Parallel Rendering on Hybrid Multi-GPU Clusters

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

Multi-GPU Rendering with the open Vulkan API

The Vulkan API provides a low level interface to modern Graphics Processing Units (GPUs). We demonstrate how to use Vulkan to send commands explicitly to separate GPUs for implementing platform,- and vendor independent multi-GPU rendering. We describe how to implement the sort-first and sort-last approaches to perform parallel rendering with Vulkan. We introduce an abstraction library which we have made available, and an application for multi-GPU rendering of meshes. Performance benchmarks have been performed in order to evaluate the implementation. We also show that we can utilize the additional GPU memory from multiple GPUs to render larger data sets than possible with a single GPU

Tuoris: A middleware for visualizing dynamic graphics in scalable resolution display environments

In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images

Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi

Parallel rendering on hybrid multi-GPU clusters

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