In contrast to sort-first, sort-last parallel rendering has the distinct advantage that the task division for parallel geometry processing and rasterization is simple, and can easily be incorporated into most visualization systems. However, the efficient final depth-compositing for polygonal data, or alpha-blending for volume data of partial rendering results is the key to achieve scalability in sort-last parallel rendering. In this paper, we demonstrate the efficiency as well as flexibility of the direct send sort-last compositing algorithm, and compare it to existing approaches, both in a theoretical analysis and in an experimental setting.

https://www.zora.uzh.ch/id/eprint/47723/4/DirectSend.pdf

Direct send compositing for parallel sort-last rendering

Imaging spectroscopy, also known as hyperspectral imaging, is a new technique that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. In particular, NASA is continuously gathering high-dimensional image data from the surface of the earth with hyperspectral sensors such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion hyperspectral imager aboard NASA's Earth Observing-1 (EO-1) spacecraft. Despite the massive volume of scientific data commonly involved in hyperspectral imaging applications, very few parallel strategies for hyperspectral analysis are currently available, and most of them have been designed in the context of homogeneous computing platforms. However, heterogeneous networks of workstations represent a very promising cost-effective solution that is expected to play a major role in the design of high-performance computing platforms for many on-going and planned remote sensing missions. Our main goal in this paper is to understand parallel performance of hyperspectral imaging algorithms comprising the standard hyperspectral data processing chain (which includes pre-processing, selection of pure spectral components and linear spectral unmixing) in the context of fully heterogeneous computing platforms. For that purpose, we develop an exhaustive quantitative and comparative analysis of several available and new parallel hyperspectral imaging algorithms by comparing their efficiency on both a fully heterogeneous network of workstations and a massively parallel homogeneous cluster at NASA's Goddard Space Flight Center in Maryland.

/pdf/an-experimental-comparison-of-parallel-algorithms-for-2gbbxc33fk.pdf

An experimental comparison of parallel algorithms for hyperspectral analysis using heterogeneous and homogeneous networks of workstations

Achieving efficient scalable parallel rendering for interactive visualization applications on medium-sized graphics clusters remains a challenging problem. Framerates of up to 60hz require a carefully designed and fine-tuned parallel rendering implementation that fits all required operations into the 16ms time budget available for each rendered frame. Furthermore, modern commodity hardware embraces more and more a NUMA architecture, where multiple processor sockets each have their locally attached memory and where auxiliary devices such as GPUs and network interfaces are directly attached to one of the processors. Such so called fat NUMA processing and graphics nodes are increasingly used to build cost-effective hybrid shared/distributed memory visualization clusters. In this paper we present a thorough analysis of the asynchronous parallelization of the rendering stages and we derive and implement important optimizations to achieve highly interactive framerates on such hybrid multi-GPU clusters. We use both a benchmark program and a real-world scientific application used to visualize, navigate and interact with simulations of cortical neuron circuit models.

/pdf/parallel-rendering-on-hybrid-multi-gpu-clusters-1jgagzluql.pdf

Parallel rendering on hybrid multi-GPU clusters

Parallel rendering of large polygonal models with transparency is challenging due to the need for alpha-correct blending and compositing, which is costly for very large models with high depth complexity and spatial overlap. In this paper we compare the performance of raster-based rendering methods on mesh models of neurons using two applications, one of which is specifically tailored to the neuroscience application domain, the other a general purpose visualization tool with domain specific additions. The first implements both sort-first and sort-last and uses a scene graph style traversal to cull objects, and dual depth peeling for order independent transparency, whilst the other uses a simpler brute force data-parallel approach with sort last composition. The advantages and trade offs of these approaches are discussed.We present the optimized algorithms needed to achieve interactive frame rates for a non-trivial, real-world parallel rendering scenario. We show that a generic data visualization application can provide competitive performance when optimizing its rendering pipeline, with some loss of capability over an optimized domain-specific application.

Practical parallel rendering of detailed neuron simulations

Hardware-accelerated image composition for sort-last parallel rendering has received increasing attention as an effective solution to increased performance demands brought about by the recent advances in commodity graphics accelerators. So far, several different hardware solutions for alpha and depth compositing have been proposed and a few of them have become commercially available. They share impressive compositing speed and high scalability. However, the cost makes it prohibitively expensive to build a large visualization system. In this paper, we used a hardware image compositor marketed by Mitsubishi Precision Co., Ltd. (MPC) which is now available as an independent device enabling the building of our own visualization cluster. This device is based on binary compositing tree architecture, and the scalable cascade interconnection makes it possible to build a large visualization system. However, we focused on a minimal configuration PC cluster using only one compositing device while taking cost into consideration. In order to emulate this cascade interconnection of MPC compositors, we propose and evaluate the hybrid hardware-assisted image composition method which uses the OpenGL alpha blending capability of the graphics boards for assisting the hardware image composition process. Preliminary experiments show that the use of graphics boards diminished the performance degradation when using an emulation based on image feedback through available interconnection network. We found that this proposed method becomes an important alternative for providing high performance image composition at a reasonable cost.

/pdf/hybrid-hardware-accelerated-image-composition-for-sort-last-1glaf6dcao.pdf

Hybrid hardware-accelerated image composition for sort-last parallel rendering on graphics clusters with commodity image compositor

Although the sort-last parallel rendering is a promising approach to accelerate large-scale computer graphics applications handling huge data sets, parallel image composition is a bottleneck of performance improvement. So far, several image coding schemes have been proposed in order to achieve fast image composition by compressing communicated data. These schemes mainly encode blank pixels in rendered images, which are pixels with no projection of objects. However, sufficient compression was not available in the case that rendered images have few blank pixels. This paper presents an image coding scheme that reduces the communication time in parallel image composition by effective compression of non-blank pixels and load balancing. The coding scheme exploits coherence of differential pixel values with a few additional computations that do not spoil the reduction in communication time. Experiments on a PC cluster with eight processing elements connected by a 100 Mbit Ethernet switching hub show that the worst frame rate of all viewing parameters can greatly be improved by the proposed coding scheme.

Differential coding scheme for efficient parallel image composition on a PC cluster system

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Differential coding scheme for efficient parallel image composition on a PC cluster system