HPL-MxP is an emerging high performance benchmark used to measure the mixed-precision computing capability of leading supercomputers. In this work, we present our efforts on the new Sunway that linearly scales the benchmark to over 40 million cores, sustains an overall mixed-precision performance exceeding 5 ExaFlop/s, and achieves over 85% of peak performance, which is the highest efficiency reached among all heterogeneous systems on the HPL-MxP list. The optimizations of our HPL-MxP implementation include the following: (1) a Two-Direction Look-Ahead and Overlap algorithm that enables overlaps of all communications with computation; (2) a multi-level process-mapping and communication scheduling method that uses the entire network as best as possible while maintaining conflict-free algorithm-flow; and (3) a CG-Fusion computing framework that eliminates up to 60% of inter-chip communications and removes the memory access bottleneck while serving both computation and communication simultaneously. This work could also provide useful insights for tuning cutting-edge applications on Sunway supercomputers as well as other heterogeneous supercomputers.

Matrix factorization functions are used in many areas and often play an important role in the overall performance of the applications. In the LAPACK library, matrix factorization functions are implemented with blocked factorization algorithm, shifting most of the workload to the high-performance Level-3 BLAS functions. But the non-blocked part, the panel factorization, becomes the performance bottleneck, especially for small- and medium-size matrices that are the common cases in many real applications. On the new Sunway many-core platform, the performance bottleneck of panel factorization can be alleviated by keeping the panel in the LDM for the panel factorization. Therefore, we propose a new framework for implementing matrix factorization functions on the new Sunway many-core platform, facilitating the in-LDM panel factorization. The framework provides a template class with wrapper functions, which integrates inter-CPE communication for the Level-1 and Level-2 BLAS functions with flexible interfaces and can accommodate different partitioning schemes. With the framework, writing panel factorization code with data residing in the LDM space can be done with much higher productivity. We implemented three functions ( dgetrf , dgeqrf , and dpotrf ) based on the framework and compared our work with a CPE_BLAS version, which uses the original LAPACK implementation linked with optimized BLAS library that runs on the CPE mesh. Using the most favorable partitioning, the panel factorization part achieves speedup of up to 26.3, 19.1, and 18.2 for the three matrix factorization functions. For the whole function, our implementation is based on a carefully tuned recursion framework, and we added specific optimization to some subroutines used in the factorization functions. Overall, we obtained average speedup of 9.76 on dgetrf , 10.12 on dgeqrf , and 4.16 on dpotrf , compared to the CPE_BLAS version. Based on the current template class, our work can be extended to support more categories of linear algebra functions.

We study and evaluate performance optimization techniques for the HPCG benchmark on the newest generation Sunway supercomputer. Specifically, a two-level blocking scheme is proposed to expose adequate parallelism in the symmetric Gauss-Seidel kernel while keeping a fast convergence rate, a fine-grained kernel fusion technique is developed to alleviate the bandwidth load on local storage with small capacity, and a low overhead thread collaboration method is presented to efficiently move data between threads and hide its cost with data transfer operations. Test results show that the optimized HPCG code is able to exploit 73.0% of the theoretical memory bandwidth, and scale to over 42 million heterogeneous cores with 95.5% weak-scaling efficiency and 5.91 Pflop/s performance. We also study how the performance can be improved if the specific rules of HPCG are not fully obeyed, and design dependency preserving parallelization and vectorization methods, further boosting performance to 27.6 Pflop/s.

Automatic detection of pain and protective behaviour can help chronic pain patients to get proper assistance and helpful treatment with the help of medical professionals. Using the EmoPain datasetwe study how autoencoder-based and attention-based deep learning models can be used to automatically detect pain and protective behavior that is usually associated with it. We propose a deep learning architecture called Autoencoder-LSTM-Attention-Net (ALANet), which can improve the automatic detection of pain and protective behaviors. Through comparative experiments with other machine learning models trained on the EmoPain dataset, we found that by using a combination of autoencoder and attention mechanisms, we can not only improve the recognition performance, but also greatly increase the speed of training the model. In addition, we analyse the effect of extracting temporal information from each body part separately compared to all body parts combined.