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This Letter presents a new algorithmic method developed to evaluate two-electron repulsion integrals based on contracted Gaussian basis functions in a parallel way. This new algorithm scheme provides distinct SIMD (single instruction multiple data) optimized paths which symbolically transforms integral parameters into target integral algorithms. Our measurements indicate that the method gives a significant improvement over the CPU-friendly PRISM algorithm. The benchmark tests (evaluation of more than 10 8 integrals using the STO-3G basis set) of our GPU (NVIDIA GTX 780) implementation showed up to 750-fold speedup compared to a single core of Athlon II X4 635 CPU. [ABSTRACT FROM AUTHOR]

A graphical environment for CNN algorithm development is presented. The new generation of graphical cards with many general purpose processing units introduces the massively parallel computing into PC environment. UniversalMachine on Flows- (UMF) like notation, highlighting image flows and operations, is a useful tool to describe image processing algorithms. This documentation step can be turned into modeling using our framework backed with MATLAB Simulink and the power of a video card. This latter relatively cheap extension enables a convenient and fast analysis of CNN dynamics and complex algorithms. Comparison with other PC solutions is also presented. For single template execution, our approach yields run times 40x faster than that of the widely used Candy simulator. In the case of simpler algorithms, real-time execution is also possible. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future. This article is characterized under:Software > Simulation MethodsComputer and Information Science > Computer Algorithms and ProgrammingMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods [ABSTRACT FROM AUTHOR]