A system for interactive modeling of physical curved surface objects

England, J. N.

doi:10.1145/965139.807412

Cited by 10 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of computer graphics hardware for general‐purpose computation has been an area of active research for many years, beginning on machines like the Ikonas [Eng78], the Pixel Machine [PH89], and Pixel‐Planes 5 [FPE*89]. Pixar's Chap [LP84] was one of the earliest processors to explore a programmable SIMD computational organization, on 16‐bit integer data; Flap [LHPL87], described three years later, extended Chap's integer capabilities with SIMD floating‐point pipelines.…”

Section: Gpgpu Applicationsmentioning

confidence: 99%

A Survey of General‐Purpose Computation on Graphics Hardware

Owens¹,

Luebke

Govindaraju³

et al. 2007

Computer Graphics Forum

1,575

979

View full text Add to dashboard Cite

show abstract

Section: Gpgpu Applicationsmentioning

confidence: 99%

A Survey of General‐Purpose Computation on Graphics Hardware

Owens¹,

Luebke

Govindaraju³

et al. 2007

Computer Graphics Forum

1,575

979

View full text Add to dashboard Cite

show abstract

“…While use of computer graphics hardware for general-purpose computation has been an area of active research for many years (eg [2], [3] [19]), the wide deployment of GPUs in the last several years has resulted in an increase in experimental research with graphics hardware. Some notable work include password cracking [4], artificial neural networks [5], solving partial differential equations (PDEs) [6], line integral convolution and Lagrangian-Eulerian advection [7,8,9], and protein folding (Folding@Home) [10].…”

Section: Gpgpusmentioning

confidence: 99%

Emerging Computing Technologies in High Energy Physics

Farbin

2009

Preprint

View full text Add to dashboard Cite

While in the early 90s High Energy Physics (HEP) lead the computing industry by establishing the HTTP protocol and the first web-servers, the long time-scale for planning and building modern HEP experiments has resulted in a generally slow adoption of emerging computing technologies which rapidly become commonplace in business and other scientific fields. I will overview some of the fundamental computing problems in HEP computing and then present the current state and future potential of employing new computing technologies in addressing these problems.

show abstract

“…⇤ GPGPU computing can be nominally traced back to 1978 with the Ikonas Graphics Systems [England, 1978], but GPGPU programming on modern GPUs began around 1999-2000 when NVIDIA released the GeForce 256 [Wu and Liu, 2008].…”

Section: Computingmentioning

confidence: 99%

A Data and Contention Aware Approach to Dynamic Scheduling for Heterogeneous Processors

Gregg

View full text Add to dashboard Cite

Heterogeneous computers, with a multi-core central processing unit (CPU) and one or more many-core graphical processing units (GPUs) capable of running general purpose applications, are becoming standard on the desktop and in cluster and supercomputing platforms. Using programming languages and extensions such as CUDA or OpenCL, application developers can write code that can run on any of the heterogeneous devices available on the system. As these tools mature, contention for device resources will become more prevalent, especially in systems that utilize an application queue. Current application scheduling methods only consider single applications and preferentially schedule each application for a device regardless of other applications in the queue. This degrades overall computational throughput, and leads to device underutilization. Scheduling application kernels across all heterogeneous components to maximize application throughput is nontrivial and requires the scheduler to have knowledge of the location of data that will be needed for each kernel, the state of the system when each kernel will be launched, and the scheduler be able to predict runtimes for individual applications. Furthermore, a scheduler can utilize historical information about prior kernel run times to further influence its decision. This dissertation investigates these elements of heterogeneous scheduling and develops a novel methodology for e ciently making dynamic scheduling decisions that maximize computational throughput when multiple applications are run on a heterogeneous computer. This dissertation describes an innovative taxonomy for describing data transfer requirements i Abstract ii for a heterogeneous application, and it also shows that dynamic scheduling for heterogeneous computers benefits from knowledge about system state information, including data locality and contention among running processes. Finally, this dissertation investigates a novel technique to increase GPU throughput by running concurrent kernels on a device, based upon their orthogonal use of device resources.

show abstract

A system for interactive modeling of physical curved surface objects

Cited by 10 publications

References 8 publications

A Survey of General‐Purpose Computation on Graphics Hardware

A Survey of General‐Purpose Computation on Graphics Hardware

Emerging Computing Technologies in High Energy Physics

A Data and Contention Aware Approach to Dynamic Scheduling for Heterogeneous Processors

Contact Info

Product

Resources

About