Competitive Portfolios | Kalyan Perumalla

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

Sat, 01 Feb 2025 00:00:00 +0000

Solicitation PDF

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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.

Competitive Portfolios for Advanced Scientific Computing Research

Mon, 01 Jan 2024 00:00:00 +0000

Awards

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

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

Support long-term, high-impact research,
Aggressively respond to, and take advantage of, emerging science and technology trends, and
Collaborate with a diverse community of the most-promising academic and industry partners.

Research Proposals: Each Laboratory is limited to leading one proposal in response to this Announcement. The Principal Investigator (PI) must be a Laboratory division director or a manager with equivalent supervisory responsibilities. The proposal narrative (at most 30 pages) must provide a Laboratory vision and management plan for the portfolio of capabilities stemming from the proposed research and development in scientific computing. The narrative is comprised of one or more research Thrusts. Each Thrust must have a Laboratory Senior/Key Personnel (SKP) as the Lead along with other SKPs and researchers. Overall, the proposal must describe the research Thrusts and integration Tasks needed to enable new scientific computing- based capabilities that address national priorities in energy, the environment, and national security. The proposal should describe how the overall vision and each Thrust take advantage of the responding Laboratory’s, and each partnering institution’s, distinctive expertise and capabilities.

Research Thrusts: A Thrust is a distinct, focused area of basic research in scientific computing.

Applied Mathematics: Single-facet Thrusts require and build on research expertise in a core area such as s linear algebra and nonlinear solvers, discretization and meshing, multi-scale mathematics, discrete mathematics, optimization, complex systems, emergent phenomena, and applied analysis methods including but not limited to analysis of large- scale data, uncertainty quantification, and error analysis, or related topics. [4, pg. 281]
Computer Science: Single-facet Thrusts require and build on research expertise in a core area such as programming languages, high-performance computing tools, peta- to exa- scale scientific data management and scientific visualization, distributed computing infrastructure, programming models for novel computer architectures, and automatic tuning for improving code performance, or related topics. [4, pg. 281]
Advanced Computing Technologies and Testbeds: Access to resources to test and develop new tools, libraries, languages, etc. is an important enabling capability [4, pg. 281]. Thrusts focused on the establishment and development of testbeds1 that offer promising paths to versatile energy-efficient computing, addressing among other challenges, the data storage and movement requirements of artificial intelligence and simulation workloads may be proposed in response to this Announcement. Computing hardware should be interpreted broadly to include computational, memory, networking,