Arnold is a physically based renderer for feature-length animation and visual effects. Conceived in an era of complex multi-pass rasterization-based workflows struggling to keep up with growing demands for complexity and realism, Arnold was created to take on the challenge of making the simple and elegant approach of brute-force Monte Carlo path tracing practical for production rendering. Achieving this required building a robust piece of ray-tracing software that can ingest large amounts of geometry with detailed shading and lighting and produce images with high fidelity, while scaling well with the available memory and processing power. Arnold’s guiding principles are to expose as few controls as possible, provide rapid feedback to artists, and adapt to various production workflows. In this article, we describe its architecture with a focus on the design and implementation choices made during its evolutionary development to meet the aforementioned requirements and goals. Arnold’s workhorse is a unidirectional path tracer that avoids the use of hard-to-manage and artifact-prone caching and sits on top of a ray-tracing engine optimized to shoot and shade billions of spatially incoherent rays throughout a scene. A comprehensive API provides the means to configure and extend the system’s functionality, to describe a scene, render it, and save the results.

Abstract We present new methods for uniformly sampling the solid angle subtended by a disk. To achieve this, we devise two novel area‐preserving mappings from the unit square [0,1]2 to a spherical ellipse (i.e. the projection of the disk onto the unit sphere). These mappings allow for low‐variance stratified sampling of direct illumination from disk‐shaped light sources. We discuss how to efficiently incorporate our methods into a production renderer and demonstrate the quality of our maps, showing significantly lower variance than previous work.

Abstract We present an area‐preserving parametrization for spherical rectangles which is an analytical function with domain in the unit rectangle [0, 1]2 and range in a region included in the unit‐radius sphere. The parametrization preserves areas up to a constant factor and is thus very useful in the context of rendering as it allows to map random sample point sets in [0, 1]2 onto the spherical rectangle. This allows for easily incorporating stratified, quasi‐Monte Carlo or other sampling strategies in algorithms that compute scattering from planar rectangular emitters.

In this paper we identify important opportunities for parallelization in the least-squares Monte Carlo (LSM) algorithm, due to Longstaff and Schwartz, for the pricing of American options. The LSM method can be divided into three phases: path-simulation, calibration and valuation. We describe how each of these phases can be parallelized, with more focus on the calibration phase, which is inherently more difficult to parallelize. We implemented these parallelization techniques on Blue Gene using the Quantlib open source financial engineering package. We achieved up to factor of 9 speed-up for the calibration phase and 18 for the complete LSM method on a 32 processor BG/P system using monomial basis functions.

High Throughput Computing (HTC) environments strive "to provide large amounts of processing capacity to customers over long periods of time by exploiting existing resources on the network" according to Basney and Livny [1]. A single Blue Gene/L rack can provide thousands of CPU resources into HTC environments. This paper discusses the implementation of an asynchronous task dispatch system that exploits a recently released feature of the Blue Gene/L control system - called HTC mode - and presents data on experimental runs consisting of the asynchronous submission of multiple batches of thousands of tasks for financial workloads. The methodology developed here demonstrates how systems with very large processor counts and light-weight kernels can be configured to deliver capacity computing at the individual processor level in future petascale computing systems.