In this article, we propose a novel routine pattern extraction architecture to analyze the daily behaviors of mobile users. The key component of the proposed architecture is a dynamic programming-based sequence dissimilarity calculation method, which aims to measure the dissimilarity between trajectories and then extract patterns using the clustering method. The method exploits three different information: (1) spatial-temporal information, (2) information in the continuous same location in the sequence and (3) the probabilities of location occurrences in the data. We conduct experiments on a synthetic dataset and two real-world datasets. The obtained results demonstrate that our proposed method is efficient in extracting hidden routine patterns from users’ trajectory data.

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 -- 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an unprecedented 17 billion atoms. The strong scaling of a 13.5-million atom copper system shows that the time-to-solution can be 7 times faster, reaching 11.2 nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on ab initio accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.

There is a widening gap between GPU and other components (CPU, PCIe bus and communication network) in heterogeneous parallel system. The gap forces us to orchestrate cooperative execution among these components much more carefully than ever before. By taking the LINPACK benchmark as a case study, this article proposes a fine-grained pipelining algorithm on large-scale CPU-GPU heterogeneous cluster systems. First, we build an algorithmic model that reveals a new approach to GPU-centric and fine-grained pipelining algorithm design. Then, we present four model-driven pipelining algorithms that incrementally squeeze bubbles in the pipeline so that it is occupied by more useful floating-point calculations. The algorithms are implemented on both the AMD and NVIDIA GPU platforms. The finally optimized LINPACK program achieves 107 PFlops on 25, 600 GPUs (70 percent floating-point efficiency). Several insights have been drawn to suggest tradeoff of algorithm design, programming support, and architecture design.

As the widening gap between GPU computing capability and other components (CPU, PCIe bus and communication network), it's increasingly challenging to design high performance parallel algorithms for large CPU-GPU heterogeneous systems. There are mainly two reasons. Firstly, simply offloading the kernel library to GPU incurs large volume data transfer through low-speed PCIe bus. Secondly, communication overheads through network severely affects scalability. To solve the above issues, we advocate a paradigm shift to CPU-centric and fine-grained pipelining algorithm design. By taking Linpack benchmark as a case study, the new algorithm design paradigm shows its effectiveness. Our optimized Linpack program achieves 63.79PFlops on 16384 GPUs. Its floating-point efficiency outperforms the NVIDIA proprietary counterparts by 5% on average.