Competitive Portfolios for Advanced Scientific Computing Research: Data Management and Visualization

Solicitation PDF

• Original at OSTI
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The SC ASCR program hereby announces its interest in advanced scientific computing research portfolios for accelerating discovery and innovation in support of the DOE mission. ASCR seeks to invest in DOE National Laboratory-led portfolios that balance long-term, high-impact research along with the ability to aggressively respond to, and take advantage of, emerging science and technology trends. The ASCR Computer Science (CS) research program [1] supports long-term, basic research that enables computing and networking at extreme scales and the understanding of extreme-scale and complex data from both simulations and experiments. ASCR, in tandem with industry and others, has made highly successful investments to ensure U.S. leadership in high performance computing (HPC), which resulted in Exascale systems that are enabling scientific discovery and decision support through data integration, simulation and modeling [2].

To ensure continued leadership in delivering on the promise of computational science, and drive innovation in energy-efficient and versatile HPC for science, ASCR seeks to invest in DOE National Laboratory-led portfolios that:

• Support long-term, high-impact CS research,
• Aggressively respond to, and take advantage of, emerging science and technology needs and trends including Artificial Intelligence (AI), and
• Collaborate with a diverse community of the most-promising academic and industry partners

SUPPLEMENTARY INFORMATION

Scientific research driven by Artificial Intelligence (AI)-enabled technologies is not only making scientists more productive but promises to change how scientists find the most-promising ideas to investigate in the future [3]. This requires deep changes in the methods available, and algorithms developed, to store, search, retrieve, analyze, and visualize scientific data. Past efforts which focused primarily on storing and analyzing data quickly only in specifically-anticipated contexts are giving way to discovery-optimized techniques which prioritize supporting AI- enabled investigation and the aggregation of curated data sets of many kinds.

In this context, ASCR seeks innovative research with vision beyond its current investments in HPC data management, storage [4], and scientific visualization [5] that will help enable the development of the next-generation energy-efficient and capable computing systems [6] and approaches enabling accelerated scientific discovery. This can include the use of new hardware, software, algorithms, and other related technologies that are currently at early stages of development.

While proposed work can leverage software from prior research efforts where they add significant value, ASCR is primarily looking for new research efforts in scientific data management, storage, and visualization. These efforts should build on the best available open platforms and befit the future of energy-efficient AI-driven scientific discovery where data management and visualization are fast, efficient, and flexible.