HPE’s HPC and AI Solutions for the Power Industry: Driving the Fossil Fuel Transition into a Renewable Future

{SPONSORED CONTENT]The oil and gas industry has been a high-speed engine that has fueled HPC growth for over 40 years. It’s an industry with an insatiable demand for high-performance, high-capacity supercomputing processing power and data storage – the demand continues to increase as fossil fuel reserves have become harder to come by. In fact, one of the reasons the dire and incorrect predictions of the 1970s and 1980s that the world would run out of oil within decades is because they failed to account for HPC’s steady progress in managing increasingly complex seismic workloads.

Main drivers of the energy transition (source: HPE)

Long in the top echelon of supercomputing vendors serving advanced supercomputing sites, HPE has always competed successfully in the energy industry. In fact, one of the most powerful HPC systems installed at a commercial HPC site is the “HPE-Cray” DAMMAM-7. Number 11 on the Top500 list of the world’s most powerful supercomputers, the system is installed at Saudi Aramco, the Saudi oil and gas company.

As the HPC industry moves into the exascale era (systems capable of a billion trillion calculations per second), the needs of the O&G industry are changing – not only for traditional seismic exploration, but also to support the transition of industry to alternative energy sources. Increasingly, the industry needs larger and more extensive HPC platforms that incorporate advanced artificial intelligence as well as powerful and flexible edge and cloud capabilities – and, naturally, elite processing power. .

“Across the energy sector, organizations are investing heavily in physical and digital infrastructure to better generate, transform, store and distribute energy,” said Bill Mannel, vice president and general manager, HPC, at HPE. “High performance computing and artificial intelligence are becoming increasingly crucial for this digital transformation. “

“HPC and AI are also transforming,” Mannel said. “First, as HPC, AI and Big Data converge, exascale-class systems provide faster insight to solve some of the critical issues of this new energy age. Second, energy companies are increasingly expanding their on-premises digital infrastructure with cloud and edge computing.

In short, the HPC resources used by oil and gas companies must support strategies to deal with severe pressure to balance strict climate change targets with growing demand for fossil fuels. Population density and energy consumption cause unsustainable levels of carbon and greenhouse gas (GHG) emissions, leading to climate change. In fact, energy company operations alone account for 9% of all human-made GHG emissions. Energy companies are increasingly facing social, legal and environmental pressures from stakeholders to decarbonize. And they do – oil and gas companies are turning into carbon neutral energy companies.

source: HPE

“As they transition, energy companies will increasingly use a greater variety of HPC and AI workloads from different verticals,” Mannel said. “With approximately 40 years of experience, HPE is a recognized leader in the HPC verticals and provides customers with the expertise and solutions to advance their businesses using these workloads. To stay competitive, HPE customers can more quickly and cost-effectively process complex data, reduce risk, and improve decision-making by leveraging cloud and exascale computing.

Bringing HPC experience across a range of software applications critical to various industries, HPE works with the open source community and customers and business partners on initiatives to help energy companies implement a diverse set of workloads. energy transition work, such as:

The diversity of HPC and AI applications for energy transition workloads requires a new approach to traditional HPC.

Next-generation systems will have to deal with exascale class performance requirements and massive data throughput requirements. These new systems will be more heterogeneous with multiple processors, accelerators such as GPUs, a variety of interconnects and other elements.

Additionally, the delivery of HPC and AI is changing. The energy industry is increasingly expanding on-premise data centers with cloud computing to improve the end-user experience, agility and economy. The use of public cloud in geoscience is expected to increase by 22.4% CAGR through 2024, according to industry analysis company HPC Hyperion Research.

Integration of HPC, analytics and AI for the energy transition (source: HPE)

Public clouds have brought about a radical change in the flexibility and elasticity of computing cycles. Methodologies such as containerized workloads are now also deployed to on-premises systems facilitating the portability of software between public clouds and on-premises data centers. While this flexibility is great, once workloads mature and move from development to production, the cost of running in a public cloud can skyrocket.

Another problem with data-intensive workflows is data fetching. It is usually easy and inexpensive to upload data to the cloud provider, which is good when the value of the data is low. But because customers want to implement AI and analytics, data repatriation can be hampered due to egress costs.

HPE GreenLake, a pillar of HPE’s drive to become a cloud-centric technology company, is designed to be the best of two global solutions: to deliver the public cloud economy with the security and performance of IT on site, provide a cloud-like infrastructure while retaining control over data and managing and scaling workloads (pay-as-you-go), but with the benefits of dedicated systems.

GreenLake is built on HPE’s portfolio of integrated HPC solutions for compute, networking, storage and software, with a single point of contact for all support requirements through HPE Pointnext services.

HPE’s portfolio of HPC and AI solutions for the energy transition (source: HPE)

These services are provided by HPE support staff with experience in helping oil and gas companies with their energy transition and solutions tailored to their specific needs. Energy companies can run HPC and AI workloads with HPE solutions at the edge (where increasing volumes of data are generated), in data centers, and in cloud environments (for greater flexibility and economy) .

HPE solutions range from small, simple systems to exascale-class supercomputers with tailor-made software, interconnect and storage capabilities.

HPE Cray supercomputing systems and the HPE Apollo family are purpose-built HPC and AI platforms that can support a wide range of size, complexity, processor, and accelerator choices. HPC options include top-bin processors, fast memory, integrated accelerators (GPUs or coprocessors) and fast cluster arrays and I / O interconnects.

For harsh edge environments such as oil rigs or smart meter / drills, HPE Edgeline systems provide enterprise-class compute, storage, networking, security and systems management at the edge.

Additionally, as energy workflows become increasingly complex and data-intensive, the HPE HPC Storage Portfolio meets the demands of AI storage as well as all-flash enterprise file storage. , and it is also scalable and cost effective.

HPE HPC and AI compute, storage and software solutions portfolio (source: HPE)

The Cray ClusterStor E1000 is uniquely designed to meet the demanding I / O requirements of supercomputers, and HPE Parallel File System Storage provides a high performance solution for HPC clusters. This wallet also includes item storage; data management framework software to manage, migrate, protect and archive data.

“Energy companies around the world rely on these HPE solutions,” Mannel said. “Now, HPE is encouraging collaborations and innovations to help energy customers improve oil and gas exploration and production, reduce energy-related emissions, and switch to alternative renewable energy sources for stay competitive. It is our mission in the energy sector, to play a leading role in the new era of energy and for the sustainable future of our planet.

For more information, read Energy Transition and the Exascale Era white paper

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