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Flexible and Architecture Aware Kalman Filter Code Generation

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0022414
Agency Tracking Number: 0000263593
Amount: $200,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: C53-32c
Solicitation Number: DE-FOA-0002554
Timeline
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-02-14
Award End Date (Contract End Date): 2023-02-13
Small Business Information
5335 Far Hills Ave
Dayton, OH 45459-4248
United States
DUNS: 141943030
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Gerald Sabin
 (937) 433-2886
 GSabin@RNET-Tech.com
Business Contact
 V. Nagarajan
Phone: (937) 433-2886
Email: VNagarajan@RNET-Tech.com
Research Institution
 Washington State University
 
2710 Crimson Way
Richland, WA 99354-1671
United States

 () -
 Nonprofit College or University
Abstract

Kalman filtering is a statistical estimation algorithm that uses measurements observed over time and a physical model to minimize errors due to uncertainties in the measurements (measurement noise) and the physical process (process noise). Kalman Filters are the de facto standard for charged particle reconstruction in medium and high-energy nuclear physics experiments such as those at RHIC and envisioned at the EIC. The proposal is to develop a code generator to generate custom, tuned, optimized Kalman Filter functions that can be included in the software workflow of any application requiring Kalman filtering. The generated code will automatically generate the Jacobian, implement architecture-aware optimizations, and be tuned for the specific problem size and hardware architecture. The generated code will include C++ source code (e.g., CUDA, HIP, OpenMP 5.0, OpenCL, Kokkos, RAJA) with Fortran and Python bindings. Phase I work will focus on automatic code generation and optimization for the Linear and Extended Kalman Filter (EKF). The goal of the Phase I project will be to demonstrate the technical feasibility of a code generation tool for Kalman filters and its performance benefits. The performance portable Kalman Filter code generation tool will be developed for and tested with particle track reconstruction and particle track simulation codes such as ACTS and Geant4. This will allow for performance portable Kalman Filter codes in applications of importance to the DOE. This will allow deployment on the most cost-effective GPU platform near the colliders, as well as during post-processing on leadership class exascale resources with a range of GPU acceleration options. Kalman filtering is also used in a wide range of applications, including guidance, navigation and control of vehicles, signal processing, robot motion planning, trajectory optimization, modeling of the central nervous system among many others.

* Information listed above is at the time of submission. *

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