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Technologies for Large-Scale Numerical Simulation

Description:

Scope Title:

Scope No: 1 Exascale Computing

Scope Description:

The largest challenge facing the high-performance computing (HPC) community today is the tremendous amount of refactoring that is typically required of existing large-scale applications in order to address the hardware paradigm shift that has taken place over the past 5 to 10 years to usher in the exascale era, which is now upon us—and this shift is expected to continue and become even more heterogeneous in the coming years. There is an urgent need for application refactoring and performance portability in this environment. To address these challenges, the approach of this subtopic is to seek novel software technologies such as artificial intelligence (AI) and machine learning that will provide notable benefits to NASA's supercomputing users and facilities and to infuse these technologies into NASA supercomputing operations. 

 

NASA scientists and engineers are increasingly turning to large-scale numerical simulation on supercomputers to advance understanding of complex Earth and astrophysical systems and to conduct high-fidelity aerospace engineering analyses. The goal of this subtopic is to increase the mission impact of NASA's investments in supercomputing systems and associated operations and services. Specific objectives are to: 

  • Decrease the barriers to entry for prospective high-performance computing (HPC) cloud users.
  • Increase the usability of the JupyterLab/hub environment by allowing users to transparently make use of existing or dynamic cloud resources.
  • Minimize the supercomputer user's total time-to-solution (e.g., time to discover, understand, predict, or design).
  • Increase the achievable scale and complexity of computational analysis, data ingest, and data communications.
  • Enhance and accelerate the execution of CFD (computational fluid dynamics) models using artificial intelligence (AI) to assist with handling of the high-dimensionality field and computational expensiveness.
  • Reduce the cost of providing a given level of supercomputing performance for NASA applications such as FUN3D with help from AI and machine learning.
  • Enhance the efficiency and effectiveness of NASA's supercomputing operations and services. 
  • Enhance the supercomputer application area towards data analytics and AI and expand to other mission customers.
  • Develop next-generation performance analysis tools, incorporating AI to recognize patterns in an application software.


Expected outcomes are to improve the productivity of NASA's supercomputing users, broaden NASA's supercomputing user base, accelerate advancement of NASA science and engineering, and benefit the supercomputing community through dissemination of operational best practices. 

 

The approach of this subtopic is to seek novel software technologies that provide notable benefits to NASA's supercomputing users and facilities and to infuse these technologies into NASA supercomputing operations. Successful technology development efforts under this subtopic would be considered for follow-on funding by, and infusion into, NASA's high-end computing (HEC) projects: the High-End Computing Capability project at Ames and the Scientific Computing project at Goddard. To assure maximum relevance to NASA, funded SBIR contracts under this subtopic should engage in direct interactions with one or both HEC projects, and with key HEC users where appropriate.

Expected TRL or TRL Range at completion of the Project: 5 to 7

Primary Technology Taxonomy:

  • Level 1 11 Software, Modeling, Simulation, and Information Processing
  • Level 2 11.6 Ground Computing

Desired Deliverables of Phase I and Phase II:

  • Prototype
  • Software

Desired Deliverables Description:

Research should be conducted to demonstrate technical feasibility and NASA relevance during Phase I and show a path toward a Phase II prototype demonstration. Offerors should demonstrate awareness of the state of the art of their proposed technology and should leverage existing commercial capabilities and research efforts where appropriate, including open-source software and open standards. Note that the NASA supercomputing environment is characterized by: 

  • HEC systems operating behind a firewall to meet strict information technology (IT) security requirements.
  • Communication-intensive applications.
  • Massive computations requiring high concurrency.
  • Complex computational workflows and immense datasets.
  • The need to support hundreds of complex application codes, many of which are frequently updated by the user/developer. 
  • Encourage to develop new application areas like AI and machine learning.

State of the Art and Critical Gaps:

The state of the art and the critical gaps of the main technology areas are:

1. NASA science requires at least 100x more powerful supercomputers and 1,000x higher application parallelism in 10 years, at the same power.

2. Current technologies for high-fidelity computational simulation and data analytics are distinct, and interfacing them is inefficient.

Relevance / Science Traceability:

Virtually all high-end computing systems and applications can benefit from the deliverables of this subtopic. As the demand for high-end computing continues to grow, there is an increasing need for the solicited technologies in both the government and industry.

References:

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