Autocorrelation problems are a rich source of extremely hard computational challenges for which conventional parallel computing has been the only reasonably successful approach. Sequences with constant autocorrelation are called complementary and have a wide range of applications, including: Coding Theory, Telecommunications, Image Compression, and Wireless Communication Protocols.

Over the past decade, the PI has been able to find several new complementary sequences in a series of papers with his collaborators in Canada, the United States, Australia and Europe. It has become apparent, though, that the current algorithms have reached a point of saturation, in that they are unsuitable for producing new results. The advent of hardware accelerator technologies such as the GPU is a promising new direction. In some problems GPU‐enabled algorithms have been reported to exhibit a 2000‐fold speedup, which is quite significant. Therefore, it is clear that hardware accelerator technologies provide vast opportunities for innovation in scientific computing. It is a fortuitous coincidence that our partner, Maplesoft, based in Waterloo Ontario, has recently devoted a large part of their efforts in producing parallel versions of Maple, the flagship Canadian mathematical software product. Our proposal will benefit and complement the efforts of Maplesoft.

Industry Partner(s):Maplesoft Inc.

Academic Institution:Wilfrid Laurier University

Academic Researcher: Ilias Kotsireas

Co-PI Name: Dragomir Djokovic

Focus Areas: Cybersecurity

Platforms: Parallel CPU