Overcoming the difficulty of large-scale CGH generation on multi-GPU cluster
Overcoming the difficulty of large-scale CGH generation on multi-GPU cluster
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DOI：
10.1145/3180270.3180273
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
T. Baba;Shinpei Watanabe;B. Jackin;Takeshi Ohkawa;K. Ootsu;T. Yokota;Y. Hayasaki;T. Yatagai
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文献类型：
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作者：
T. Baba;Shinpei Watanabe;B. Jackin;Takeshi Ohkawa;K. Ootsu;T. Yokota;Y. Hayasaki;T. Yatagai
3D全息显示一直被认为是未来的人机界面，因为它不需要用户佩戴特殊设备。然而，其繁重的计算要求阻止了这种显示器的实现。最近的一项研究表明，为了实现高分辨率和宽视角，需要对具有数十亿像素的物体和全息图进行真实的实时处理。针对这个问题，首先，我们采用了传统的FFT算法的GPU集群环境，以避免繁重的节点间通信。然后，我们应用了几个单节点和多节点的优化和并行化技术。单节点优化包括改变对象分解方式、减少CPU与GPU之间的数据传输、内核集成、流处理以及利用节点内多GPU。多节点优化包括对象数据从主机节点到其他节点的分发方法。实验结果表明，节点内优化比原始单节点代码获得了11.52倍的加速。此外，使用8个节点、每个节点2个GPU的多节点优化实现了4.28秒的执行时间。用于从3.2千兆像素物体生成1.6千兆像素全息图。这意味着CPU使用传统的基于FFT的算法进行顺序处理的速度提高了237.92倍。
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 giga-pixel 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.