Linear search, Bubble sort, merge sort, and matrix multiplication programs are included in an attempt to highlight the difference in execution between single loop, variable length array and nested loops with one and two dimensional arrays. We propose a method that finds heuristic for different types of programming constructs with loops and arrays. It is so because the number of paths may increase exponentially with the increase of array size for some programming constructs, like merge sort. Automated test data generation for programs having loops with variable number of iteration and variable length array is a challenging problem. Automated test data generation devotes special attention to loops for better coverage. Normally, program execution spends most of the time on loops. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a groundbreaking role in faster, more efficient execution of even the most complicated machine learning tasks. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. We document an efficient distributed matrix multiplication using Cannon's algorithm, which improves significantly on the performance of the existing MLlib implementation. By combining distributed message passing (using asynchronous network IO), OpenJDK's new auto-vectorization and Spark's barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon's distributed matrix multiplication to Spark. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. In Spark, a task in a stage doesn't depend on any other tasks in the same stage, and hence it can be scheduled independently. The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow.

Matrix multiplication verilog