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Graphics Processing Unit (GPU) Acceleration for Cosite Interference Prediction Tools


OBJECTIVE: Port existing electromagnetic interference and vulnerability (EMI/EMV) simulation tools to the latest technology computer clusters in order to significantly reduce the time required for analyzing cosite interference on complex platforms. DESCRIPTION: Modern aircraft present very complex environments carrying a large number of radio frequency (RF) systems and their associated antennas. These RF systems must all function properly in operational environments without causing interference to each other (i.e., cosite interference). The operational requirements of these systems can vary depending on mission requirements and timeline. Operational frequencies (including frequency hopping) and other operating parameters of the RF systems can vary, and different RF systems may be required to operate simultaneously and in different configurations of the aircraft. To insure mission success, all possible configurations must be evaluated and any interference mitigated prior to fielding the aircraft. The goal of EMI/EMV prediction tools is to allow analysts to accurately predict RF system level performance degradation in these types of complex environments and to explore mitigation methodologies to eliminate any cosite interference. These simulations can be very time consuming due to the large number of potential permutations of operational parameters and configurations that need to be considered for a complete analysis, and this is exacerbated by the added computational burden of including the non-linear aspects of the problem that often represent important interference mechanisms. Advances in computer architecture over the past decade have greatly reduced the cost of computational hardware required for computational simulations. The goal of this project is to develop new analysis algorithms that can take advantage of parallelization on modern computational hardware to improve execution time by orders of magnitude, thus reducing the time required for a complete cosite interference analysis. The hardware for such an effort can be a traditional central processing unit (CPU) cluster or a graphics processing unit (GPU) cluster [1]. GPU-based development has moved into the mainstream due to the maturity of GPU programming languages such as CUDA [2] and OpenCL [3] and researchers are using GPU clusters for a variety of simulation problems [4]. With the above in mind, we are seeking innovative solutions for developing EMI/EMV simulation algorithms for cosite interference prediction on both CPU and GPU-based parallel environments for the purpose of greatly accelerating their performance. Small businesses must clearly demonstrate the capabilities of their simulation tool and its effectiveness in predicting cosite interference in their proposal. They should also have an understanding of GPUs and OpenCL or CUDA and be prepared to work in both a CPU and a GPU environment. Previous experience in programming GPUs is highly desirable. Prime should have property rights to the source code of the EMI/EMV simulation tool. PHASE I: Investigate the key computational algorithms found in EMI/EMV simulation tools in order to determine the potential benefits of different forms of hardware parallelization. Identify EMI/EMV algorithms that may be problematic in transferring to a parallel environment and develop modifications. Research new algorithms that can provide higher-fidelity solutions than existing technology, and if feasible, develop prototypes to measure their effectiveness. Explore other hardware acceleration techniques that could potentially be developed during the Phase II effort in order to improve the accuracy or computational performance of EMI/EMV simulations. Develop a Phase II implementation plan for a hardware accelerated version of the software. PHASE II: Execute the implementation plan created in Phase I to develop the hardware-accelerated EMI/EMV software algorithms and build a commercially mature software tool. Port the new EMI/EMV software tool to the project sponsor's cluster environment. Validate the successful implementation of the parallelization through timing and accuracy studies using realistically complex aircraft environments. Ensure that the resulting algorithms are scalable with increasing number of processors. Deliver, install, and provide training for the parallelized EMI/EMV simulation software to NAVAIR along with thorough documentation. If NAVAIR is interested in other hardware acceleration techniques identified during Phase I, implement prototype capabilities during the Phase II effort. PHASE III: Refine the methodology and tool developed in Phase II and expand its predictive capabilities. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The technology developed under this topic can be used in the commercial communications industry, including platform integration, electromagnetic compatibility (EMC) and electromagnetic interference (EMI) and antenna placement. REFERENCES: 1. What is GPGPU? 2. GPU Computing: The Revolution. 3. OpenCL - The open standard for parallel programming of heterogeneous systems. 4. Z. Fan, F. Qiu, A. Kaufman, & S. Yoakum-Stover (2004). GPU Cluster for High Performance Computing. In Proc. of the 2004 ACM/IEEE Conf. on Supercomputing (SC'04). IEEE Computer Society, Washington, DC, USA, 47
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