| 研究課題/領域番号 |
21K17750
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分60090:高性能計算関連
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| 研究機関 | 国立研究開発法人産業技術総合研究所 |
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研究代表者 |
陳 鵬 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (30890199)
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| 研究期間 (年度) |
2021-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2022年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2021年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
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| キーワード | GPGPU / Tensor Core / Supercomputer / Image reconstruction / Computed Tomography / MPI / Image Reconstruction / Filtered Back-projection / CPU / GPU / FPGA / Ptychography / HPC / Image Processing |
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| 研究開始時の研究の概要 |
In this study, the applicant seeks to (1) design novel image reconstruction algorithms to push the frontier of high-resolution images with high image quality, and (2) explore the holy grail of high-resolution image reconstruction: real-time reconstruction of 3D volumes for CT scans.
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| 研究実績の概要 |
To achieve large-scale tomographic imaging, we leverage cutting-edge technology, such as Nvidia GPUs and Tensor Cores, to tackle the challenge of producing high-resolution images. While traditional methods rely on supercomputers and are time-consuming, our goal is to achieve rapid and accurate results, even on smaller systems. Our innovative approach utilizes special GPU features, including Tensor Cores, to accelerate image generation without compromising quality. Additionally, we develop a system that enables multiple GPUs to collaborate, further enhancing image processing speed. Evaluations demonstrate that our approach significantly outperforms conventional methods, even with large datasets. In summary, our work effectively meets the demands of modern imaging technology.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
To achieve large-scale tomographic imaging, we utilize advanced Nvidia GPUs and Tensor Cores for rapid generation of high-resolution images. While traditional methods rely on supercomputers and are slow, our approach operates efficiently, even on smaller systems, such as 8 Nvidia A100 GPUs. By harnessing the power of GPUs, we expedite image generation without compromising quality. Our system facilitates collaboration among multiple GPUs, further enhancing processing speed. This proven method has garnered recognition in esteemed conferences and journals. In conclusion, our innovative use of Nvidia GPUs and Tensor Cores establishes a new benchmark for large-scale high-resolution tomographic imaging. Our solution is not only faster but also more efficient compared to traditional methods.
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| 今後の研究の推進方策 |
Our goal is to enhance image reconstruction by optimizing computing kernels using supercomputers and various accelerators, achieving superior computational and power efficiency. We will continually leverage the latest accelerators, such as Nvidia GPUs, AMD GPUs, and FPGAs, to optimize computing kernels. Additionally, we will utilize state-of-the-art supercomputers like Fugaku and ABCI to enhance the computing capabilities for large-scale image reconstruction.
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