Opportunity Information: Apply for USGS 16 FA 0152
Apply for USGS 16 FA 0152
- The DOI-USGS1 in the science and technology and other research and development sector is offering a public funding opportunity titled "Building a CUDA-Based Parallel Processing Library for 3DEP" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.808.
- This funding opportunity was created on May 04, 2016 and posted on May 04, 2016.
- Applicants must submit their applications by May 18, 2016. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $60,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education.
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Opportunity Summary:
The U.S. Geological Survey (USGS), within the Department of the Interior, issued a discretionary funding opportunity titled "Building a CUDA-Based Parallel Processing Library for 3DEP" (Funding Opportunity Number: USGS 16 FA 0152) to support research and development aimed at speeding up the processing of large-volume lidar data products used in the 3D Elevation Program (3DEP). The core objective is to develop a CUDA-based parallel processing library that takes advantage of GPU acceleration, with the intent that the resulting software can be deployed on systems with either a single GPU or multiple GPUs working together. In practical terms, the project is targeting performance improvements for workflows that are traditionally run on serial CPU implementations, which can be slow and resource-intensive when dealing with modern lidar data volumes.
The solicitation sets clear performance expectations for the resulting library. It should be capable of processing a single lidar dataset as large as 6 GB, and it is expected to achieve an average speedup of about 20x relative to serial CPU processing. This frames the project as a focused engineering and applied R&D effort: it is not merely exploratory research, but a deliverable-oriented build that emphasizes real-world throughput, scalability, and efficiency in handling sizable lidar products. The emphasis on both single- and multi-GPU environments suggests the USGS was looking for a solution that can scale from modest workstations to more capable GPU servers, making it more broadly usable across different operational settings.
The funding mechanism for this opportunity is a cooperative agreement, indicating that USGS likely anticipated having substantial involvement during the project period (for example, coordination on technical requirements, access to sample data or validation approaches, or iterative review of progress). The activity category is Science and Technology and other Research and Development, and the opportunity is associated with CFDA number 15.808. Eligibility is limited to public and state-controlled institutions of higher education, meaning the primary target applicants are qualifying universities and similar academic entities.
Administratively, the opportunity was posted and created on May 4, 2016, with both the original and current closing date listed as May 18, 2016, reflecting a short application window. The award ceiling was $60,000, and USGS expected to make one award, implying a single, relatively compact project intended to produce a specific technical capability rather than a broad multi-institution program. Overall, the grant is essentially a small, tightly scoped R&D investment to produce a GPU-accelerated CUDA library that substantially reduces lidar processing time for 3DEP-scale data volumes, with measurable performance targets and deployment expectations across different GPU configurations.
Frequently Asked Questions (FAQ)
What is the title of this USGS funding opportunity?
The opportunity is titled "Building a CUDA-Based Parallel Processing Library for 3DEP."
What is the Funding Opportunity Number?
The Funding Opportunity Number is USGS 16 FA 0152.
Which federal agency is offering this grant?
The grant is offered by the U.S. Geological Survey (USGS) within the Department of the Interior.
What is the main purpose of the grant?
The purpose is to support research and development that speeds up processing of large-volume lidar data products used in the 3D Elevation Program (3DEP) by building a CUDA-based parallel processing library that uses GPU acceleration.
What is 3DEP in the context of this opportunity?
3DEP refers to the 3D Elevation Program, and this project is aimed at improving the speed and efficiency of processing the lidar data products used within that program.
What type of software deliverable is expected?
A CUDA-based parallel processing library designed to accelerate lidar data processing workflows using GPUs.
What computing approach is the project emphasizing?
The project emphasizes GPU acceleration using CUDA and parallel processing, specifically to improve workflows that are traditionally run as serial CPU implementations.
Does the library need to work on different GPU setups?
Yes. The solicitation states the intent that the software can be deployed on systems with either a single GPU or multiple GPUs working together.
What performance improvement is USGS expecting?
The library is expected to achieve an average speedup of about 20x relative to serial CPU processing.
What size lidar dataset should the library be able to process?
The solicitation sets an expectation that the library should be capable of processing a single lidar dataset as large as 6 GB.
Is this opportunity more like exploratory research or a deliverable-focused build?
It is framed as a deliverable-oriented engineering and applied R&D effort, with measurable performance targets focused on throughput, scalability, and efficiency for sizable lidar products.
What is the funding mechanism for this opportunity?
The funding mechanism is a cooperative agreement.
What does it mean that the award is a cooperative agreement?
Based on the solicitation description, a cooperative agreement indicates USGS likely anticipated substantial involvement during the project period, such as coordination on technical requirements, access to sample data or validation approaches, and iterative review of progress.
What is the activity category for this opportunity?
The activity category is Science and Technology and other Research and Development.
What CFDA number is associated with this opportunity?
The opportunity is associated with CFDA number 15.808.
Who is eligible to apply?
Eligibility is limited to public and state-controlled institutions of higher education.
Are private universities or non-academic organizations eligible?
The information provided states eligibility is limited to public and state-controlled institutions of higher education. No other applicant types are listed as eligible in the provided description.
When was the opportunity posted and created?
It was posted and created on May 4, 2016.
What was the application closing date?
Both the original and current closing date are listed as May 18, 2016.
How long was the application window?
Based on the posted/created date (May 4, 2016) and the closing date (May 18, 2016), the application window was short.
What is the maximum award amount (award ceiling)?
The award ceiling was $60,000.
How many awards did USGS expect to make?
USGS expected to make one award.
What does it imply that only one award was expected?
It implies a single, relatively compact project intended to produce a specific technical capability rather than a broad multi-institution program.
What kind of workflows is this library intended to improve?
It is intended to improve lidar processing workflows that are typically run as serial CPU implementations, which can be slow and resource-intensive for modern lidar data volumes.
Why is scalability mentioned as important in this solicitation?
The solicitation emphasizes deployment on both single- and multi-GPU systems, suggesting the solution should scale from modest workstations to more capable GPU servers and be usable across different operational settings.
What are the key technical expectations summarized in one place?
The library should (1) use CUDA-based GPU acceleration, (2) support both single- and multi-GPU deployment, (3) process a single lidar dataset as large as 6 GB, and (4) deliver an average speedup of about 20x compared to serial CPU processing.
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