The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Scalable ray tracing with multiple GPGPUs

Rapid development in the field of computer graphics over the last 40 years has brought forth different techniques to render scenes. Rasterization is today’s most widely used technique, which in its most basic form sequentially draws thousands of polygons and applies texture on them. Ray tracing is an alternative method that mimics light transport by using rays to sample a scene in memory and render the color found at each ray’s scene intersection point. Although mainstream hardware directly supports rasterization, ray tracing would be the preferred technique due to its ability to produce highly crisp and realistic graphics, if hardware were not a limitation. Making an immediate hardware transition from rasterization to ray tracing would have a severe impact on the computer graphics industry since it would require redevelopment of existing 3D graphics-employing software, so any transition to ray tracing would be gradual. Previous efforts to perform ray tracing on mainstream rasterizing hardware platforms with a single processor have performed poorly. This thesis explores how a multiple GPGPU system can be used to render scenes via ray tracing. A ray tracing engine and API groundwork was developed using NVIDIA’s CUDA (Compute Unified Device Architecture) GPGPU programming environment and was used to evaluate performance scalability across a multi-GPGPU system. This engine supports triangle, sphere, disc, rectangle, and torus rendering. It also allows independent activation of graphics features including procedural texturing, Phong illumination, reflections, translucency, and shadows. Correctness of rendered images validates the ray traced results, and timing of rendered scenes benchmarks performance. The main test scene contains all object types, has a total of 32 Abstract objects, and applies all graphics features. Ray tracing this scene using two GPGPUs outperformed the single-GPGPU and single-CPU systems, yielding respective speedups of up to 1.8 and 31.25. The results demonstrate how much potential exists in treating a modern dual-GPU architecture as a dual-GPGPU system in order to facilitate a transition from rasterization to ray tracing

A framework for efficient execution of data parallel irregular applications on heterogeneous systems

Exploiting the computing power of the diversity of resources available on heterogeneous systems is mandatory but a very challenging task. The diversity of architectures, execution models and programming tools, together with disjoint address spaces and di erent computing capabilities, raise a number of challenges that severely impact on application performance and programming productivity. This problem is further compounded in the presence of data parallel irregular applications. This paper presents a framework that addresses development and execution of data parallel irregular applications in heterogeneous systems. A uni ed task-based programming and execution model is proposed, together with inter and intra-device scheduling, which, coupled with a data management system, aim to achieve performance scalability across multiple devices, while maintaining high programming productivity. Intradevice scheduling on wide SIMD/SIMT architectures resorts to consumer-producer kernels, which, by allowing dynamic generation and rescheduling of new work units, enable balancing irregular workloads and increase resource utilization. Results show that regular and irregular applications scale well with the number of devices, while requiring minimal programming e ort. Consumer-producer kernels are able to sustain signi cant performance gains as long as the workload per basic work unit is enough to compensate overheads associated with intra-device scheduling. This not being the case, consumer kernels can still be used for the irregular application. Comparisons with an alternative framework, StarPU, which targets regular workloads, consistently demonstrate signi cant speedups. This is, to the best of our knowledge, the rst published integrated approach that successfully handles irregular workloads over heterogeneous systems.This work is funded by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) and by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) within projects PEst-OE/EEI/UI0752/2014 and FCOMP-01-0124-FEDER-010067. Also by the School of Engineering, Universidade do Minho within project P2SHOCS - Performance Portability on Scalable Heterogeneous Computing Systems

GPU ray tracing with CUDA

Ray Tracing is a rendering method that generates high quality images by simulating how light rays interact with objects in a virtual scene. The ray tracing technique can accurately portray advanced optical effects, such as reflections, refractions, and shadows, but at a greater computational cost and rendering time than other rendering methods. Fortunately, technological advances in GPU computing have provided the means to accelerate the ray tracing process to produce images in a significantly shorter time. This paper attempts to clearly illustrate the difference in rendering speed and design by developing and comparing a sequential CPU and parallel GPU implementation of a ray tracer, written in C++ and CUDA respectively. A performance analysis reveals that the optimized GPU ray tracer is capable of producing images with speedup gains up to 1852X when compared to the former CPU implementation --Document

Interactive Visualization of the Largest Radioastronomy Cubes

3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our framework's performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201

FDTD/K-DWM simulation of 3D room acoustics on general purpose graphics hardware using compute unified device architecture (CUDA)

The growing demand for reliable prediction of sound fields in rooms have resulted in adaptation of various approaches for physical modeling, including the Finite Difference Time Domain (FDTD) and the Digital Waveguide Mesh (DWM). Whilst considered versatile and attractive methods, they suffer from dispersion errors that increase with frequency and vary with direction of propagation, thus imposing a high frequency calculation limit. Attempts have been made to reduce such errors by considering different mesh topologies, by spatial interpolation, or by simply oversampling the grid. As the latter approach is computationally expensive, its application to three-dimensional problems has often been avoided. In this paper, we propose an implementation of the FDTD on general purpose graphics hardware, allowing for high sampling rates whilst maintaining reasonable calculation times. Dispersion errors are consequently reduced and the high frequency limit is increased. A range of graphics processors are evaluated and compared with traditional CPUs in terms of accuracy, calculation time and memory requirements

Perceptually optimized real-time computer graphics

Perceptual optimization, the application of human visual perception models to remove imperceptible components in a graphics system, has been proven effective in achieving significant computational speedup. Previous implementations of this technique have focused on spatial level of detail reduction, which typically results in noticeable degradation of image quality. This thesis introduces refresh rate modulation (RRM), a novel perceptual optimization technique that produces better performance enhancement while more effectively preserving image quality and resolving static scene elements in full detail. In order to demonstrate the effectiveness of this technique, a graphics framework has been developed that interfaces with eye tracking hardware to take advantage of user fixation data in real-time. Central to the framework is a high-performance GPGPU ray-tracing engine written in OpenCL. RRM reduces the frequency with which pixels outside of the foveal region are updated by the ray-tracer. A persistent pixel buffer is maintained such that peripheral data from previous frames provides context for the foveal image in the current frame. Traditional optimization techniques have also been incorporated into the ray-tracer for improved performance. Applying the RRM technique to the ray-tracing engine results in a speedup of 2.27 (252 fps vs. 111 fps at 1080p) for the classic Whitted scene with reflection and transmission enabled. A speedup of 3.41 (140 fps vs. 41 fps at 1080p) is observed for a high-polygon scene that depicts the Stanford Bunny. A small pilot study indicates that RRM achieves these results with minimal impact to perceived image quality. A secondary investigation is conducted regarding the performance benefits of increasing physics engine error tolerance for bounding volume hierarchy based collision detection when the scene elements involved are in the user\u27s periphery. The open-source Bullet Physics Library was used to add accurate collision detection to the full resolution ray-tracing engine. For a scene with a static high-polygon model and 50 moving spheres, a speedup of 1.8 was observed for physics calculations. The development and integration of this subsystem demonstrates the extensibility of the graphics framework

Auto-tuning Interactive Ray Tracing using an Analytical GPU Architecture Model

This paper presents a method for auto-tuning interactive ray tracing on GPUs using a hardware model. Getting full performance from modern GPUs is a challenging task. Workloads which require a guaranteed performance over several runs must select parameters for the worst performance of all runs. Our method uses an analyti- cal GPU performance model to predict the current frame’s render- ing time using a selected set of parameters. These parameters are then optimised for a selected frame rate performance on the partic- ular GPU architecture. We use auto-tuning to determine parameters such as phong shading, shadow rays and the number of ambient oc- clusion rays. We sample a priori information about the current ren- dering load to estimate the frame workload. A GPU model is run iteratively using this information to tune rendering parameters for a target frame rate. We use the OpenCL API allowing tuning across different GPU architectures. Our auto-tuning enables the render- ing of each frame to execute in a predicted time, so a target frame rate can be achieved even with widely varying scene complexities. Using this method we can select optimal parameters for the cur- rent execution taking into account the current viewpoint and scene, achieving performance improvements over predetermined parame- ters