Graphics

About: Graphics is a research topic. Over the lifetime, 17394 publications have been published within this topic receiving 411468 citations. The topic is also known as: graphic.

Data Analysis and Graphics Using R: An Example-Based Approach

Abstract: (2010). Data Analysis and Graphics Using R: An Example-Based Approach. Journal of Quality Technology: Vol. 42, No. 4, pp. 417-417.

System and method for accelerating and optimizing the processing of machine learning techniques using a graphics processing unit

Abstract: A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.

Multilocation video conference terminal including video switching contention control

Abstract: In a multilocation video conference system, contention for the video to be transmitted to the locations is resolved by employing so-called talker and graphics contention resolution processes Both the talker and graphics contention processes may be overridden by manual selection of the video to be viewed at each location In the talker contention process, the video to be transmitted is not switched until one and only one talker location is detected during a predetermined talker timing interval In the graphics contention process, all requests for graphics transmission are rejected until one and only one graphics transmission request is detected during a predetermined graphics request timing interval Once transmission of graphics has been assigned to a location, all other locations in the conference are transmitted the graphics video information until the location displaying graphics releases from the graphics transmission mode or there is a manual override The location transmitting graphics information is transmitted the video from the last of the other locations selected for talker video transmission

Finite Difference Time Domain (FDTD) Simulations Using Graphics Processors

Abstract: This paper presents a graphics processor based implementation of the Finite Difference Time Domain (FDTD), which uses a central finite differencing scheme for solving Maxwell's equations for electromagnetics. FDTD simulations can be very computationally expensive and require thousands of CPU hours to solve on traditional general purpose processors. Modern Graphics Processing Units (GPUs) found in desktop computers are programmable and are capable of much higher vector floating-point performance than general purpose CPUs. This paper shows how GPUs can be used to greatly speedup FDTD simulations. The main objective is to leverage GPU processing power for FDTD update calculations and complete computationally expensive simulations in reasonable time. This allows researchers to simulate much longer pulse lengths and larger models than was possible in the past. A new FDTD code was developed to leverage graphics processors using Linux, C, OpenGL, Cg, and commodity GeForce 7 series GPUs. The graphics hardware was accessed through standard OpenGL. The FDTD model space was then transferred to the GPU device memory through OpenGL textures and host readable via frame buffer objects exposed by the OpenGL 2.0 application programming interface (API). GPU fragment processors were utilized for the FDTD update computations via Cg fragment programs. For models that were sufficiently large, greater than (140)3 cells, the GPU performed FDTD update calculations at least 12 times faster than the execution of the same simulation on a contemporary multicore CPU from Intel or AMD. The use of GPUs shows great promise for high performance computing applications like FDTD that have high arithmetic intensity and limited or no data dependencies in computation streams. Until recently, to use GPUs as a co-processor, the normalCPU-based code needed to be rewritten extensively using special graphics programming language Cg and OpenGL APIs, which is difficult for non-graphics programmers. However, newer GPUs, such as NVIDIA's G80, provide unified shaders models for programming GPU processing elements and APIs that allow compiler tools to allow direct programming of graphics hardware without extra intermediate graphics programming with OpenGL and Cg. Currently, a message passing interface-based parallel GPU FDTD code is being developed and benchmarked on a cluster of G80 GPUs.

R Marries NetLogo: Introduction to the RNetLogo Package

Abstract: The RNetLogo package delivers an interface to embed the agent-based modeling platform NetLogo into the R environment with headless (no graphical user interface) or interactive GUI mode. It provides functions to load models, execute commands, push values, and to get values from NetLogo reporters. Such a seamless integration of a widely used agent-based modeling platform with a well-known statistical computing and graphics environment opens up various possibilities. For example, it enables the modeler to design simulation experiments, store simulation results, and analyze simulation output in a more systematic way. It can therefore help close the gaps in agent-based modeling regarding standards of description and analysis. After a short overview of the agent-based modeling approach and the software used here, the paper delivers a step-by-step introduction to the usage of the RNetLogo package by examples.