Rendering in Blender Cycles using MPI and Intel® Xeon Phi™

Jaroš, M.; Řı́ha, Lubomı́r; Karásek, Tomáš; Strakoš, Petr; Krpelik, Daniel

doi:10.1145/3110224.3110236

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…Path tracing is a type of ray tracing technique used to simulate the physical behavior of light. This family of algorithms is based on approximating the solution to the rendering equation, which describes the light propagation in a scene via the following integral equation [ 30 ]:

where

is the space variable,

is the direction of the outgoing ray of light,

is the total spectral radiance directed from the point x along the direction

is emitted spectral radiance,

is the unit hemisphere in direction of normal vector

is the direction of the incoming ray of light,

is spectral radiance coming inward to

from the direction

and

is the bidirectional reflectance distribution function (the proportion of light reflected from the direction

).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…As solving Equation ( 1 ) is a computationally extensive task, it is common to approximate the integral in Equation ( 1 ) by utilizing the Monte Carlo simulation as follows [ 30 ]:

where

is an arbitrary function and X is a set consisting of N samples uniformly distributed in

. In order to improve the convergence speed of this method, path tracing techniques apply a methodology based on deterministic sampling, called the Quasi-Monte Carlo method.…”

Section: Materials and Methodsmentioning

confidence: 99%

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Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Palevicius

Pal

Landauskas

et al. 2022

Sensors

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Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.

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where

is the space variable,

is the direction of the outgoing ray of light,

is the total spectral radiance directed from the point x along the direction

is emitted spectral radiance,

is the unit hemisphere in direction of normal vector

is the direction of the incoming ray of light,

is spectral radiance coming inward to

from the direction

and

is the bidirectional reflectance distribution function (the proportion of light reflected from the direction

).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…As solving Equation ( 1 ) is a computationally extensive task, it is common to approximate the integral in Equation ( 1 ) by utilizing the Monte Carlo simulation as follows [ 30 ]:

where

is an arbitrary function and X is a set consisting of N samples uniformly distributed in

. In order to improve the convergence speed of this method, path tracing techniques apply a methodology based on deterministic sampling, called the Quasi-Monte Carlo method.…”

Section: Materials and Methodsmentioning

confidence: 99%

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Palevicius

Pal

Landauskas

et al. 2022

Sensors

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“…In our previous work [Jaros et al 2017], we extended the Cycles to support new parallel hardware architectures (e.g., Intel Xeon Phi co-processors and processors) and other HPC technologies (e.g., distributed rendering using MPI). We build on this extended Cycles version, although none of these extensions is needed for the work presented here.…”

Section: Extensions For Multi-gpu Supportmentioning

confidence: 99%

GPU Accelerated Path Tracing of Massive Scenes

et al. 2021

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This article presents a solution to path tracing of massive scenes on multiple GPUs. Our approach analyzes the memory access pattern of a path tracer and defines how the scene data should be distributed across up to 16 GPUs with minimal effect on performance. The key concept is that the parts of the scene that have the highest amount of memory accesses are replicated on all GPUs. We propose two methods for maximizing the performance of path tracing when working with partially distributed scene data. Both methods work on the memory management level and therefore path tracer data structures do not have to be redesigned, making our approach applicable to other path tracers with only minor changes in their code. As a proof of concept, we have enhanced the open-source Blender Cycles path tracer. The approach was validated on scenes of sizes up to 169 GB. We show that only 1–5% of the scene data needs to be replicated to all machines for such large scenes. On smaller scenes we have verified that the performance is very close to rendering a fully replicated scene. In terms of scalability we have achieved a parallel efficiency of over 94% using up to 16 GPUs.

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“…OpenMP allows programmers to add an if clause to target pragmas to In our case, the image size and the number of objects in a scene can be considered as parameters to be tested by an if clause to decide between doing offloading to the cloud or not. Some works have studied the offloading of Blender's rendering engine to Xeon Phi platforms using OpenMP [22] and MPI [23]. While those works demonstrate good performance, Xeon Phi are quite an exotic platforms not really available to the majority of users.…”

Section: Related Workmentioning

confidence: 99%

Automatic Ray-Tracer Cloud Offloading in OPENMP

Mortatti

Yviquel

Araújo

2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Rendering an image from a 3D scene requires a large amount of computation which grows exponentially with the complexity of the scene (e.g. number of objects and light sources). With the increasing demand of high definition content, 3D designers need to use high-performance computer systems to keep the rendering time acceptable. Since owning computer clusters is expensive, designers usually rent computing power directly from cloud service providers (e.g. AWS and Azure). However, even though many cloud providers already propose dedicated rendering services, integrating them within the standard workflow of modeling softwares can become a complex and cumbersome task. It typically requires exporting the project from the design software, dealing with various access control mechanisms from different clouds to upload the project, and executing the rendering remotely through command-line. Offloading computation to the cloud is a technique which can considerably simplify such tasks. To achieve that, this paper uses an extension of OpenMP 4.X to eliminate any major interactions with the end-user, while minimizing the complexity of cloud integration and optimizing the design workflow. It applies such approach to a ray-tracing application, a simplified version of the engines used by professional 3D modeling software (e.g. Blender). It automatically offloads the rendering process from the user computer to computer cluster within the Microsoft Azure cloud, brings the resulting images back after the computation ends and displays them directly on the screen of the user computer, thus providing a transparent programming model and good speed-ups over local execution.

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Rendering in Blender Cycles using MPI and Intel® Xeon Phi™

Cited by 10 publications

References 6 publications

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

GPU Accelerated Path Tracing of Massive Scenes

Automatic Ray-Tracer Cloud Offloading in OPENMP

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