Bhowmik, B.Kumar, S.Raju, S.R.Prakash, A.Mense, O.2026-02-0620242024 IEEE 21st India Council International Conference, INDICON 2024, 2024, Vol., , p. -https://doi.org/10.1109/INDICON63790.2024.10958524https://idr.nitk.ac.in/handle/123456789/29180Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks. © 2024 IEEE.HMMsMCDOpenMPVDPWSNs