GPGPU

(General Purpose computation on GPUs) Using a graphics processing unit (GPU) for general-purpose parallel processing applications rather than rendering images for the screen. For fast results, applications such as sorting, matrix algebra, image processing and physical modeling require multiple sets of data to be processed in parallel. GPUs are also used in desktop computers for improved voice, face and gesture recognition.

The GPU functions as a coprocessor with its own memory that processes many threads simultaneously. For more information, visit www.gpgpu.org. See GPU, CUDA, OpenCL, DirectCompute, PhysX and AMD Fusion.

A Desktop GPGPU Powerhouse

This GV100 GPU from NVIDIA is a PCI Express card that performs 7.4 trillion 64-bit floating point operations per second (7.4 TFLOPS). Its Tensor performance reaches 118.5 TFLOPS (see TensorFlow). (Image courtesy of NVIDIA Corporation.)

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References in periodicals archive

It has been reported that GPGPU is suitable for image processing.

Analysis of Implementing Mobile Heterogeneous Computing for Image Sequence Processing

For our prototype we have used NVidia GPU hardware and CUDA (Compute Unified Device Architecture) to implement GPGPU operations.

Innovative 3D MMO servers architectures based on GPGPU

In recent, various GPGPU (General-purpose computing on Graphic Process Unit) techniques have been introduced, which enable the computation of general-purpose operations to be speed up using GPU in various fields such as computer vision [5-7].

Simulation of Deformable Objects Using GLSL 4.3

None of this would have been possible without the advent of GPGPU technology,” Challa said.

Aust Stealth Co Builds World-First IOT Analytics Platform on NVIDIA Backbone

For multicore SoCs, for example, GPGPU, the user is required to provide explicit computation partitioning among tasks in the CUDA programming language.

Ingredients of adaptability: a survey of reconfigurable processors

Keywords: energy efficiency, parallel processing, CUDA, GPGPU, Kepler, algorithmic function.

Economic considerations regarding the opportunity of optimizing data processing using graphics processing units

Some other areas covered are scheduling periodic real-time tasks with heterogeneous reward requirements, identifying burglars through networked sensor-camera mates, timing analysis of a protected operating system kernel, a responsive GPGPU execution model for runtime engines, and scheduling of certifiable mixed-criticality sporadic task systems.

Real-time systems symposium; proceedings

In our proposed solution, for the implementation we will use NVidia GPU hardware (2x8800 GTX Ultra mounted in the same PC) and the NVidia (CUDA) toolkit to implement Ray Tracing as a GPGPU program.

Ray tracing as multi GPGPU