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2019 Rice Oil & Gas Conference
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AI & Deep Learning
Data Storage & I/O Performance
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#1
High Performance Computing and High Performance Data Analytics, and what O&G will do with it in the next 10 years
#2
Evolving System Design for HPC: Next Generation CPU and Accelerator Technologies
#3
AI & Deep Learning: AI for HPC in Oil & Gas: Experiences and Opportunities
#4
Conference Registration & Networking
#5
Systems & Facilities: Moving Seismic Migration Into Cloud
#6
The US Exascale Computing Project: Addressing Extreme-Scale Computing Challenges
#7
New Era in HPC
#8
Workshop: Singularity Containers: Secure, Repeatable and Mobile Runtimes for Oil and Gas Applications and Workflows
#9
Workshop: Best Practices in Supercomputing Systems Management
#10
Welcome
#11
Arm in HPC
#12
AI & Deep Learning: Accelerating Traditional Data Science using GPUs and RAPIDS
#13
Data Storage & I/O Performance: A Generic and Holistic High Performance Distributed Computing and Storage System for Large Datasets in Oil and Gas Industry
#14
Networking Reception
#15
Systems & Facilities: Perlmutter - a 2020 pre-exascale GPU-accelerated system for NERSC.
#16
Early Career Panel
#17
Post-K: A Game Changing Supercomputer for Convergence of HPC and Big Data/AI
#18
Data Storage & I/O Performance: Breaking the Memory/IO Wall in Oil & Gas Applications using Approximation Techniques: Mixed Precision and Compression.
#19
Computing for the Endless Frontier
#20
Software Technology & Applications: Automatic generation of production-grade hybrid MPI-OpenMP parallel wave propogators using Devito
#21
Systems & Facilities: CORAL System Update
#22
Software Technology & Applications: Software Technology & Applications: Seismic modeling on Arm architectures
#23
Systems & Facilities: Extreme Efficiency in the Exascale Data Centre
#24
Democratising Access to HPC
#25
AI & Deep Learning: Zero-Communication Model Parallelism for Distributed Extreme-Scale Deep Learning
#26
Data Storage & I/O Performance: Solving I/O Slowdown: The "Noisy Neighbor" Problem
#27
AI & Deep Learning: Strong Scaling Strategy for Deep Neural Network Seismic Segmentation Models
#28
Data Storage & I/O Performance: Future-Looking Data Storage for Peak Performance at ACCRE’s HPC Environment
#29
AI & Deep Learning: Interactive Machine Learning and Inference
#30
Breakfast & Networking
#31
Welcome
#32
Break
#33
Software Technology & Applications: An Overview of RAJA
#34
Systems & Facilities: Cloud-based HPC Services for Well Stimulation Planning
#35
Lunch & Networking
#36
Workshop: Hiring, Developing and Retaining an Inclusive Workforce
#37
Data Storage & I/O Performance: Providing Balanced Systems and Expectations with the IO500
#38
Software Technology & Applications: Swift and Parsl: Parallel Scripting for HPC Workflow in Science and Engineering
#39
Software Technology & Applications: Python Workflows on HPC Systems: Pitfalls and Best Practice
#40
Break
#41
Poster and Closing Networking Session
#42
A Parallel Workflow Framework for Data and Compute Intensive Applications
#43
A Comparison of Image Segmentation Methods
#44
A discontinuous Galerkin method for first-order elastic-acoustic coupled wave equations
#45
A machine learning approach for stochastic seismic inversion on high performance computing facilities
#46
Computing Challenges and Solutions in Rock Drilling Model enabled Drill Bit Design
#47
Data-parallel distributed training of very large models beyond GPU capacity
#48
Deep Global Model Reduction Learning
#49
Extending HPCToolkit to Measure and Analyze Code Performance on GPU-accelerated Platforms
#50
Gas Detection and Localization Wireless Sensor Network
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Monday, Mar 4
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