研究目的
To accelerate the multi-mode difference map algorithm for ptychography reconstruction using multiple distributed GPUs, significantly reducing the computation time from hours to about 1 minute for real-time data processing and visualization.
研究成果
The GPU implementation of the multi-mode DM algorithm for X-ray ptychography significantly reduces computation time, enabling real-time feedback for experimental setup adjustments. Future work includes further performance tuning and memory footprint reduction.
研究不足
The necessity of MPI collective communications for synchronization and the challenge of parallelizing the object update due to non-overlapping points in runtime. Memory footprint is significantly larger for multi-mode reconstruction, requiring over 10 times the GPU memory compared to raw data size.
1:Experimental Design and Method Selection:
The study leverages the multi-mode difference map algorithm for ptychography reconstruction, implemented using Python with mpi4py and PyCUDA for GPU acceleration.
2:Sample Selection and Data Sources:
Realistic diffraction measurements of various sizes are used for benchmarking.
3:List of Experimental Equipment and Materials:
NVIDIA GPUs, including Tesla K20 and V100 models, are used for computation.
4:Experimental Procedures and Operational Workflow:
The workflow involves distributing the dataset indices equally among available GPUs, performing iterative reconstruction with synchronization via MPI collective communications.
5:Data Analysis Methods:
Performance is benchmarked by measuring time-to-solution scaling as a function of the number of GPUs for various dataset sizes.
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