Overcoming the difficulty of large-scale CGH generation on multi-GPU cluster

Abstract

The 3D holographic display has long been expected as a future human interface as it does not require users to wear special devices. However, its heavy computation requirement prevents the realization of such displays. A recent study says that objects and holograms with several giga-pixels should be processed in real time for the realization of high resolution and wide view angle. To this problem, first, we have adapted a conventional FFT algorithm to a GPU cluster environment in order to avoid heavy inter-node communications. Then, we have applied several single-node and multi-node optimization and parallelization techniques. The single-node optimizations include the change of the way of object decomposition, reduction of data transfer between CPU and GPU, kernel integration, stream processing, and utilization of multi-GPU within a node. The multi-node optimizations include distribution methods of object data from host node to the other nodes. The experimental results show that the intra-node optimizations attain 11.52 times speed-up from the original single node code. Further, multi-node optimizations using 8 nodes, 2 GPUs per node, attain the execution time of 4.28 sec. for generating 1.6 gigapixel hologram from 3.2 giga-pixel object. It means 237.92 times speed-up of the sequential processing by CPU using a conventional FFT-based algorithm. CCS Concepts • Computer systems organization → Heterogeneous (hybrid) systems; • Human-centered computing → Displays and imagers; • Software and its engineering → Parallel programming languages; • Applied computing → Physical sciences and engineering;

Fast Computation with Efficient Object Data Distribution for Large-Scale Hologram Generation on a Multi-GPU Cluster

“…Aiming for the ultimate goal of realizing 3D holographic display with high-resolution and wide view angle properties in real time, this research revises the results of [19], [20] and [21] and shows how we resolve the difficulties of large-scale CGH generation on a multi-GPU cluster by adapting the FFT-based algorithm to the clusters' environment and how to apply application-oriented optimizations under the multicore-CPU and multi-GPU combined heterogeneous architecture.…”