Cluster-Based and GPU-Driven Parallel Computing Model to Accelerate Circuit Simulation
Shital V. Jagtap1, Y.S. Rao2
1Shital V. Jagtap*, Research scholar, Computer engineering department, Ramrao Adik Institute of Technology, Navi Mumbai, India.
2Dr. Y. S. Rao, Principal, Sardar Patel College of Engineering, Mumbai, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2402-2408 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1836029420/2020©BEIESP | DOI: 10.35940/ijitee.D1836.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this digital age circuit design, analysis and validation is not only fundamental step but quite crucial in all the industries and in research. Simulation software is available for circuit analysis, but they all prove to be slower for very large circuit simulation or to execute thousands of iteration of transient analysis. Accelerating simulator is as important as speeding up circuit design. In this paper we have addressed circuit analysis using parallel computing approach on Graphics Processing Unit (GPU). Now a day’s high end GPUs are available with sufficient memory in the architecture itself. Circuit processing functions are analysed to search compute intensive functions. Mathematical operations are redesigned so that it will execute in parallel. LU decomposition algorithm and complex math operations are converted in parallel form. Some mathematical operations are simplified to merge them in suitable cluster. Clustering approach is used which helps in finding kernel of uniform operations to map on GPU cores. GPU programming strategies like if-else in-lining, parallel reduction etc are useful in accelerating circuit operations. Use of look up tables in shared memory or constant memory proves to be useful in fast data access. At least 15% speed gain is achieved for operational analysis and 40% for transient analysis of regular circuits.
Keywords: Clustering, GPU (Graphics Processing Unit), LU Decomposition, Parallel Computation, Transient Analysis.
Scope of the Article: Clustering