Temporal Sparse Encoding and Decoding of Arrays in Systems Based on the High Level Architecture Standard

In this thesis, a method for encoding and decoding arrays in systems based on the standard High Level Architecture is presented. High Level Architecture is a standard in the simulation industry, which enables interoperability between different simulation systems. When simulations share specific data with other simulations, they always send all parts of the data. This can become quite inefficient when the data is of an array type and only one or a few of its elements' values have changed. The whole array is always transmitted regardless whether the other simulations in the system need all elements or just the ones that have been modified since the last transmission. Therefore there might be more traffic on the network than needed in these cases. The proposed method, named Temporal Sparse Encoding, only encodes the modified elements when it needs to, plus some additional bytes as overhead, that allows for only sending updated elements. The method is based on the concept of sparse arrays and matrices, and is inspired by the Coordinate format, which uses extra arrays with indices referring to specific elements of interest. In a small simulation system, acting as a testing environment, it is shown how Temporal Sparse Encoding can save both time and above all, bandwidth, when sharing updates. Each test was carried out 10 times and in each test case 1 000 updates were transmitted. In each test case the transmission time was measured and the compression ratio was calculated by dividing the number of bytes in the encoding containing all elements by number of bytes in the encoding containing just the updated ones. The biggest compression ratio was calculated to be 750.13 and came from when 1 out of 1 000 elements were updated and transmitted. The smallest compression ratio was 1.00 and came from all the cases where all the array's elements were updated and transmitted. Some of the conclusions that were made was that the Temporal Sparse Encoding can save up to 33% of the time compared to the standard encoding and that a lot of the transmission time is spent on extracting elements once they have been decoded. These findings suggest that endeavors in optimization should be focused at the language level, specifically on management of data, rather than the transmission of data when there is not a lot of traffic occurring on the network.