Deep Computing Solutions: Difference between revisions

{{Program

|ProgramName=Deep Computing Solutions

The '''Deep Computing Solutions''' (DCS) initiative is a Department of Energy-led effort focused on harnessing advanced computational technologies, including high-performance computing (HPC) and deep learning, to tackle pressing scientific and engineering challenges. Managed by the Office of Science, DCS supports research in areas like climate modeling, fusion energy, and quantum materials by providing researchers with cutting-edge tools and infrastructure, such as GPU-accelerated systems and optimized software stacks.<ref>{{cite web |url=https://www.energy.gov/science |title=Office of Science |publisher=Department of Energy}}</ref> Its interdisciplinary approach has made it a cornerstone for integrating AI-driven methods into traditional scientific workflows, fostering collaboration across national labs and universities.

{{Official URL (simple)|url=https://www.energy.gov/science/deep-computing-solutions}}

* Achieve exascale computing benchmarks, targeting quintillions of calculations per second.<ref>{{cite web |url=https://www.ornl.gov/science-area/computing |title=Computing Research |publisher=Oak Ridge National Laboratory}}</ref>

Deep Computing Solutions is administered by the Office of Science within the Department of Energy, leveraging resources from national laboratories like Oak Ridge and Lawrence Livermore.<ref>{{cite web |url=https://www.energy.gov/science |title=Office of Science |publisher=Department of Energy}}</ref> A program director, typically titled "Director of Computational Science," oversees operations, coordinating with a team of computational scientists and engineers. Funding is sourced from federal appropriations, with additional support from industry partnerships, and is channeled into hardware upgrades, software development, and workforce training.

The origins of Deep Computing Solutions trace back to the [[America COMPETES Act of 2007]], which emphasized U.S. leadership in science and technology, prompting increased investment in computational research.<ref>{{cite web |url=https://www.energy.gov/science |title=Office of Science |publisher=Department of Energy}}</ref> Launched in 2010, DCS built on earlier HPC efforts, adapting to the rise of deep learning in the 2010s. Key milestones include deploying some of the world’s fastest supercomputers and integrating AI frameworks like TensorFlow into scientific computing by 2018. It continues to evolve, with plans to support next-generation exascale systems and sustainable energy research.

Initially funded with $50 million in 2010, DCS has seen its budget grow to support supercomputing infrastructure and AI research, with annual allocations now exceeding $200 million.<ref>{{cite web |url=https://www.ornl.gov/science-area/computing |title=Computing Research |publisher=Oak Ridge National Laboratory}}</ref> Funding began with the 2010 fiscal year and remains ongoing, bolstered by supplemental grants like those from the Bipartisan Infrastructure Law. It relies on federal budgets, with contributions from tech partners, ensuring long-term viability for cutting-edge hardware and software.

The program operates through a network of national labs, providing researchers with access to supercomputers and cloud-based AI tools.<ref>{{cite web |url=https://www.energy.gov/science |title=Office of Science |publisher=Department of Energy}}</ref> Its rollout has progressed in phases: early HPC systems in the 2010s, AI integration by 2015, and exascale targets set for the 2020s. There’s no fixed end date; DCS adapts to technological advancements, emphasizing open-source software and collaborative platforms to maximize impact.