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OpenMP is an implementation program\udinterface that might be utilized to explicitly immediate\udmulti-threaded and it shared memory parallelism.\udOpenMP platform for specifications multi-processing via\udconcurrent work between interested parties of hardware\udand software industry, governments and academia.\udOpenMP is not needs implemented identically by all\udvendors and it is not proposed for distributed memory\udparallel systems by itself. In order to invert a matrix,\udthere are multiple approaches. The proposed LU\uddecomposition calculates the upper and lower triangular\udvia Gauss elimination method. The computation can be\udparallelized using OpenMP technology. The proposed\udtechnique main goal is to analyze the amount of time\udtaken for different sizes of matrices so we used 1 thread,\ud2 threads, 4 threads, and 8 threads which will be\udcompared against each other to measure the efficiency of\udthe parallelization. The result of interrupting compered\udthe amount of time spent in all the computing using 1\udthread, 2 threads, 4 threads, and 8 threads. We came up\udwith if we raise the number of threads the performance\udwill be increased (less amount of time required). If we\uduse 8 threads we get around 64% performance gained.\udAlso as the size of matrix increases, the efficiency of\udparallelization also increases, which is evident from the\udtime difference between serial and parallel code. This is\udbecause, more computations are done parallel and hence\udthe efficiency is high. Schedule type in OpenMP has\uddifferent behavior, we used static, dynamic, and guided\udschem

Inverse Matrix using Gauss Elimination Method by OpenMP