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• IDCT and FDCT written in Halide. • 200 Lines of Halide replace over 20,000 lines of Assembly Language. • 5-25% faster for decoding. • 10-40% faster for encoding. • Significantly easier to maintain and port to new architectures. The Discrete Cosine Transform (DCT) is commonly used for image and video coding and very efficient implementations of the forward and inverse transforms are of great importance. The popular libjpeg-turbo library contains handwritten, highly-optimised assembly language DCT implementations utilizing SIMD instruction sets for a variety of architectures. We present an alternative approach, implementing the 8x8 IDCT and FDCT written in the functional image processing language Halide. We show how less than 200 lines of Halide can replace over 20,000 lines of code the libjpeg-turbo library to perform JPEG encoding and decoding. The Halide implementation is compared for ARMv8 NEON and x86-64 SIMD extensions and shows a 5–25 percent performance improvement over the SIMD code in libjpeg-turbo for decoding and a 10–40 percent improvement for encoding. The Halide code is significantly easier to maintain and port to new architectures than the existing code. [ABSTRACT FROM AUTHOR]

Summary: A parallel implementation of an agent‐based, artificial life model for graphical processing units (GPUs) is presented. The model represents predator‐prey populations as a collection of agents (animats) who act independently and interact with their environment and each other. Individual animat interactions include avoidance, mating, and hunting which contribute to emergent behavior in the system as a whole such as herd formation and defensive spirals. Computational simulations of the model exploring the behavior of the model are limited in size due to the large populations that may result from certain environments and rule combinations. Parallel methods for computing the model on GPUs and a CUDA implementation are presented and are shown to provide a significant performance improvement over existing sequential implementations without simplifying the model behavior. The reduced simulation run‐time can allow both larger systems to be computed and a wider range of rule combinations that may produce larger populations to be investigated in a feasible amount of time. [ABSTRACT FROM AUTHOR]

Connected-component labelling remains an important and widely-used technique for processing and analysing images and other forms of data in various application areas. Different data sources produce components with different structural features and may be more or less suited to certain connected-component labelling algorithms. Although many efficient serial algorithms exist, determining connected-components on Graphical Processing Units (GPUs) is of interest as many applications use GPUs for processing other parts of the application and labelling on the GPU can avoid expensive memory transfers. The general problem of connected-component labelling is discussed and two existing GPU-based algorithms are discussed—label-equivalence and Komura-equivalence. A new GPU-based, parallel component-labelling algorithm is presented that identifies and eliminates redundant operations in the previous methods for rectilinear two- and three-dimensional datasets. A set of test-cases with a range of structural features and systems sizes is presented and used to evaluate the new labelling algorithm on modern NVIDIA GPU devices and compare it to existing algorithms. The results of the performance evaluation are presented and show that the new algorithm can provide a meaningful performance improvement over previous methods across a range of test cases. [ABSTRACT FROM AUTHOR]