Category Archive

Researchers from UC Berkeley and Lawrence Berkeley National Laboratory credit ArrayFire in a paper published in the 2020 IEEE High-Performance Extreme Computing Conference (HPEC). The paper is titled “GPU Accelerated Anomaly Detection of Large Scale Light Curves.” In this work, the authors present a new algorithm for reconstructing the three-dimensional (3D) electrostatic potential of a sample at atomic resolution from phase contrast imaging using high-resolution transmission electron microscopy. Summary Transmission electron microscopy (TEM) offers various imaging modes, allowing for quantitative 3D estimations of local structure, electrostatic and magnetic potentials, and local chemistry, significantly impacting biology and materials science. It is now possible to measure the 3D position of individual atoms with high precision and even determine both the 3D position and species …

(This is a guest post by Gustavo Stahl from São Paulo State University in Brazil.) Summary Corners present in images are widely used in multiple areas of computer science, such as augmented reality, autonomous vehicles, service robots, 3D reconstructions, object tracking, and many more. To work appropriately, applications in these areas usually rely on fast corner detectors with good-quality extractions. The FOAGDD (First-order Anisotropic Gaussian Direction Derivative) is an algorithmic technique for extracting corners in an image originally proposed by Weichuan Zhang and Changming Sun in 2019. The method surpassed the majority of extractors in corner detection quality but lacked speed, making it improper for real-time applications. Hence, this paper proposes transferring the workload from the original implementation to the …

Researchers from Tsinghua University, the Chinese Academy of Sciences, and David Bader of the New Jersey Institute of Technology credit ArrayFire in a paper published in the 2020 IEEE High-Performance Extreme Computing Conference (HPEC). The paper is titled “GPU Accelerated Anomaly Detection of Large Scale Light Curves.” In this research, light from 200,000 stars is tracked, looking for events of high-mass dark objects that bend light from the source, indicating the discovery of planets and black holes. Summary Microlensing is a unique anomaly that occurs when a lens (or lenses) passes between a light source (star) and an observer (Earth). These lenses are high-mass objects that bend the light from the source. This anomaly is helpful in the detection of “dark” objects. …

Researchers from the University Bordeaux credit ArrayFire in a paper published in a Master’s Thesis by André Almeida. The thesis is titled “Soliton Excitations in Open Systems using GPGPU Supercomputing.” It investigates the stability of nonlinear excitations in open optical systems modeled by the Complex Ginzburg Landau Equation when influenced by effects such as dissipation and gain, using numerical simulations. Summary In the early years of the 19th century the naval engineer James Scott Russell made the first observation of a very uniform accumulation of water in a boat canal that was capable to propagate for many kilometers without any losses in amplitude and with constant width. This was a very strange phenomenon at the time because no known description of hydrodynamics …

Guest post by William Tambellini of RWS Language Weaver. This post shows how RWS Language Weaver, a comprehensive and adaptable neural machine translation platform, uses ArrayFire to run AI algorithms at scale. Language Weaver provides secure enterprise machine translation solutions adapted to client content – empowering you to communicate without language barriers. Language is often a barrier to clear communication with internal and external stakeholders. For governments, Language Weaver brings a global perspective into an analytics pipeline integrating with content intelligence applications to minimize the effort required to translate multilingual content. For global enterprises, Language Weaver can help you improve collaboration between teams, increase productivity, and go to market faster internationally. For legal and compliance teams, Language Weaver manages multilingual …

Researchers from the University Bordeaux in France credit ArrayFire in a paper published in ICPR 2020’s workshop on Explainable Deep Learning for AI. The paper is titled “Samples Classification Analysis Across DNN Layers with Fractal Curves.” It provides a tool for visualizing where the deep neural network starts to be able to discriminate the classes. Summary Deep neural networks (DNN) are becoming the prominent solution when using machine learning models. However, they suffer from a black-box effect that complicates their inner workings interpretation and thus the understanding of their successes and failures. Information visualization is one way, among others, to help in their interpretability and hypothesis deduction. This paper presents a novel way to visualize atrained DNN to depict at the same …

Researchers from the University of Central Florida credit ArrayFire in a paper published in IEEE Transactions on Signal Processing. The paper is titled “An Exact and Fast Computation of Discrete Fourier Transform for Polar and Spherical Grid” and provides the first exact and fast solution to the problem of obtaining discrete Fourier transform for polar and spherical grids. This paper is fully reproducible on Github. Summary Numerous applied problems of two-dimensional (2-D) and 3-D imaging are formulated in the continuous domain. They emphasize obtaining and manipulating the Fourier transform in polar and spherical coordinates. However, translating continuum ideas with discretely sampled data on a Cartesian grid is problematic. There exists no exact and fast solution to the problem of obtaining discrete Fourier …

Researchers from the Catalan Telecommunications Technology Centre in Spain credit ArrayFire in a paper published in the Applied Soft Computing Journal. The paper is titled “A GPU fully vectorized approach to accelerate performance of NSGA-2 based on stochastic non-domination sorting and grid-crowding” and showcases ArrayFire accelerating decision space exploration for multi-objective optimization problems. Summary This work introduces an accelerated implementation of NSGA-2 on a graphics processing unit (GPU) to reduce execution time. Parallelism is achieved at the population level using vectorization. All the algorithm components are run on the device, minimizing communication overhead. New stochastic versions of both non-domination sorting and crowding are introduced in the article. They are designed to be efficiently vectorized on GPU; therefore, the proposed approach is finally …