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SHPCP @ SEG 2016
Society of High Performance Computing Professionals Focus Group and Presentation Theater at Booth #556
Geophysics has been associated with computing technology from its inception. High Performance Computing (HPC) consistently provides challenges and opportunities to enhance hardware, software and infrastructure solutions to match the performance and quality of the industry’s algorithms, data consumption and exploration advances.
The geophysical industry constantly increases the demands placed on HPC suppliers to process more data faster than ever before.
Considering the SEG’s interest in and need for HPC, the SEG and The Society of HPC Professionals (SHPCP) have brought together a group of HPC organizations to exhibit in a common area of the Exhibit Hall and make technical presentations. This theater will showcase two state-of-the-art 84” LCD panel systems provided by Prysm.
Several SHPCP (www.hpcsociety.org) are regular exhibitors at the SEG Annual Meetings; however, the theater will also include participants that are first-time SEG Exhibitors and will feature new industry technology. Sponsors of this area will deliver presentations in the theater with daily programs.
The following organizations will be participating in the HPC Focused Group: Acceleware, Altair, Avere Systems, Cray, DDN, HGST, IBM, Intel, Micron, NetApp, PCPC Direct, Penguin Computing, Prysm, Unique Digital, Seagate, VCE, and Verrex. Please refer to the Exhibit Hall Panel layout in the show program guide to find location of the HPC Theater at Booth #556.
Monday, October 17th - 9:30 AM Abstract: Lightning strikes occur worldwide. They are not random. Locations are controlled by telluric currents. The NLDN (National Lightning Detection Network) lighting database has collected 18+ years of lightning strike data in the continental United States. The GLD-360 (Global Lightning Database – 360) has collected 5+ years of lightning strike data worldwide. NLDN data is most comprehensive and includes the timing (microseconds), location (150-600 foot horizontal resolution), rise-time (microseconds), peak current (kilo-amperes), polarity (80% of strikes negative), peak-to-zero (microseconds), number of sensors recording the strike, the major and minor axis of triangulated location calculations, chi-squared (quality measurement), and several other attributes for each lightning strike in the continental U. S. Historically these data were collected for insurance, meteorology, and safety markets. These data now support resource exploration like oil & gas, mineral, aquifer, and geothermal.
Monday, October 17th - 10:30 AM FSG Information Systems
Monday, October 17th - 1:00 PM
Monday, October 17th - 2:00 PM
Monday, October 17th - 3:00 PM Abstract: Advances in big data analytics are among the promising and valuable techniques in many industrial domains. Will the big data analytics techniques change the traditional methodology of seismic data interpretation in identifying and modeling geological features? Will geophysicists benefit by combining big data analytics techniques to enhance the quality of seismic data interpretation results? Our research aims to answer these questions by creating a cloud-based scalable seismic data analytics platform with deep learning technology, and assessing its applicability in seismic data interpretation. The session will go through the architecture of the platform, present its functionality, as well as the use case of applying deep learning technology on geological faults detection. The work is built on top of Apache Spark and Google TensorFlow deep learning framework. We acknowledge the support of the National Science Foundation for this project through a number of research and innovation grant programs.
Monday, October 17th - 4:00 PM
Tuesday, October 18th - 9:30 AM Abstract: Lightning strikes occur worldwide. They are not random. Locations are controlled by telluric currents. The NLDN (National Lightning Detection Network) lighting database has collected 18+ years of lightning strike data in the continental United States. The GLD-360 (Global Lightning Database – 360) has collected 5+ years of lightning strike data worldwide. NLDN data is most comprehensive and includes the timing (microseconds), location (150-600 foot horizontal resolution), rise-time (microseconds), peak current (kilo-amperes), polarity (80% of strikes negative), peak-to-zero (microseconds), number of sensors recording the strike, the major and minor axis of triangulated location calculations, chi-squared (quality measurement), and several other attributes for each lightning strike in the continental U. S. Historically these data were collected for insurance, meteorology, and safety markets. These data now support resource exploration like oil & gas, mineral, aquifer, and geothermal.
Tuesday, October 18th - 10:30 AM
Tuesday, October 18th - 1:00 PM Tuesday, October 18th - 2:00 PM Allinea Software Abstract: Maintaining competitive advantage in the current oil and gas market is a growing challenge. HPC departments are pressured to obtain maximum productivity from their development staff and optimal utilization of existing compute resources. In this presentation Allinea will illustrate how the development tool Forge can be used to ensure program correctness and high performance of HPC codes with minimal effort. The application analytics tool Allinea Performance Reports will be demonstrated to illustrate how to characterize and understand HPC application runs.
Tuesday, October 18th - 3:00 PM Prairie View A&M Abstract: Advances in big data analytics are among the promising and valuable techniques in many industrial domains. Will the big data analytics techniques change the traditional methodology of seismic data interpretation in identifying and modeling geological features? Will geophysicists benefit by combining big data analytics techniques to enhance the quality of seismic data interpretation results? Our research aims to answer these questions by creating a cloud-based scalable seismic data analytics platform with deep learning technology, and assessing its applicability in seismic data interpretation. The session will go through the architecture of the platform, present its functionality, as well as the use case of applying deep learning technology on geological faults detection. The work is built on top of Apache Spark and Google TensorFlow deep learning framework. We acknowledge the support of the National Science Foundation for this project through a number of research and innovation grant programs.
Wednesday, October 19th - 9:00 AM Abstract: Is it possible to automate deployment of containerized HPC applications in the cloud? This session will introduce the new Nimbix process to do just that, without compromising performance, scale, or capabilities. Learn how to leverage readily available services such as the Docker Hub to achieve continuous integration of large scale applications using the state of the art in containerized HPC cloud technologies.
Wednesday, October 19th - 10:00 AM Allinea Software Abstract: Maintaining competitive advantage in the current oil and gas market is a growing challenge. HPC departments are pressured to obtain maximum productivity from their development staff and optimal utilization of existing compute resources. In this presentation Allinea will illustrate how the development tool Forge can be used to ensure program correctness and high performance of HPC codes with minimal effort. The application analytics tool Allinea Performance Reports will be demonstrated to illustrate how to characterize and understand HPC application runs.
Wednesday, October 19th - 11:00 AM Abstract: Advances in big data analytics are among the promising and valuable techniques in many industrial domains. Will the big data analytics techniques change the traditional methodology of seismic data interpretation in identifying and modeling geological features? Will geophysicists benefit by combining big data analytics techniques to enhance the quality of seismic data interpretation results? Our research aims to answer these questions by creating a cloud-based scalable seismic data analytics platform with deep learning technology, and assessing its applicability in seismic data interpretation. The session will go through the architecture of the platform, present its functionality, as well as the use case of applying deep learning technology on geological faults detection. The work is built on top of Apache Spark and Google TensorFlow deep learning framework. We acknowledge the support of the National Science Foundation for this project through a number of research and innovation grant programs.
Wednesday, October 19th - 1:00 PM Dynamic Measurement Abstract: Lightning strikes occur worldwide. They are not random. Locations are controlled by telluric currents. The NLDN (National Lightning Detection Network) lighting database has collected 18+ years of lightning strike data in the continental United States. The GLD-360 (Global Lightning Database – 360) has collected 5+ years of lightning strike data worldwide. NLDN data is most comprehensive and includes the timing (microseconds), location (150-600 foot horizontal resolution), rise-time (microseconds), peak current (kilo-amperes), polarity (80% of strikes negative), peak-to-zero (microseconds), number of sensors recording the strike, the major and minor axis of triangulated location calculations, chi-squared (quality measurement), and several other attributes for each lightning strike in the continental U. S. Historically these data were collected for insurance, meteorology, and safety markets. These data now support resource exploration like oil & gas, mineral, aquifer, and geothermal.
Wednesday, October 19th - 2:00 PM
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