Advancements in GPGPU computing

The graphics processing unit (GPU) has evolved from configurable graphics processor to a powerful engine for high performance computer. In this paper, we describe the graphics pipeline of GPU, and introduce the history and evolution of GPU architecture. We also provide a summary of software environments used on GPU, from graphics APIs to non-graphics APIs. At last, we present the GPU computing in computational fluid dynamics applications, including the GPGPU computing for Navier-Stokes equations methods and the GPGPU computing for Lattice Boltzmann method.

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IACM: Integrated adaptive cache management for high-performance and energy-efficient GPGPU computing

2016 IEEE 34th International Conference on Computer Design (ICCD) ◽

10.1109/iccd.2016.7753308 ◽

2016 ◽

Cited By ~ 2

Author(s):

Kyu Yeun Kim ◽

Jinsu Park ◽

Woongki Baek

Keyword(s):

Energy Efficient ◽

High Performance ◽

Cache Management ◽

Gpgpu Computing ◽

Adaptive Cache

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MULTIRESOLUTION RENDERING BASED ON GPGPU COMPUTING

International Journal of Computing ◽

10.47839/ijc.12.4.609 ◽

2014 ◽

pp. 298-307

Author(s):

Julián Lamas-Rodríguez ◽

Francisco Argüello ◽

Dora B. Heras

Keyword(s):

Volume Rendering ◽

Texture Mapping ◽

Gpgpu Computing ◽

Visualization Process ◽

Resolution Level ◽

Data Transfers ◽

Multiresolution Rendering ◽

Different Levels

The problem of visualizing large volumetric datasets is appealing for computation on the GPU. Nevertheless, the design of GPU volume rendering solutions must deal with the limited available memory in a graphics card. In this work, we present a system for multiresolution volume rendering which preprocesses the dataset dividing it into bricks and generating a compressed version by applying different levels of compression based on wavelets. The compressed volume is then stored in the GPU memory. For the later visualization process by texture mapping each brick of the volume is decompressed and rendered with a different resolution level depending on its distance to the camera. This approach computes most of the tasks in the GPU, thus minimizing the data transfers among CPU and GPU. We obtain competitive results for volumes of size in the range between 64 and 256.

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