The emergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) has significantly expanded the applications of three-dimensional convolutional neural networks (3D CNNs). At the same time, the next-generation Sunway supercomputer has evidenced its superior computational capabilities in the HPC+AI domain. However, complex 3D convolution remains a primary performance limitation in many applications. The optimization of tensor-like operators on the Sunway processor is usually implemented via a multi-level blocking approach, adapting to its architecture. Although it can effectively mitigate the differences in memory access latency among different memory hierarchies, the performance of 3D convolutions is still frequently limited by the transfer bandwidth.

Latest advances in computational methods and high-performance computing have enabled large-scale scientific simulations to become feasible. However, efficiently analyzing the resulting large datasets remains challenging. Typically, 3D voxel data occupies huge storage space which causes serious storage pressure. Performing real-time feature extraction during simulations could mitigate this demand. In this paper, We propose a three-decker structure framework for locating and extracting voxel object features such as pose, class, and size. The framework first utilizes a 3D CNN to localize the object’s center and size along each axis, enabling adaptive cropping of the target from the original voxel space. Then the singular value decomposition (SVD) will applied to the extracted object to preliminarily extract and refine its posture. Finally, a neural network will refine the SVD-extracted pose and predict the category and size. Finally, a neural network is utilized to infer residual pose corrections as well as category and size information for the object after preliminary pose alignment via SVD decomposition. Our method achieves accurate feature extraction for voxel objects with complex poses. More importantly, we achieve pose estimation without an initial position. By storing extracted features instead of full data, we significantly reduce storage needs. We validate our approach using the phase field simulations dataset and ModelNet40 dataset. At last, based on the proposed framework we develop an in-situ feature extraction library for running with large-scale scientific computing programs and performing real-time feature extraction on the large amount of computation data it generates. As a concrete running example, we selected the microstructure evolution program governed by the phase-field method for a common run and achieved high-accuracy feature extraction.

In this paper, we present an integrated framework composed of a highly efficient phase field simulator and an in-situ feature extraction library. This novel framework enables us to conduct extreme-scale micro-structure evolution simulations while the characteristic features of each individual grain are extracted on the fly. After systematic design and optimization on the new generation Sunway supercomputer, the code scales up to 39 million cores and achieves 582 PFlops in double precision and 637 POps in mixed precision.