The Week in Review

By Tiffany Trader

August 26, 2010

Here is a collection of highlights from this week’s news stream as reported by HPCwire.

Numerical Algorithms Group Releases Fortran Builder 5.2

Spintronics Breakthrough Holds Promise for Next-Generation Computers

Bull Supercomputer Starts Work at Atomic Weapons Establishment

NICS to Add More Than 300 Teraflops to the NSF’s Computing Capacity

Marshall University to Receive More than $525,000 to Enable Internet2 Access

AMD Announces Two New x86 Core Designs

LSU Research Group Part of DARPA Project to Create Advanced Computing Systems

Bielefeld University Selects ScaleMP for Molecular Physics Research

Platform Computing Introduces Low Cost Starter Pack for Private Cloud

AMD Appoints Donald Newell as Server Chief Technology Officer

NVIDIA Names Georgia Institute of Technology a CUDA Center of Excellence

Michael L. Norman Named Director of the San Diego Supercomputer Center

EM Photonics, U of Delaware Team to Develop Advanced Algorithms for Air Force

Shodor Attends TeraGrid ’10, Wins Award

Supercomputing Software Ported to Windows in Belarus

Supercomputing, anytime, anywhere

Researchers from the Massachusetts Institute of Technology (MIT) and the Texas Advanced Computing Center (TACC) have come up with a pared down version of a supercomputing application for the Android smart phone. The team used TACC’s Ranger supercomputer to analyze large, time-consuming simulations and create a “reduced model” that would work on a handheld device, specifically the Google Android smart phone. With the reduced model, the application becomes portable and can be used to generate real-time results. Basically any computation done on the smart phone can be thought of as a continuation of the work done on the supercomputer.

The release poses the question: “What if you could perform supercomputing calculations in real-time, on your smartphone, in any location?” An attention grabber, for sure. Let’s just say that while an inexpensive handheld device will never compete with a multi-million dollar massive supercomputer on every level, with the right preparation and custom-crafted algorithms, results can indeed be comparable.

“Once you’ve created the reduced model, you can do all the computations on a phone,” said David Knezevic, a post-doctoral associate in mechanical engineering at MIT, working in the lab of Prof. Anthony Patera.

Reduced models are not new, but the MIT/TACC team improved the error bounds, leading to more accurate and reliable results.

Knezevic, again: “We have a bound on how much accuracy we’re losing with our reduced model, so we can say with rigor that we’re doing supercomputing on a phone.”

Here’s more from the release:

The reduced model is constructed by focusing the supercomputer simulations on a range of parameters that are of interest to the user. Once the construction is finished, the model can be used to perform simulations for new parameters, nearly instantaneously, for use in real-world applications.

….

Using the smart phone application, researchers can change values, improve the error bounds by increasing the complexity of the local calculation, and even visualize the solution interactively in three dimensions.

Being able to run supercomputing-type apps on small, low-power devices like the smart phone is perfect for real-time field work. Possible uses being considered are landmine detection and other types of applied science and engineering, such as building design. The algorithm can also be used with linked devices acting as sensors that provide the data to the reduced order model. This could lead to apps such as real-time traffic reports, or “live” control systems for automobiles and aircraft for improved safety and performance. For more analysis on using a cluster of smart phones for real-time intelligent applications, check out this piece from Douglas Eadline.

Knezevic summed up the potential of the project thusly: “When you tell people you can solve a problem that would normally take two hours on Ranger in one second, with guaranteed error bounds, people instantly understand what model reduction is all about.”

3PAR bidding war continues

Last week, Week in Review covered Dell’s offer to buy 3PAR at $18 per share for a total of $1.15 billion cash. Exactly one week later, HP announced its counter offer: $24.00 per share, or $1.5 billion in cash.

Well, this morning it seemed that the bidding war was most likely over. 3PAR announced that it was accepting Dell’s counter-counter-offer for $24.30 in cash per share, or almost $1.52 billion. The contract included a $72 million termination fee, sort of the reverse of a prenup, where 3PAR would have to pay Dell if it accepts any another offer.

Late in the day Thursday, HP showed that it is not ready to back down from this three-way tug of war, boosting its offer to $27 per share, about 1.69 billion. An amount that is over market price for the stock.

As of press time, Dell hasn’t presented a counter-offer, but according to the original Dell/3PAR announcement, Dell has perpetual matching rights, meaning it has the right to match any counter-offer within three days.

The stock, by the way, is still trading above $26, more than double its price from before the takeover battle broke out.

The offer war between the three parties just proves how badly both Dell and HP want to be to fortify their storage offerings with 3PAR’s technology.

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Enlisting Deep Learning in the War on Cancer

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Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines. Read more…

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DDN Enables 50TB/Day Trans-Pacific Data Transfer for Yahoo Japan

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Infographic Highlights Career of Admiral Grace Murray Hopper

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Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

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AWS Embraces FPGAs, ‘Elastic’ GPUs

December 2, 2016

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AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. Read more…

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Weekly Twitter Roundup (Dec. 1, 2016)

December 1, 2016

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

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HPC Career Notes (Dec. 2016)

December 1, 2016

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high performance computing community. Read more…

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Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines. Read more…

By John Russell

Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

By John Russell

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

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By Jan Rowell

Seagate-led SAGE Project Delivers Update on Exascale Goals

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. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

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HPE-SGI to Tackle Exascale and Enterprise Targets

November 22, 2016

At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (Hanna), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC. Read more…

By John Russell

Intel Details AI Hardware Strategy for Post-GPU Age

November 21, 2016

Last week at SC16, Intel revealed its product roadmap for embedding its processors with key capabilities and attributes needed to take artificial intelligence (AI) to the next level. Read more…

By Alex Woodie

Why 2016 Is the Most Important Year in HPC in Over Two Decades

August 23, 2016

In 1994, two NASA employees connected 16 commodity workstations together using a standard Ethernet LAN and installed open-source message passing software that allowed their number-crunching scientific application to run on the whole “cluster” of machines as if it were a single entity. Read more…

By Vincent Natoli, Stone Ridge Technology

IBM Advances Against x86 with Power9

August 30, 2016

After offering OpenPower Summit attendees a limited preview in April, IBM is unveiling further details of its next-gen CPU, Power9, which the tech mainstay is counting on to regain market share ceded to rival Intel. Read more…

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AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

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Think Fast – Is Neuromorphic Computing Set to Leap Forward?

August 15, 2016

Steadily advancing neuromorphic computing technology has created high expectations for this fundamentally different approach to computing. Read more…

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The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

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HPE Gobbles SGI for Larger Slice of $11B HPC Pie

August 11, 2016

Hewlett Packard Enterprise (HPE) announced today that it will acquire rival HPC server maker SGI for $7.75 per share, or about $275 million, inclusive of cash and debt. The deal ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. Bringing SGI into its fold bolsters HPE's high-performance computing and data analytics capabilities and expands its position... Read more…

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ARM Unveils Scalable Vector Extension for HPC at Hot Chips

August 22, 2016

ARM and Fujitsu today announced a scalable vector extension (SVE) to the ARMv8-A architecture intended to enhance ARM capabilities in HPC workloads. Fujitsu is the lead silicon partner in the effort (so far) and will use ARM with SVE technology in its post K computer, Japan’s next flagship supercomputer planned for the 2020 timeframe. This is an important incremental step for ARM, which seeks to push more aggressively into mainstream and HPC server markets. Read more…

By John Russell

IBM Debuts Power8 Chip with NVLink and Three New Systems

September 8, 2016

Not long after revealing more details about its next-gen Power9 chip due in 2017, IBM today rolled out three new Power8-based Linux servers and a new version of its Power8 chip featuring Nvidia’s NVLink interconnect. Read more…

By John Russell

Leading Solution Providers

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

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US, China Vie for Supercomputing Supremacy

November 14, 2016

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Intel Launches Silicon Photonics Chip, Previews Next-Gen Phi for AI

August 18, 2016

At the Intel Developer Forum, held in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. Read more…

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CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

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Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

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Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

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Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Micron, Intel Prepare to Launch 3D XPoint Memory

August 16, 2016

Micron Technology used last week’s Flash Memory Summit to roll out its new line of 3D XPoint memory technology jointly developed with Intel while demonstrating the technology in solid-state drives. Micron claimed its Quantx line delivers PCI Express (PCIe) SSD performance with read latencies at less than 10 microseconds and writes at less than 20 microseconds. Read more…

By George Leopold

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