Shadertoy

Jeremias, Pol; Quilez, Inigo

doi:10.1145/2659467.2659474

Cited by 3 publications

(3 citation statements)

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“…The first compiles the entered code to a compute shader to process or generate arbitrary data. The second node compiles to a fragment shader that generates an arbitrary 2D texture, with limited compatibility to Shadertoy [17]. Other nodes that do not perform computation can act as data sources or sinks, although there is no clear separation between the two roles.…”

Section: Methodsmentioning

confidence: 99%

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Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data

Malek

Sensen

2018

International Journal of Biomedical Imaging

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Objective We have created an open-source application and framework for rapid GPU-accelerated prototyping, targeting image analysis, including volumetric images such as CT or MRI data. Methods A visual graph editor enables the design of processing pipelines without programming. Run-time compiled compute shaders enable prototyping of complex operations in a matter of minutes. Results GPU-acceleration increases processing the speed by at least an order of magnitude when compared to traditional multithreaded CPU-based implementations, while offering the flexibility of scripted implementations. Conclusion Our framework enables real-time, intuition-guided accelerated algorithm and method development, supported by built-in scriptable visualization. Significance This is, to our knowledge, the first tool for medical data analysis that provides both high performance and rapid prototyping. As such, it has the potential to act as a force multiplier for further research, enabling handling of high-resolution datasets while providing quasi-instant feedback and visualization of results.

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Section: Methodsmentioning

confidence: 99%

“…Specifically, we want to be able to see results immediately, even while editing the source code. This concept is used today, for example, for entertainment in the so-called Demoscene [17–19]. We expect this approach to also enable intuition-driven development in the domain of medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data

Malek

Sensen

2018

International Journal of Biomedical Imaging

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“…Examples include the OpenML project (Vanschoren et al., 2014), the Model Zoo project (Fenoy et al., 2013), the PyTorch hub (Paszke et al., 2019), and the model repository of TensorFlow (Abadi et al., 2016). In the computer vision domain, the Shadertoy platform offers an algorithm repository in which each item can be modified online and the execution results can be seen immediately (Jeremias & Quilez, 2014). These works are instructive for the sharing of model knowledge.…”

Section: Introductionmentioning

confidence: 99%

Construction of an open knowledge framework for geoscientific models

Xu,

Yue,

Chen

et al. 2024

Transactions in GIS

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Geoscientific models have rapidly developed in recent decades as effective tools to understand the past, perceive the present, and predict the future. However, with the increasing number of models available, discovering suitable ones and applying them properly in problem‐solving situations has become more challenging. Existing materials describing geoscientific models (e.g., articles, manuals, handbooks, metadata documents, and web pages) are scattered and varied in structure and content. To help model users from different disciplinary backgrounds find, access, implement, and reuse models more conveniently, we propose an open knowledge framework for geoscientific models. The knowledge framework includes three levels: a resource level for indicating where to find a model, a connection level for indicating what materials are related to a model, and an application level for indicating how the model is applied. Through such a three‐level framework, model users can collaboratively provide descriptive information for a model, link different materials to a model (e.g., data, references, and tools), and contribute experiences regarding model application in practical cases (as reusable solutions). Thus, a web‐based community can be formed to facilitate the better use of geoscientific models. This article introduces the Open Geographic Modeling and Simulation System (OpenGMS) as the implementation of this open knowledge framework. Case studies are given to showcase the effectiveness and capability of the proposed framework.

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