Evaluation of Java for General Purpose GPU Computing

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/WAINA.2013.234[Abstract] The presence of many-core units as accelerators has been increasing due to their ability to improve the performance of highly parallel workloads. General Purpose GPU(GPGPU) computing has allowed the graphical units to emerge as successful co-processors that can be employed to improve the performance of many different non-graphical applications with high parallel requirements, which make them suitable for many High Performance Computing workloads. While the main libraries developed to exploit the massive parallel capacity of GPUs are oriented to C/C++ programmers, there have been several efforts to extend this support to other languages. Among them, Java stands out for being one of the most extended languages and there are multiple projects that try to enable Java to take advantage of GPGPU computing. In this scenario, this paper presents an evaluation of the most relevant among the current solutions that exploit GPGPU computing in Java.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación y Ciencia: FPU AP2010-4348Xunta de Galicia; CN2012/21

EVACUATION ROUTE MODELING AND PLANNING WITH GENERAL PURPOSE GPU COMPUTING

This work introduces a bilevel, stochastic optimization problem aimed at robust, regional evacuation network design and shelter location under uncertain hazards. A regional planner, acting as a Stackelberg leader, chooses among evacuation-route contraflow operation and shelter location to minimize the expected risk exposure to evacuees. Evacuees then seek an equilibrium with respect to risk exposure in the lower level. An example network is solved exactly with a strategy that takes advantage of a fast, low-memory, equilibrium algorithm and general purpose computing on graphical processing units

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

We reported initial work towards a new fast and scalable scientific visualization technology that leverages the Vulkan API to achieve unprecedented performance through GPUs. This technology is implemented in a C/C++ library called Datoviz that offers an intermediate-level API for scientific visualization libraries and software. Datoviz provides a unified graphics stack for 2-D, 3-D, graphical user interfaces, and natively supports efficient interactions between rendering and general-purpose GPU computing. A major direction of development is to investigate the integration of Datoviz as a low-level backend of a future version of VisPy, a popular Python scientific plotting librar

Simulating Spiking Neural P systems without delays using GPUs

We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel nature of SNP systems necessitate the use of hardware intended for parallel computations. The simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using an SNP system that generates all numbers in $\mathbb N$ - {1} are discussed, as well as recommendations for future work.Comment: 19 pages in total, 4 figures, listings/algorithms, submitted at the 9th Brainstorming Week in Membrane Computing, University of Seville, Spai

GPU-Based Volume Rendering of Noisy Multi-Spectral Astronomical Data

Traditional analysis techniques may not be sufficient for astronomers to make the best use of the data sets that current and future instruments, such as the Square Kilometre Array and its Pathfinders, will produce. By utilizing the incredible pattern-recognition ability of the human mind, scientific visualization provides an excellent opportunity for astronomers to gain valuable new insight and understanding of their data, particularly when used interactively in 3D. The goal of our work is to establish the feasibility of a real-time 3D monitoring system for data going into the Australian SKA Pathfinder archive. Based on CUDA, an increasingly popular development tool, our work utilizes the massively parallel architecture of modern graphics processing units (GPUs) to provide astronomers with an interactive 3D volume rendering for multi-spectral data sets. Unlike other approaches, we are targeting real time interactive visualization of datasets larger than GPU memory while giving special attention to data with low signal to noise ratio - two critical aspects for astronomy that are missing from most existing scientific visualization software packages. Our framework enables the astronomer to interact with the geometrical representation of the data, and to control the volume rendering process to generate a better representation of their datasets.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

International audienceWe reported initial work towards a new fast and scalable scientific visualization technology that leverages the Vulkan API to achieve unprecedented performance through GPUs. This technology is implemented in a C/C++ library called Datoviz that offers an intermediate-level API for scientific visualization libraries and software. Datoviz provides a unified graphics stack for 2-D, 3-D, graphical user interfaces, and natively supports efficient interactions between rendering and general-purpose GPU computing. A major direction of development is to investigate the integration of Datoviz as a low-level backend of a future version of VisPy, a popular Python scientific plotting library

Calculation of HELAS amplitudes for QCD processes using graphics processing unit (GPU)

We use a graphics processing unit (GPU) for fast calculations of helicity amplitudes of quark and gluon scattering processes in massless QCD. New HEGET ({\bf H}ELAS {\bf E}valuation with {\bf G}PU {\bf E}nhanced {\bf T}echnology) codes for gluon self-interactions are introduced, and a C++ program to convert the MadGraph generated FORTRAN codes into HEGET codes in CUDA (a C-platform for general purpose computing on GPU) is created. Because of the proliferation of the number of Feynman diagrams and the number of independent color amplitudes, the maximum number of final state jets we can evaluate on a GPU is limited to 4 for pure gluon processes ($gg\to 4g$), or 5 for processes with one or more quark lines such as $q\bar{q}\to 5g$ and $qq\to qq+3g$. Compared with the usual CPU-based programs, we obtain 60-100 times better performance on the GPU, except for 5-jet production processes and the $gg\to 4g$ processes for which the GPU gain over the CPU is about 20

DO-178C certification of general-purpose GPU software: review of existing methods and future directions

—General-Purpose GPU software is considered for use in avionics to satisfy the increased computational requirements of future systems. Therefore, it needs to be certified following the DO-178C guidance as all airborne software. In this work, we review the existing methods in the literature, we analyse their advantages and disadvantages, and we discuss how they can be combined to obtain certification with lower effort and cost. Our focus is restricted on application-level software, under the premise that successful completion of verification of avionics graphics GPU software products has been demonstrated, so their GPU compiler has been considered acceptable for these already DO-178C certified products, or existing qualified GPU compilers exist. Finally, we discuss upcoming solutions for certified general purpose GPU computing .This work was performed within the Airbus TANIAGPU Project ADS (E/200) in collaboration with the project partners Airbus Defence and Space, Madrid, Spain and CoreAVI, Canada. It was also partially supported by the European Space Agency (ESA) through the GPU4S (GPU for Space) activity, the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB and FJCI-2017-34095 (Spanish State Research Agency / http://dx.doi.org/10.13039/501100011033) and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft