3D Visualization for Oil and Gas Evolves

By Michael M. Heck

October 20, 2006

Technical Director, Visualization Sciences Group,
Mercury Computer Systems, Inc.

3D visualization has been the key to increased success and efficiency in many areas of exploration and production (E&P). In this industry visualization plays a critical role in gaining insight from data. But often when we discuss visualization, we are talking only about the actual rendering of images on the screen. In fact, the visualization challenge for E&P is characterized by computationally expensive algorithms, a very large number of diverse data sets, and a need for greater interactivity and collaboration. To meet this challenge, we must make data management, computation and rendering work together smoothly and efficiently. In this way we will continue to deliver on 3D visualization’s promise of enabling better decisions in less time.

In the past, the E&P industry has been characterized by its use of big machines for both computation and rendering. As the economics of the “PC” architecture overtook big machines, it seemed that the capabilities of a single machine would never be sufficient. The industry turned to clusters of PCs as a solution. Clusters have been widely adopted for purely computational tasks, but only to a limited extent for visualization. Clusters have significant value for visualization, but also introduce significant complexity and cost in administration compared to single machines. Today, with advances in data management, computing and rendering, the “single machine” is once again a viable platform for visualization of E&P data.

Data management

Multi-resolution bricked volume data.Seismic volumes are typically tens of gigabytes today, and hundreds of gigabytes are not uncommon. Sixty-four-bit operating systems have enabled much larger system memory, but both system memory and texture memory, on the graphics processing unit, remain scarce resources compared to the size of the data sets. An effective solution using hierarchical multi-resolution bricking is now available in visualization toolkits — middleware. In this solution, a pre-processing step subdivides the volume data into “bricks” and computes multiple resolution levels. The full-resolution data is the lowest level of the hierarchy and each higher-level brick represents multiple bricks at the level below. With data in this form, the middleware can initially load the lower-resolution data then automatically refine the image as higher-resolution data is loaded in the background. This enables interactive navigation of the largest volumes even on relatively low-end machines. The user does not have to wait for all the data to be loaded, only the data actually needed is loaded and multiple users can access the same data simultaneously because they use only their own local system memory to load the data. The multi-resolution bricking technique is already used in many E&P applications. VolumeViz from Mercury Computer Systems is one example of this visualization middleware. This same technique can be extended to other large data sets such as horizon surfaces and reservoir models.

Computing

For many years applications enjoyed an automatic increase in performance as CPU vendors competed to increase the clock speed in each new generation of chips. Physical limitations such as power consumption and heat dissipation have largely ended this era. The CPU vendors are now competing to increase the number of “cores” in each new generation of chips. Dual-core chips are already common, with quad — and higher — core chips coming soon. To take advantage of this new performance curve software developers will need to embrace multi-threading.

At the same time, alternative chip architectures have become available that provide much higher floating-point performance than conventional CPU chips, but require even more unconventional programming models. The GPU chip on every 3D graphics board is programmable and has very high performance for some algorithms. Its biggest advantage is the option of combining computing and rendering on the same processor. The Cell BE processor is a next-generation heterogeneous multi-core chip now available on a PCI-Express accelerator board from Mercury Computer Systems. All of these programming models, whether multi-threading or stream computing, present tremendous challenges for software developers.

Automatic use of multiple threads in VolumeViz enables parallel computation on large volume data.Middleware libraries can solve part of this problem. For example the VolumeViz toolkit automatically creates a separate thread to manage data loading and multiple separate threads to do the actual physical I/O. In addition, VolumeViz enables the application to supply computation modules that are executed in parallel by the data threads. This capability enables the application to take advantage of multiple cores without changing the application code. VolumeViz also provides a framework for managing computing and rendering on the GPU chip. Application-defined GPU programs are downloaded and executed by VolumeViz in cooperation with its predefined GPU programs for rendering effects. Middleware libraries also provide building-block algorithms, such as fast Fourier transform (FFT) and convolution that are already highly optimized for new architectures.

 

Rendering

Rendering of 3D images is naturally a parallel-computing task. And each new generation of GPU chip has more “pipes” (parallel computing units), providing an automatic increase in rendering performance. Powerful GPUs are available even in laptop machines, making state-of-the-art rendering accessible to almost all users. The ability to program the GPU results in higher quality rendering, new rendering techniques, and new opportunities for interaction by combining computing and rendering on the GPU. Middleware libraries, such as VolumeViz, implement many of these techniques and provide a convenient framework for applications to implement their own techniques. Some relatively new rendering techniques include bump mapping, dynamic lighting, arbitrarily shaped probes — mapping seismic data onto arbitrary geometry — and co-blending of multiple data sets. Combining computing and rendering in the GPU enables techniques including volume clipping (e.g., against horizon surfaces), volume masking (using values of one volume to mask another volume), and volume warping (e.g., horizon flattening).

Combining multiple data sets (co-blending) on the GPU.

Summary

3D visualization will continue to be a critical part of addressing today’s challenges in exploration and production. To be effective and successful, 3D visualization must integrate solutions for data management, computing and rendering. Today, visualizing large E&P data sets no longer requires a supercomputer or even a super cluster. Advances in both hardware and software are coming together to enable larger data sets, more automated analysis, and more effective presentation of the data on single workstations. Taking advantage of these advances will be challenging for software developers and will require some re-thinking of application architectures and user interfaces. However innovative “middleware” solutions can solve some of these problems and provide a framework for a complete solution.

—–

Michael M. Heck is technical director of the Visualization Sciences Group (VSG) at Mercury Computer Systems, Inc. where he evangelizes the use of 3D visualization. He works with customers to understand their applications and apply visualization technology to meet current requirements, and guides the development of visualization technology to meet future requirements. Mr. Heck has been involved in implementing, managing, teaching and applying 3D visualization software for 20+ years. During that time he has been a speaker at conferences including SEG and the World Oil Visualization Showcase, has been an invited instructor for the SIGGRAPH conference courses, and he has authored technical articles on visualization for publications including Communications of the ACM and the American Oil & Gas Reporter.

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!

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 2017 Wraps Up in Barcelona

May 18, 2017

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 loo Read more…

By Kim McMahon

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

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…

NSF, IARPA, and SRC Push into “Semiconductor Synthetic Biology” Computing

May 18, 2017

Research into how biological systems might be fashioned into computational technology has a long history with various DNA-based computing approaches explored. N Read more…

By John Russell

DOE’s HPC4Mfg Leads to Paper Manufacturing Improvement

May 17, 2017

Papermaking ranks third behind only petroleum refining and chemical production in terms of energy consumption. Recently, simulations made possible by the U.S. D Read more…

By John Russell

PRACEdays 2017: The start of a beautiful week in Barcelona

May 17, 2017

Touching down in Barcelona on Saturday afternoon, it was warm, sunny, and oh so Spanish. I was greeted at my hotel with a glass of Cava to sip and treated to a Read more…

By Kim McMahon

NSF Issues $60M RFP for “Towards a Leadership-Class” System

May 16, 2017

In case you missed it, the National Science Foundation issued the request for proposals (RFP) for the next ‘Towards a Leadership-Class Computing Facility – Read more…

By John Russell

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

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Bright Computing 8.0 Adds Azure, Expands Machine Learning Support

May 9, 2017

Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The Read more…

By John Russell

Microsoft Azure Will Debut Pascal GPU Instances This Year

May 8, 2017

As Nvidia's GPU Technology Conference gets underway in San Jose, Calif., Microsoft today revealed plans to add Pascal-generation GPU horsepower to its Azure clo Read more…

By Tiffany Trader

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

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

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

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

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

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

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

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

Leading Solution Providers

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

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

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn't made the task of parallel progr 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

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

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