Developer Resources for Energy
A hub of SDKs, technical resources, and more for developers working in the energy industry.
App Frameworks and SDKs
NVIDIA® CUDA-X, built on top of NVIDIA CUDA®, is a collection of libraries, tools, and technologies that deliver dramatically higher performance - compared to CPU-only alternatives - across multiple application domains, from artificial intelligence (AI) to high performance computing (HPC).
The NVIDIA EGX™ platform delivers the power of accelerated AI computing to the edge with an easy-to-deploy, cloud-native software stack, a range of validated servers and devices, Helm charts to ease deployment of AI applications, and a vast ecosystem of partners who offer EGX through their products and services.
The PGI Community Edition with OpenACC® offers scientists and researchers a quick path to accelerated computing with less programming effort. By inserting compiler “hints” or directives into your C, C++, or Fortran code, the PGI OpenACC compiler will offload and run your code on the GPU and CPU.
JOIN US AT GTC
Discover what’s next for AI across the Energy industry with 30+ groundbreaking sessions spanning dynamic energy systems, field planning, geophysics optimization, renewable energy, and seismic image analysis. April 12-16.Register for Free Sessions
Browse by Resource Type
Machine Learning with OpenShift and NVIDIA GPUs at ExxonMobil
Learn how to use NVIDIA GPUs on an OpenShift platform and how data scientists and engineers at ExxonMobil are using open-source containers for AI and machine learning.
SaltNet: A Production-Scale Automated Salt Model Building Pipeline
Learn about the development of a production-scale workflow that leverages NVIDIA GPUs to accurately and quickly identify salt structure.
Reservoir Simulation Evolved: From Kepler to Ampere and Beyond
Learn the benefits and capabilities of full GPU acceleration for reservoir modeling, including a historical perspective on performance and forward forecast.
Accelerating Machine Learning at the Edge: Real-Time Well Engineering
Real-time machine learning enables quantitative analytics at the edge. To calculate the results in a satisfactory time window, both the hardware and software should be up to the task.
Fully Exploiting a GPU Supercomputer for Seismic Imaging
This demonstration shows the porting of modern seismic applications like reverse time migration, full waveform inversion, and one-way migration to the GPU-accelerated Pangea III supercomputer.
Autonomous Anomaly Detection for Dense-Sensor IoT Prognostics
Low latencies and high throughputs are critical to achieving real-time streaming prognostics, particularly in cloud environments. Learn how NVIDIA GPU acceleration of MSET2, Oracle’s advanced machine learning pattern-recognition method, yields substantial latency reduction and throughput gains.
Netherlands-Based Rolloos Taps AI to Make Oil Rigs Safer for Workers
Company offers a network of cameras, sensors, and deep learning to locate workers in unsafe spots and alert them before an accident occurs.
How AI is Providing Digital Twins for Predictive Maintenance in Oil and Gas
The oil and gas industry faces serious challenges, including the costs of maintaining its aging infrastructure. Deep learning, GPUs, and the concept of “Digital Twins” offer enormous potential for predictive maintenance.
How GPUs are Transforming the Oil and Gas Industry
Oil and gas companies are in the data business as much as they are in the hydrocarbon business. To stay ahead of the competition, they need to derive insights from the massive amounts of sensor, geolocation, weather, drilling, and seismic data they generate.
Leveraging Accelerated Computing to Transform the Energy Industry
Stone Ridge Technology and NVIDIA discuss how to mitigate risks, increase efficiency, and reduce uncertainty in integrated production and reservoir systems for oil and gas.
Remote Work with NVIDIA: Oil and Gas Exploration from Home
GeoComputing Group, Kosmos Energy, and NVIDIA discuss solutions that allow users to stream their most robust applications and datasets without compromising security.
NVIDIA DEEP LEARNING INSTITUTE
The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Training is available as self-paced, online courses or in-person, instructor-led workshops.
High Performance Computing with Containers
Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for HPC application development.
AI Workflows for Intelligent Video Analytics with DeepStream
Fundamentals of Accelerated Computing with CUDA C/C++
Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniques and the NVIDIA Nsight™ Systems profiler.
Fundamentals of Accelerated Computing with OpenACC
Explore how to build and optimize heterogeneous applications on multiple GPU clusters using OpenACC, a high-level GPU programming language.
Sign up for the latest developer news from NVIDIA