Advances in high-performance computing have led to the broad use of computational studies in everyday engineering and scientific applications. A single study may require thousand...
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel ...
Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai...
We revisit and use the dependence transformation method to generate parallel algorithms suitable for cluster and grid computing. We illustrate this method in two applications: to o...
Ulisses Kendi Hayashida, Kunio Okuda, Jairo Panett...
The assumption of maximum parallelism support for the successful realization of scalable quantum computers has led to homogeneous, "sea-of-qubits" architectures. The res...
Darshan D. Thaker, Tzvetan S. Metodi, Andrew W. Cr...
We present an approach to parallel variational optical flow computation on standard hardware by domain decomposition. Using an arbitrary partition of the image plane into rectangul...