Livermore Computing, Reddit Asked Them Anything

By Tiffany Trader

August 10, 2017

In case you missed it, the staff of Livermore Computing (LC) at the Lawrence Livermore National Laboratory (LLNL) recently fielded some questions from the internet, part of Reddit’s Science Ask Me Anything (AMA) series. Livermore is home to Sequoia, currently the fifth fastest machine in the world, benchmarked at 17 Linpack petaflops. The IBM BlueGene/Q machine enables the National Nuclear Security Administration (NNSA) to fulfill its stockpile stewardship mission through simulation in lieu of underground testing.

This fall Livermore expects to take delivery of Sierra, the pre-exascale supercomputer that is part of the tri-lab CORAL collaboration. Built by IBM and Nvidia, Sequoia will offer about six times more computing power than Sequoia with a planned peak computational capacity of ~150 petaflops. Like Sequoia, Sierra will serve the NNSA’s program to ensure the safety, security, and effectiveness of the nation’s nuclear deterrent without testing.

Below we round up a few highlights from the AMA — so read on to find out what Livermore Computing representatives think about AI, how they are preparing for the coming Sierra system and the benefits of being in wine country. Plus, they answer the all-important question: how much money could Sequoia make mining bitcoins (hypothetically)?

Blankrubber:

What is one thing about your work that the general public pushes back against and what one thing would you like them to understand?

Livermore_Computing:

Sometimes people ask us why we need ever more powerful supercomputers. No matter how much computing power we have provided, our national missions create a need for more complex simulations and thus a need for more powerful supercomputers.

Find more information on the nation’s exascale computing project see https://exascaleproject.org/. To see examples of application areas requiring exascale computing, check out the ECP Research Areas.

MagicianDolphin:

Do the future prospects of Artificial Intelligence ring a positive or negative tune, and why?

Livermore_Computing:

It depends on how AI is used and the intentions of the user. With the increasing complexity of programs (multiple millions of lines of code), we might reach a point where further development of some applications require help from an AI agent. One useful area to explore is monitoring the condition of supercomputers and predicting failures. We will always need more intelligence, both human and artificial. Several LLNL projects are investigating AI use in HPC. One effort leverages the TrueNorth brain-inspired supercomputer, you can read more at https://www.llnl.gov/news/lawrence-livermore-and-ibm-collaborate-build-new-brain-inspired-supercomputer.

Magic147:

Is alcohol bad for computing? Or does it hurt your Livermore?

Livermore_Computing:

Alcohol is bad for computers, especially the non-liquid cooled ones. Livermore is known for its wine. Alcohol is good for computer scientists. See the following for reference: https://xkcd.com/323/

tupe12:

What’s the silliest things that can be done on supercomputers?

Livermore_Computing:

We often need to test the heat tolerance of supercomputers, so one of our engineers was asked to write a computation to generate heat. Not do otherwise productive work, just get the computer as hot as we possibly could.

mr_trumpandhillary:

This is a kinda dumb question but how do super computers work

Livermore_Computing:

No such thing as a dumb question, only an opportunity to learn! Supercomputers or HPC, work by distributing a task across numerous computers. They benefit by high speed communications between these computers working on the parts of the task. They also leverage message passing between the processes on the various computers to coordinate working on the overall task. This high speed, coordinated, distributed computation across numerous computers is what makes a supercomputer work.

itisjustjeff:

How excited are you for Sierra?

Livermore_Computing:

We are extremely excited for Sierra!

We have people developing tools, programming models, porting millions of lines of code, and just generally trying new and interesting things. A lot of them open source. We have sysadmins getting familiar with new hardware, our networking folks are looking at how to make networks keep up with the incredible computational speedup. We also have several teams focused on helping applications developers prepare for running efficiently on Sierra.

Power efficiency is extremely important. We are proud to be active contributors, users, and supporters of the open source community. For info on our software go to https://software.llnl.gov/.

mindl0rd:

How do you start working in your field? What degrees or certifications are you looking for in perspective candidates?

Livermore_Computing:

Well… we have a geologist who is a Linux kernel hacker!

Seriously, we have a range of degrees represented across Livermore Computing. Some of us have no degree at all. We have employees with associates degrees, bachelors, masters, and PhD’s. Some are straight out of school, others have been in HPC since before the internet existed.

Fields range from Computer Science and Computer Engineering, to Mathematics and Statistics degrees. We also staff who come from the sciences directly, including those from physical and life sciences such as Computational Biology, Physics, and others!

Look for employment opportunities here: http://careers-ext.llnl.gov/ Apply for an internship: http://students.llnl.gov/ Here’s the page for this summer’s HPC Cluster Engineer Academy:
https://computation.llnl.gov/hpc-cluster-engineer-academy

Bull3t_Th3ory:

This is where my uncle works! Say hi to Greg T for me!!!

Now for my question: -Given the rise of cryptocurrency and it’s dependence on computing power to solve blocks and earn currency. Are there any plans to use existing supercomputing power to mine for cryptocurrency?

Livermore_Computing:

Greg says hi!

DOE supercomputers are government resources for national missions. Bitcoin mining would be a misuse of government funds.

In general, though, it’s fun to think about how you could use lots of supercomputing power for Bitcoin mining, but even our machines aren’t big enough to break the system. The number of machines mining bitcoin worldwide has been estimated to have a hash rate many thousands of times faster than all the Top 500 machines combined, so we wouldn’t be able to decide to break the blockchain by ourselves (https://www.forbes.com/sites/peterdetwiler/2016/07/21/mining-bitcoins-is-a-surprisingly-energy-intensive-endeavor/2/#6f0cae8a30f3). Also, mining bitcoins requires a lot of power, and it’s been estimated that even if you used our Sequoia system to mine bitcoin, you’d only make $40/day (https://bitcoinmagazine.com/articles/government-bans-professor-mining-bitcoin-supercomputer-1402002877/). The amount we pay every day to power the machine is a lot more than that. So even if it were legal to mine bitcoins with DOE supercomputers, there’d be no point. The most successful machines for mining bitcoins use low-power custom ASICs built specifically for hashing, and they’ll be more cost-effective than a general purpose CPU or GPU system any day.

nbo10:

Do you have to get every response reviewed and approved before you post it?

Livermore_Computing:

This answer is currently under review… Yes.

~

Read the entire AMA here.

The Livermore Computing group hopes to do another AMA in the future, so start thinking of your questions.

Livermore Computing staff in front of Sequoia
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 Researchers Test AI Traffic Monitoring Tool in Austin

December 13, 2017

Traffic jams and mishaps are often painful and sometimes dangerous facts of life. At this week’s IEEE International Conference on Big Data being held in Boston, researchers from TACC and colleagues will present a new Read more…

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in what has become an overwhelmingly two-socket landscape in the d Read more…

By John Russell

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of quantum. This week, Microsoft took the next step in advanci Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Explore the Origins of Space with COSMOS and Memory-Driven Computing

From the formation of black holes to the origins of space, data is the key to unlocking the secrets of the early universe. Read more…

ESnet Now Moving More Than 1 Petabyte/wk

December 12, 2017

Optimizing ESnet (Energy Sciences Network), the world's fastest network for science, is an ongoing process. Recently a two-year collaboration by ESnet users – the Petascale DTN Project – achieved its ambitious goal t Read more…

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of Read more…

By Tiffany Trader

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be care Read more…

By Alex R. Larzelere

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Microsoft Spins Cycle Computing into Core Azure Product

December 5, 2017

Last August, cloud giant Microsoft acquired HPC cloud orchestration pioneer Cycle Computing. Since then the focus has been on integrating Cycle’s organization Read more…

By John Russell

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

HPE In-Memory Platform Comes to COSMOS

November 30, 2017

Hewlett Packard Enterprise is on a mission to accelerate space research. In August, it sent the first commercial-off-the-shelf HPC system into space for testing Read more…

By Tiffany Trader

SC17 Cluster Competition: Who Won and Why? Results Analyzed and Over-Analyzed

November 28, 2017

Everyone by now knows that Nanyang Technological University of Singapore (NTU) took home the highest LINPACK Award and the Overall Championship from the recently concluded SC17 Student Cluster Competition. We also already know how the teams did in the Highest LINPACK and Highest HPCG competitions, with Nanyang grabbing bragging rights for both benchmarks. Read more…

By Dan Olds

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

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

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he 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

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

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

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

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

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

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

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a 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

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released 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

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