Acceleration of Blender Cycles Path-Tracing Engine Using Intel Many Integrated Core Architecture

Jaroš, M.; Řı́ha, Lubomı́r; Strakoš, Petr; Karásek, Tomáš; Vašatová, Alena; Jarošová, Marta; Kozubek, Tomáš

doi:10.1007/978-3-319-24369-6_7

Cited by 4 publications

(2 citation statements)

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“…A typical example is a user that locally collects a large amount of data from a scientific experiment or a mobile device and wants to perform some heavy computation on it [30][31][32]. Another good use-case for OmpCloud could be the rendering engine of 3D modeling software (e.g., Blender [33]), which requires large amounts of computation to render a big movie project [34]. The rendering could be offloaded to the cloud cluster while the resulting movie would be displayed at the designer desktop computer.…”

Section: Application Domain and Programming Modelmentioning

confidence: 99%

Cluster Programming using the OpenMP Accelerator Model

Yviquel

Cruz

Araújo

2018

ACM Trans. Archit. Code Optim.

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Computation offloading is a programming model in which program fragments (e.g., hot loops) are annotated so that their execution is performed in dedicated hardware or accelerator devices. Although offloading has been extensively used to move computation to GPUs, through directive-based annotation standards like OpenMP, offloading computation to very large computer clusters can become a complex and cumbersome task. It typically requires mixing programming models (e.g., OpenMP and MPI) and languages (e.g., C/C++ and Scala), dealing with various access control mechanisms from different cloud providers (e.g., AWS and Azure), and integrating all this into a single application. This article introduces computer cluster nodes as simple OpenMP offloading devices that can be used either from a local computer or from the cluster head-node. It proposes a methodology that transforms OpenMP directives to Spark runtime calls with fully integrated communication management, in a way that a cluster appears to the programmer as yet another accelerator device. Experiments using LLVM 3.8, OpenMP 4.5 on well known cloud infrastructures (Microsoft Azure and Amazon EC2) show the viability of the proposed approach, enable a thorough analysis of its performance, and make a comparison with an MPI implementation. The results show that although data transfers can impose overheads, cloud offloading from a local machine can still achieve promising speedups for larger granularity: up to 115× in 256 cores for the 2MM benchmark using 1GB sparse matrices. In addition, the parallel implementation of a complex and relevant scientific application reveals a 80× speedup on a 320 core machine when executed directly from the headnode of the cluster. CCS Concepts: • Computing methodologies → Distributed programming languages; • Software and its engineering → Parallel programming languages; Distributed programming languages; Runtime environments; Source code generation;

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Section: Application Domain and Programming Modelmentioning

confidence: 99%

Cluster Programming using the OpenMP Accelerator Model

Yviquel

Cruz

Araújo

2018

ACM Trans. Archit. Code Optim.

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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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