PRACEdays 2017 Wraps Up in Barcelona

By Kim McMahon

May 18, 2017

Guest contributor Kim McMahon shares highlights from the final day of the PRACEdays 2017 conference in Barcelona.

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth look at the oil and gas industry’s use of HPC and a panel discussion on bridging the gap between scientific code development and exascale technology.

Henri Calandra of Total SA spoke on the challenges of increased HPC complexity and value delivery for the oil and gas industry.

The main challenge Total and other oil and gas companies are finding is that discoveries of oil deposits are becoming more rare. To stay competitive, they need to first and foremost open new frontiers for oil discovery, but do this while reducing risk and costs.

In the 1980s, seismic data was reviewed in the 2 dimensional space. The 1990’s started development of 3D seismic depth imaging. Continuing into the 2000’s, 3D depth imaging was improved as wave equations were added to the traditional imaging. The 2010’s brought more physics, more accurate images, and more complex processes to visually view the seismic data.

Henri Calandra of Total SA – click to enlarge

The industry continues to see drastic improvements. A seismic simulation that in 2010 took four weeks to run, in 2016 takes one day. Images have significantly higher resolution and the amount of detail seen in the images enables Total to be more precise in identifying seismic fields and potential hazards in drilling.

If you look closely at the pictures (shown on the slide), you can make out improvements the image quality. Although it may seem slight to our eye, the geoscientist can see the small nuances in the images that help them be more precise, identify hazards, and achieve a better positive acquisition rate.

How did this change over the last 30+ years happen? Improved technology, integrating more advanced technologies, improved processes, more physics, more complex algorithms – basically more HPC.

Using HPC, Total has been able to reduce their risks, become more precise and selective on their explorations, identify potential oil fields faster, and optimize their seismic depth imaging.

What’s next: Opening new frontiers enabled by the better appraisal of potential new opportunities. HPC has enabled seismic depth imaging methods that can do more iterations, more physics, and more complex approximations. Models are larger, there are multiple resolutions, and 4D data. There is interactive processing happening during the drilling and these multi real-time simulations allow adjustments to the drilling, thus improving the success rate of finding oil.

Developing new algorithms is a long-term process and typically last across several generations of supercomputers. Of course, the oil and gas industry is looking forward to exascale. But the future is complex — in the compute in the form of manycore, with accelerators, and heterogeneous systems. Complexity in the storage with the abundance of data and movement between tiers of storage via multiple storage technologies. Complexity in the tools such as OpenCL, CUDA, OpenMP, OpenACC, and compilers. There is a need for standardized tools to hide the hardware complexity and help the users of the HPC systems.

None of this can be addressed without HPC specialists. Application development cannot be done without a strong collaboration between the physicist, scientist, and HPC team. This constant progress will continue to improve the predictions Total relies on for finding productive oil fields.

The second session of the day was a panel moderated by Inma Martinez: titled “Bridging the gap between scientific code development and exascale technology.” Much of the focus was on the software challenges for extreme scale computing faced by the community.

The panelists:

Henri Calandra: Total

Lee Margetts: NAFEMS

Erik Lindahl: PRACE Scientific Steering Committee

Frauke Gräter: Heidelberg Institute for Theoretical Studies

Thomas Skordas, European Commission

This highly anticipated session looked at the gap between hardware, software, and application advances and the role of industry, academia and the European Commission in the development of software for HPC systems.

Thomas Skordas pointed out that driving leadership in exascale is important and it’s about much more than hardware. It’s the next generation code, training, and understanding the opportunities exascale can accomplish.

Frauke Gräter sees data as a significant challenge; the accumulation of more and more data and the analysis of that data. In the end, scientists are looking for insights and research organizations will invest in science.

Parallelizing the algorithms is the key action according to Erik Lindahl. There is too much focus on the exascale machine but algorithms need to be good to make the best use of the hardware. Exascale, expected to happen around 2020, is not expected to be a staple in commercial datacenter until 2035. There is not a supercomputer in the world that does not run open source software, and exascale machines will follow this practice.

Lee Margetts talked of “monster machines” — the large compute clusters in every datacenter. As large vendors adopt artificial intelligence and machine learning, will we see the end of the road for the large “monster” machines? We have very sophisticated algorithms and are using very sophisticated computing. What if this technology that is used in something like oil and gas were used to predict volcanoes or earthquakes — the point being, can technologies be used for more than one science?

Henri Calandra noted that data analytics and storage will become a huge issue. If we move to exascale, we’ll have to deal with thousands of compute nodes and update code for all these machines.

The biggest challenge is the software challenge.

When asked about the new science we will see, the panel had answers that fit their sphere of knowledge. Thomas spoke of brain modeling and self-driving cars. Frauke added genome assembly and new scientific disciplines such as personalized medicine. She says, “To attract young people, we need to marry machine learning and deep learning into HPC.” Erik notes that we have a revolution of data because of accelerators. Data and accelerators enabling genome resource will drive research in this area. Lee spoke of integrating machine learning into manufacturing processes.

Kim McMahon, XAND McMahon

As Lee said, “Diversity in funding through the European commission is really important – we need to fund the mavericks as well as the crazy ones.”

My takeaway is that the accomplishment of an exascale machine is not the goal that will drive the technology forward. It’s the analysis of the data. The algorithms. Parallelizing code. There will be some who will buy the exascale machine, but it will be years after it’s available before it’s broadly accepted. As Lee said, “the focus is not the machine, the algorithms or the software, but delivering on the science. Most people in HPC are domain scientists who are trying to solve a problem.”

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!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

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 cryptocurr Read more…

By Doug Black

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

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

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

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

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

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

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 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

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage 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

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 ne Read more…

By Tiffany Trader

Leading Solution Providers

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 Read more…

By Tiffany Trader

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 w 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 Read more…

By Steve Campbell

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 Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

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 advance Read more…

By Tiffany Trader

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" process Read more…

By Tiffany Trader

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