accommodate big data and traditional HPC workflows in the same environment.

“We’ve tried to give ourselves lofty goals,” said Malcolm Muggeridge, senior engineering director at Seagate based in the U.K. who is leading the initiative. “We would like to become the platform of choice in exascale for storage solutions and will have the technology addressing that space in the 2022 timeframe. The main piece of work that has been completed [so far] is co-design activities.”

You may recall that SAGE (StorAGe for Exascale Data Centric Computing (SAGE) system aims to implement a Big Data/Extreme Computing (BDEC) and High Performance Data Analytics (HPDA) capable infrastructure suitable for Extreme scales – including Exascale and beyond. SAGE is one of 15 projects recently funded under Horizon 2020. Direct funding is actually through the European Technology Platforms (ETP) organization – “industry-led stakeholder groups recognized by the European Commission as key actors in driving innovation, knowledge transfer and European competitiveness. ETPs develop research and innovation agendas and roadmaps for action at EU and national level to be supported by both private and public funding.”

sage-seagate-architectureThe new white paper is a fairly extensive document that follows a nine-month formal project review last June and includes work completed since. Among the topics covered are: platform requirements; systems architecture; platform components; and ecosystem elements. Launched in September of 2015, SAGE tackles eight research areas: “the study of the 1) application use cases co-designing solutions to address 2) Percipient Storage Methods, 3) Advanced Object Storage, and 4) tools for I/O optimization, supporting 5) next generation storage media and developing a supporting ecosystem of 6) Extreme Data Management, 7) Programming techniques and 8) Extreme Data Analysis tools.”

According to the report, the SAGE storage system will be capable of efficiently storing and retrieving immense volumes of data at extreme scales, with the added functionality of “percipience” or the ability to accept and perform user defined computations integral to the storage system. SAGE will be built around the Mero object storage software platform and its supporting ecosystem of tools and techniques, that will work together to provide the required functionalities and scaling desired by extreme scale workflows.

One important goal is accommodating new storage technologies, such as non-volatile RAM (NVRAM). Leveraging object storage to assist ‘in-memory, closer-to-memory” computing is another. In an earlier interview Sai Narasimhamurthy, Seagate research staff engineer responsible for coordinating the technical work, told HPCwire that the stack would “have memory at the top, various NVRAM technologies in the middle, of course you have your flash technology as well as part of the stack, and then you have scratch disks and then archival disks.”

“You could have an object, or a piece of it, lying in high speed memory, a piece of it in NVRAM, and a piece of the object lying in scratch based upon the usage profile of the object,” explained Narasimhamurthy. “The view of the object is transparent to the application, it’s just I0 to an object, but on the back end you could have various types of layout which could be very interesting because you could optimize your layout for performance or for resiliency. You could do all sorts of things.”

sage-seagate-codesignClearly there are big goals for the project. Co-design is a critical early element in defining functional requirements, emphasized Muggeridge, “We have carefully selected use cases that reflect these data-centric applications. The use cases provide specific inputs that are designed to fine tune/modify the framework for the SAGE architecture.”

Muggeridge noted there is range of requirements drivers. The report calls out: inputs from the BDEC community and the US Department of Energy labs; data needs for big science, as exemplified by the Square Kilometer Array and the Human Brain Project; and Extreme scale I/O requirements drafted by the ETP; and extreme scale data needs highlighted by the HPDA community. The information was gathered mostly through workshops.

Top-level objectives have also been established and are largely familiar. One calls for the ability “to store and retrieve extreme volumes of data approaching orders of ~Exabyte for a given problem”. Another is the ability to manage workflows that include data from simulations and instruments. Not surprisingly, data IO rates, data integrity, data analytics, among other capabilities are being targeted. Indeed the first part of the project has been largely ‘definitional’ with a roll out of demonstrations planned for the next year.

Use of co-design principles to inform these objectives is a distinguishing feature of the project. SAGE has selected several use cases (applications) and spelled out in detail the parameters being measured. Use cases “cover a broad range of domains, including data from some of the world’s largest scientific experiments (including one of the world’s largest nuclear fusion facilities and one of the largest synchrotrons in Europe), aside from extremely data-centric HPC codes.” Below is a table with the uses cases selected.

sage-seagate-use-cases

So far, SAGE has gathered the first formal list of inputs from all of the specified use cases. “This phase included gathering inputs on formal I/O characterization, SAGE architecture analysis, data retention characterization and data scaling analysis, which was an analytical study of how data and I/O requirements of the use cases would scale on a future basis.”

sage-seagate-metrics

The SAGE system is built on multiple tiers of storage device hardware technology (see figure below). SAGE does not require a specific type of storage device technology, but typically it would include at least one NVRAM tier (Intel 3DxPoint technology is a strong contender at the moment), at least one flash tier and at least one disk tier. Together, these tiers are housed in standard form-factor enclosures and provide their own compute capability, enabled by standard x86 embedded processing components. Moving up the system stack, compute capability increases for faster, lower latency devices.

Mero, the object storage software first developed by Xyratex and now being extended by Seagate, is layered on top of this hardware stack, providing fundamental management of object I/O and storage across tiers. Essentially, Mero forms the core of the SAGE system. Mero is presented to users through the Clovis API. Everything above Clovis forms the SAGE ecosystem components.

sage-seagate-system-stack

Much remains to be done but it seems as if SAGE is making steady progress. Demonstrations, some at the Julich Supercomputing Centre, are expected over the next year or so. This latest paper is best read in full for current technical details of SAGE plans.

Link to new SAGE paper (Data Storage for Extreme Scale): http://sagestorage.eu/sites/default/files/Sage%20White%20Paper%20v1.0.pdf

 

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!

Geospatial Data Research Leverages GPUs

August 17, 2017

MapD Technologies, the GPU-accelerated database specialist, said it is working with university researchers on leveraging graphics processors to advance geospatial analytics. The San Francisco-based company is collabor Read more…

By George Leopold

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 Centers (IPCCs) has resulted in a new Big Data Center (BDC) that Read more…

By Linda Barney

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 week the cloud giant released deeplearn.js as part of that in Read more…

By John Russell

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Spoiler Alert: Glimpse Next Week’s Solar Eclipse Via Simulation from TACC, SDSC, and NASA

August 17, 2017

Can’t wait to see next week’s solar eclipse? You can at least catch glimpses of what scientists expect it will look like. A team from Predictive Science Inc. (PSI), based in San Diego, working with Stampede2 at the Read more…

By John Russell

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

IBM Raises the Bar for Distributed Deep Learning

August 8, 2017

IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scal Read more…

By Tiffany Trader

IBM Storage Breakthrough Paves Way for 330TB Tape Cartridges

August 3, 2017

IBM announced yesterday a new record for magnetic tape storage that it says will keep tape storage density on a Moore's law-like path far into the next decade. Read more…

By Tiffany Trader

AMD Stuffs a Petaflops of Machine Intelligence into 20-Node Rack

August 1, 2017

With its Radeon “Vega” Instinct datacenter GPUs and EPYC “Naples” server chips entering the market this summer, AMD has positioned itself for a two-head Read more…

By Tiffany Trader

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

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

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

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

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

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

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

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

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

Leading Solution Providers

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

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

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

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

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

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

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