Vector Models for Data-Parallel Computing

TL;DR: A fast sorting algorithm for the Connection Machine Supercomputer model CM-2 is developed and it is shown that any U(lg n)-depth family of sorting networks can be used to sort n numbers in U( lg n) time in the bounded-degree fixed interconnection network domain.

TL;DR: The VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

TL;DR: This paper presents the design and implementation of three key features of Futhark that seek a suitable middle ground with imperative approaches and presents a flattening transformation aimed at enhancing the degree of parallelism.

TL;DR: This work investigates a method of dividing an irregular mesh into equal-sized pieces with few interconnecting edges that exploits the geometric coordinates of the mesh vertices, based on theoretical work of Miller, Teng, Thurston, and Vavasis.

TL;DR: A comparative performance evaluation of three different parallel sorting algorithms: bitonic sort, sample sort, and parallel radix sort shows that the relative performance of the algorithms differed on the various machines.

TL;DR: The flattening transformation is extended to the full higher-order case, including lambda abstractions and data parallel arrays of functions, and closure conversion is used before flattening to introduce array closures to represent arrays of functional values.

TL;DR: The model, based on the LogGP model, has been extended to be able to deal with heterogeneous parallel systems and its scalar parameters have been replaced by vector and matrix parameters to take into account the different node's features.

TL;DR: This paper presents a new formalization of the flattening transformation in a calculational style that is easily verified and provides a good starting point for the development of new optimizations.