Real-time electroholography using a single spatial light modulator and a cluster of graphics-processing units connected by a gigabit Ethernet network

Hiromi Sannomiya; Naoki Takada; Tomoya Sakaguchi; Hirotaka Nakayama; Minoru Oikawa; Yuichiro Mori; Takashi Kakue; Tomoyoshi Shimobaba; Tomoyoshi Ito

doi:doi:10.3788/COL202018.020902

Chinese Optics Letters, 2020, 18 (2): 020902, Published Online: Jan. 7, 2020

Real-time electroholography using a single spatial light modulator and a cluster of graphics-processing units connected by a gigabit Ethernet network Download： 907次

Hiromi Sannomiya ¹Naoki Takada ^2,*Tomoya Sakaguchi ¹Hirotaka Nakayama ³Minoru Oikawa ²Yuichiro Mori ²Takashi Kakue ⁴Tomoyoshi Shimobaba ⁴Tomoyoshi Ito ⁴

Author Affiliations

¹ Graduate School of Integrated Arts and Sciences, Kochi University, Kochi, Kochi 780-8520, Japan

² Research and Education Faculty, Kochi University, Kochi, Kochi 780-8520, Japan

³ National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588, Japan

⁴ Graduate School of Engineering, Chiba University, Chiba, Chiba 263-8522, Japan

Figures & Tables

Fig. 1. Proposed system with a single SLM and a multi-GPU cluster connected by gigabit Ethernet network.

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Fig. 2. Pipeline processing for real-time electroholography using the proposed system.

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Fig. 3. Outline of the packing process used to reduce the volume of data transferred.

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Fig. 4. Outline of the unpacking process used to reproduce the binary CGH.

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Fig. 5. Comparison of a multi-GPU cluster connected with InfiniBand QDR and a multi-GPU cluster connected with a gigabit Ethernet.

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Fig. 6. Comparison of the multi-GPU cluster using the NVIDIA GeForce GTX TITAN X GPU and the multi-GPU cluster using the GeForce GTX 1080 Ti GPU.

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Fig. 7. Snapshot of a reconstructed 3D video (Video 1).

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Table1. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX TITAN X

Item	Specification
CPU	Intel Core i7 4770 (Clock speed: 3.4 GHz)
Main memory	DDR3-1600 4 GB
OS	Linux (CentOS 7.3 x86_64)
Software	NVIDIA CUDA 8.0 SDK, OpenGL, MPICH 3.2
GPU	NVIDIA GeForce GTX TITAN X

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Table2. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX 1080 Ti

Item	Specification
CPU	Intel Core i7 7800X (Clock speed: 3.5 GHz)
Main memory	DDR4-2666 16 GB
OS	Linux (CentOS 7.6 x86_64)
Software	NVIDIA CUDA 10.1 SDK, OpenGL, MPICH 3.2
GPU	NVIDIA GeForce GTX 1080 Ti

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Hiromi Sannomiya, Naoki Takada, Tomoya Sakaguchi, Hirotaka Nakayama, Minoru Oikawa, Yuichiro Mori, Takashi Kakue, Tomoyoshi Shimobaba, Tomoyoshi Ito. Real-time electroholography using a single spatial light modulator and a cluster of graphics-processing units connected by a gigabit Ethernet network[J]. Chinese Optics Letters, 2020, 18(2): 020902.

Real-time electroholography using a single spatial light modulator and a cluster of graphics-processing units connected by a gigabit Ethernet network Download： 907次

Fig. 1. Proposed system with a single SLM and a multi-GPU cluster connected by gigabit Ethernet network.

Fig. 2. Pipeline processing for real-time electroholography using the proposed system.

Fig. 3. Outline of the packing process used to reduce the volume of data transferred.

Fig. 4. Outline of the unpacking process used to reproduce the binary CGH.

Fig. 5. Comparison of a multi-GPU cluster connected with InfiniBand QDR and a multi-GPU cluster connected with a gigabit Ethernet.

Fig. 6. Comparison of the multi-GPU cluster using the NVIDIA GeForce GTX TITAN X GPU and the multi-GPU cluster using the GeForce GTX 1080 Ti GPU.

Fig. 7. Snapshot of a reconstructed 3D video (Video 1).

Table1. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX TITAN X

Table2. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX 1080 Ti

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Real-time electroholography using a single spatial light modulator and a cluster of graphics-processing units connected by a gigabit Ethernet network Download： 907次

Fig. 1. Proposed system with a single SLM and a multi-GPU cluster connected by gigabit Ethernet network.

Fig. 2. Pipeline processing for real-time electroholography using the proposed system.

Fig. 3. Outline of the packing process used to reduce the volume of data transferred.

Fig. 4. Outline of the unpacking process used to reproduce the binary CGH.

Fig. 5. Comparison of a multi-GPU cluster connected with InfiniBand QDR and a multi-GPU cluster connected with a gigabit Ethernet.

Fig. 6. Comparison of the multi-GPU cluster using the NVIDIA GeForce GTX TITAN X GPU and the multi-GPU cluster using the GeForce GTX 1080 Ti GPU.

Fig. 7. Snapshot of a reconstructed 3D video (Video 1).

Table1. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX TITAN X

Table2. Specifications of the Multi-GPU Cluster Using NVIDIA GeForce GTX 1080 Ti

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