GPU Accelerated Simulation of Scene Generation of 3D Photonic Mixer Device Camera

Simulation of Photonic Mixer Device (PMD) sensors have the capability to create virtual environment to test 3D camera design. This simulation comprises of multiple steps like scene generation using ray tracing, power calculation, raw data generation and raw data processing.  However, each step-in situation process takes longer time to implement and they are simulation process, simulators need to be faster. In this paper, we propose parallel implementation method for scene generation using GPGPUs. The feasibility of the method is confirmed using Amdahl’s law before implementation. The method is implemented and tested on GeForce 820M, GeForce 750Ti and Volta V100.Tthe highest speed up obtained is 219.913 using Volta (GV100) GPU for block size 1024. Thus, parallel method optimizes the scene generation time as compared to serial processing and the implemented results are better than the state of the art in the literature

Memory-savvy distributed interactive ray tracing

Journal ArticleInteractive ray tracing in a cluster environment requires paying close attention to the constraints of a loosely coupled distributed system. To render large scenes interactively, memory limits and network latency must be addressed efficiently. In this paper, we improve previous systems by moving to a page-based distributed shared memory layer, resulting in faster and easier access to a shared memory space. The technique is designed to take advantage of the large virtual memory space provided by 64-bit machines. We also examine task reuse through decentralized load balancing and primitive reorganization to complement the shared memory system. These techniques improve memory coherence and are valuable when physical memory is limited. C-SAF

Knowledge-based out-of-core algorithms for data management in visualization

Journal ArticleData management is the very first issue in handling very large datasets. Many existing out-of-core algorithms used in visualization are closely coupled with application-specific logic. This paper presents two knowledgebased out-of-core prefetching algorithms that do not use hard-coded rendering-related logic. They acquire the knowledge of the access history and patterns dynamically, and adapt their prefetching strategies accordingly. We have compared the algorithms with a demand-based algorithm, as well as a more domain-specific out-of-core algorithm. We carried out our evaluation in conjunction with an example application where rendering multiple point sets in a volume scene graph put a great strain on the rendering algorithm in terms of memory management. Our results have shown that the knowledge-based approach offers a better cache-hit to disk-access trade-off. This work demonstrates that it is possible to build an out-of-core prefetching algorithm without depending on rendering-related application-specific logic. The knowledge based approach has the advantage of being generic, efficient, flexible and self-adaptive

A custom designed density estimation method for light transport

We present a new Monte Carlo method for solving the global illumination problem in environments with general geometry descriptions and light emission and scattering properties. Current Monte Carlo global illumination algorithms are based on generic density estimation techniques that do not take into account any knowledge about the nature of the data points --- light and potential particle hit points --- from which a global illumination solution is to be reconstructed. We propose a novel estimator, especially designed for solving linear integral equations such as the rendering equation. The resulting single-pass global illumination algorithm promises to combine the flexibility and robustness of bi-directional path tracing with the efficiency of algorithms such as photon mapping

Memory sharing for interactive ray tracing on clusters

ManuscriptWe present recent results in the application of distributed shared memory to image parallel ray tracing on clusters. Image parallel rendering is traditionally limited to scenes that are small enough to be replicated in the memory of each node, because any processor may require access to any piece of the scene. We solve this problem by making all of a cluster's memory available through software distributed shared memory layers. With gigabit ethernet connections, this mechanism is sufficiently fast for interactive rendering of multi-gigabyte datasets. Object- and page-based distributed shared memories are compared, and optimizations for efficient memory use are discussed