Scrum is a well-known agile model due to its strong management practices. It can be mingled with many software development models such as extreme programming (XP), Agile Unified Process, and Feature Driven Development (FDD). Lean is a very popular known process in the automobile industry due to its effective practices such as Kanban bard and smooth workflow. Lean development is gaining popularity in the software industry from the last few years. Lean development is rational, convenient, responsive, and team-based and it adds value to the enterprise. Scrum is useable and practical for small and medium projects but it does not render positive support for the large size projects. In order to adopt Scrum for large size projects, there is a need to integrate Lean and Scrum. It is required to inherit some properties of the Lean into Scrum, without compromising the speed, quality, efficiency, and standards, to accomplish large size projects successfully such as enterprise resource planning (ERP) systems. It is anticipated that the proposed Lean Scrum integration will make it suitable to develop large size projects. The same is accomplished by purposing an integrated LScrum model in this research. The proposed model is validated using a survey to conclude the results. The results of the survey support the proposed integration of Lean and Scrum for the development of large size projects.

OpenMP is an implementation program interface that might be utilized to explicitly immediate multi-threaded and it shared memory parallelism. OpenMP platform for specifications multi-processing via concurrent work between interested parties of hardware and software industry, governments and academia. OpenMP is not needs implemented identically by all vendors and it is not proposed for distributed memory parallel systems by itself. In order to invert a matrix, there are multiple approaches. The proposed LU decomposition calculates the upper and lower triangular via Gauss elimination method. The computation can be parallelized using OpenMP technology. The proposed technique main goal is to analyze the amount of time taken for different sizes of matrices so we used 1 thread, 2 threads, 4 threads, and 8 threads which will be compared against each other to measure the efficiency of the parallelization. The result of interrupting compered the amount of time spent in all the computing using 1 thread, 2 threads, 4 threads, and 8 threads. We came up with if we raise the number of threads the performance will be increased (less amount of time required). If we use 8 threads we get around 64% performance gained. Also as the size of matrix increases, the efficiency of parallelization also increases, which is evident from the time difference between serial and parallel code. This is because, more computations are done parallel and hence the efficiency is high. Schedule type in OpenMP has different behavior, we used static, dynamic, and guided scheme.