Pipeline 3D

Résumé

On appelle pipeline 3D la succession des opérations généralement réalisées par une carte graphique nécessaires au rendu d'un lot de données (maillages et principalement) sur un tampon chromatique, la plupart du temps celui-ci est ensuite affiché à l'écran. Les différentes étapes du pipeline 3D sont détaillées dans le schéma ci-contre. De nos jours, la quasi-totalité des pipelines 3D utilisent la technique du rendu par rastérisation, moins photoréaliste mais beaucoup plus rapide que le ray tracing. De ce fait, les API de rendu 3D par rastérisation telles que DirectX ou OpenGL peuvent tirer parti de l'accélération matérielle fournie par les cartes graphiques. Voir aussi

Moteur 3D
Rastérisation
Carte graphique
Processeur graphique
Mémoire vidéo

Catégorie:3D

Source officielle

https://fr.wikipedia.org/wiki/Pipeline_3D

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Publications associées (8)

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Concepts associés (22)

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Séances de cours associées (8)

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GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons

Anastasia Ailamaki, Eleni Tzirita Zacharatou

Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-in-polygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.

VLDB Endowment Inc.2017

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, ,

The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower domain experts to explore multiple data sets, at different time and space resolutions. Since these systems rely on computationally-intensive spatial aggregation queries that slice and summarize the data over different regions, an important challenge is how to attain interactivity. While traditional pre-aggregation approaches support interactive exploration, they are unsuitable in this setting because they do not support ad-hoc query constraints or polygons of arbitrary shapes. To address this limitation, we have recently proposed Raster Join, an approach that converts a spatial aggregation query into a set of drawing operations on a canvas and leverages the rendering pipeline of the graphics hardware (GPU). By doing so, Raster Join is able to evaluate queries on the fly and at interactive speeds on commodity laptops and desktops. In this demonstration, we showcase the efficiency of Raster Join by integrating it with Urbane and enabling interactive exploration of urban data sets. Demo visitors will interact with Urbane to filter and visualize several urban data sets over multiple resolutions.

2018

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Toward a Verifiable Software Dataplane

Mihai Dobrescu

Software dataplanes are emerging as an alternative to traditional hardware switches and routers, promising programmability and short time to market. These advantages are set against the concern of introducing buggy or under-performing code into the network. We explore whether it is practical to formally prove that a software dataplane satisfies key properties that would ensure smooth network operation. In general, proving properties of real programs remains an elusive goal, but we argue that dataplanes are different: they typically follow a pipeline structure that enables our proposed approach, in which we verify pieces of the code in isolation, then compose the results to reason about the entire dataplane. We preliminarily demonstrate the potential of our approach by applying it on simple Click pipelines and proving that they are crash-free and execute a bounded number of instructions. This takes on the order of minutes, whereas a general-purpose state-of-the-art verifier fails to complete the same task within 12 hours.

2013

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