Opportunity

Simpler Grants.gov #24-571

NSF and DOE Joint Research Grant for Correctness in Scientific Computing Systems

Buyer

National Science Foundation

Posted

May 10, 2024

Respond By

August 11, 2026

Identifier

24-571

NAICS

541715

This opportunity is a joint research grant solicitation from the National Science Foundation (NSF) and Department of Energy (DOE) focused on advancing correctness in scientific computing systems. - Government Buyer: - U.S. National Science Foundation (NSF) - Department of Energy (DOE) - Program: Correctness for Scientific Computing Systems (CS2) - Purpose: - Elevate correctness as a core requirement in scientific computing tools and tool chains - Address issues such as numerical rounding errors, floating-point exceptions, data races, deadlocks, and memory faults - Eligibility: - U.S.-based non-profit, non-academic organizations - Institutions of higher education - DOE National Laboratories - Collaboration Requirement: - Proposals must include at least one (co)-PI from scientific computing and one from formal reasoning - Funding Details: - Total available funding: $18,000,000 - Funding instrument: Grant - No cost sharing or matching required - No specific OEMs, vendors, products, or part numbers are requested, as this is a research grant opportunity - Place of performance includes DOE National Laboratories and other eligible U.S. institutions

Description

The Correctness for Scientific Computing Systems (CS2) program is a joint initiative by the National Science Foundation (NSF) and the Department of Energy (DOE) aimed at elevating correctness as a fundamental requirement for scientific computing tools and tool chains. The program addresses challenges core to DOE's mission and essential to NSF's mission of broad scientific progress, focusing on ensuring desired behavioral properties in scientific computing systems. It emphasizes collaboration between researchers in scientific computing and formal reasoning to prove correctness in performant scientific computing systems, including probabilistic notions of correctness and system model discrepancies.

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