Intel(r) Ct Technology provides a generalized data parallel programming solution, intended to free high-performance application developers from dependencies on particular hardware architectures. It produces scalable, portable, and deterministic parallel implementations from a single-source high-level specification of a computation. Ct Technology harvests both short vector (SIMD) and thread-level parallelism from a single specification of latent parallelism, and provides portability across multiple current and future processor architectures. In particular, it can adapt to differences in instruction sets, vector widths, core counts, and cache architecture. Its high-level, high-productivity C++ API is intended for applications that require data-intensive mathematical computations such as those found in computer graphics, digital content creation, medical imaging, financial analytics, seismic reconstruction, simulation, data mining, and other science and engineering applications. In this talk I will present the basics of the Ct Technology and supporting tools and then walk through some example applications. Although Ct Technology provides a high-level C++ interface, we will also present information on a low-level interface to the Ct virtual machine being made available for purposes such as supporting domain-specific languages and alternative frontends.
Bio: Michael McCool has degrees in Computer Engineering (University of Waterloo, BASc) and Computer Science (University of Toronto, M.Sc. and PhD.) with specializations in mathematics (BASc) and biomedical engineering (MSc) as well as computer graphics and parallel computing (MSc, PhD). He has research and application experience in the areas of data mining, computer graphics (specifically sampling, rasterization, texture hardware, antialiasing, shading, illumination, and visualization), medical imaging, signal and image processing, financial analysis, and languages and programming platforms for high productivity parallel computing. In order to commercialize research work into many-core computing platforms done while he was a professor at the University of Waterloo, in 2004 he co-founded RapidMind, which in 2009 was acquired by Intel. Currently he is a Software Architect with Intel and an Adjunct Associate Professor with the University of Waterloo.