OptiX

Parker, Steven G.; Robison, Austin; Stich, Martin; Bigler, James; Dietrich, Andreas; Friedrich, Heiko; Hoberock, Jared; Luebke, David; McAllister, David; McGuire, Morgan; Morley, Keith

doi:10.1145/1833351.1778803

Cited by 12 publications

(5 citation statements)

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“…Our approach provides high-performance rendering with low memory overhead for up to billions of primitives. Furthermore, our method is general enough to embed into any ray tracing framework, such as Nvidia Optix [PBD*10], or production film renderers such as Cycles [Fou] and Arnold [KCSG18].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, whereas the integration into tools such as ParaView should in theory be simple, any such integration always uncovers at least some missing or mismatched features that may require additional modifications. Eventually, it is also worth considering the broader question of whether it would make sense to add the geometry type and algorithms described in this paper to other ray tracers, such as OptiX [PBD*10], taking advantage of Geforce RTX [nvi], and if so, whether there is a need for some standardization of what exactly a line primitive type would have to support in any given ray tracer.…”

Section: Discussionmentioning

confidence: 99%

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Ray Tracing Generalized Tube Primitives: Method and Applications

Han

Wald

Usher

et al. 2019

Computer Graphics Forum

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We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ray Tracing Generalized Tube Primitives: Method and Applications

Han

Wald

Usher

et al. 2019

Computer Graphics Forum

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Section: Opticks: Gpu Accelerated Optical Photon Simulation Using Nvidia Optixmentioning

confidence: 99%

“…OptiX is a general-purpose ray-tracing framework developed by NVIDIA to give optimal performance on NVIDIA's GPUs [11][12][13][14]. OptiX works on the premise that most ray-tracing algorithms require only a small set of operations [11]. OptiX allows users to build applications to control the generation of rays and their interactions with surfaces [15].…”

Section: Opticks: Gpu Accelerated Optical Photon Simulation Using Nvidia Optixmentioning

confidence: 99%

GPU simulation with Opticks: The future of optical simulations for LZ

et al. 2021

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The LZ collaboration aims to directly detect dark matter by using a liquid xenon Time Projection Chamber (TPC). In order to probe the dark matter signal, observed signals are compared with simulations that model the detector response. The most computationally expensive aspect of these simulations is the propagation of photons in the detector’s sensitive volume. For this reason, we propose to offload photon propagation modelling to the Graphics Processing Unit (GPU), by integrating Opticks into the LZ simulations workflow. Opticks is a system which maps Geant4 geometry and photon generation steps to NVIDIA’s OptiX GPU raytracing framework. This paradigm shift could simultaneously achieve a massive speed-up and an increase in accuracy for LZ simulations. By using the technique of containerization through Shifter, we will produce a portable system to harness the NERSC supercomputing facilities, including the forthcoming Perlmutter supercomputer, and enable the GPU processing to handle different detector configurations. Prior experience with using Opticks to simulate JUNO indicates the potential for speed-up factors over 1000× for LZ, and by extension other experiments requiring photon propagation simulations.

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“…6.2.1 Discussion. We compared the performance of our kernels with state-of-the-art, manually optimized traversal implementations: Embree [Wald et al 2014], a CPU-centric library designed by Intel, and on the GPU, the work of Aila et al [Aila and Laine 2009;Aila et al 2012], used within the OptiX [Parker et al 2010] library developed by NVIDIA. On the CPU, profiling indicates that most of the performance difference can be attributed to excessive register spilling, and as a result, increased memory traffic.…”

Section: Ray Tracingmentioning

confidence: 99%

AnyDSL: a partial evaluation framework for programming high-performance libraries

Leißa

Boesche

Hack

et al. 2018

Proc. ACM Program. Lang.

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This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manuallyoptimized expert codes that are considered to be among the top contenders in their fields. CCS Concepts: • Software and its engineering → Compilers; Parallel programming languages; • Hardware → Emerging languages and compilers; • Computing methodologies → Image processing; Ray tracing; • Applied computing → Bioinformatics;

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OptiX

Cited by 12 publications

References 0 publications

Ray Tracing Generalized Tube Primitives: Method and Applications

Ray Tracing Generalized Tube Primitives: Method and Applications

GPU simulation with Opticks: The future of optical simulations for LZ

AnyDSL: a partial evaluation framework for programming high-performance libraries

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