With the continuous improvement of living standards and consumption awareness, the value of products is greatly influenced by non-price factors. More and more companies are attracting purchasing power by providing additional services to consumers. We use Stackelberg game and CVaR theory to analyze the impact of different risk attitudes of manufacturers on pricing decisions in dual-channel supply chains, while considering competition and retailer deception. The results show that retailers with information advantages will always obtain additional profits through deception, and their deception value will vary with the manufacturer's risk attitude. Furthermore, we compared the optimal pricing and market demand under information symmetry and asymmetry with the changes of the manufacturer's risk attitude. Finally, we verified the changes in supply chain profits with the changes in retailer deception value and manufacturer's risk attitude through numerical simulation, and found that although retailer deception behavior increases its own profits, it weakens the overall utility of the dual-channel supply chain.

We employ our recently developed explicit 2nd-order charge-conservative symplectic electromagnetic particle-in-cell (PIC) scheme in the cylindrical mesh to simulate the whole-volume magnetic confinement toroidal plasmas on the new Sunway supercomputer. From a large-scale simulation of magneticized toroidal plasma with 111.3 trillion particles and 25.7 billion grids, we have obtained a sustained performance exceeding 201.1 PFLOP/s (double precision) with the fastest iteration step achieving 298.2 PFLOP/s (double precision). For the first time, unprecedented high resolution evolution of 6D electromagnetic fully kinetic plasmas based on 2D equilibrium profiles from Experimental Advanced Superconducting Tokamak (EAST) and designed operation state of China Fusion Engineering Test Reactor (CFETR) are presented, and edge micro-instabilities can be investigated directly. This shows the possibility to study crucial problems and phenomena in the magnetic confinement toroidal plasma directly using the symplectic electromagnetic fully kinetic PIC method on world's leading supercomputers.

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated$J_1$-$J_2$model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art.

Data speculation technique has been heavily exploited in various scenarios of architecture design. It bridges the time or space gap between data producer and data consumer, which gives opportunities to processors to gain significant speedups. However, large instruction windows, deep pipeline and increasing latency of on-chip communication make data misspeculation very expensive in modern processors.

In this paper, we present FlexBFS, a parallelism-aware implementation for breadth-first search on GPU. Our implementation can adjust the computation resources according to the feedback of available parallelism dynamically. We also optimized our program in three ways: (1)a simplified two-level queue management,(2)a combined kernel strategy and (3)a high-degree vertices specialization approach. Our experimental results show that it can achieve 3~20 times speedup against the fastest serial version, and can outperform the TBB based multi-threading CPU version and the previous most effective GPU version on all types of input graphs.

Many applications meet certain programming patterns like pipeline, fork-join, do-all etc. While tools such as OS threads and OpenMP allow programmers only to express task or data parallelism, special support for programming patterns is distinctly lacking. Intel threading building blocks (TBB) is developed to address this problem, but its scheduler is general and not optimized for any of its parallel algorithms which include pipeline specially. In this paper, we provide a non-blocking framework for pipeline application on multi-core platform. We target linear pipeline in which each filter has one entrance and one exit. We design a novel work-stealing scheduler optimized specially for pipeline application: first, priority based stealing, priority is calculated for each filter in pipeline so that a worker can find the optimal "victim" easily when it needs to steal, second, multiple tasks can be stolen at a time so that much stealing time is reduced. A non-block queue is used to store intermediate result to reduce lock overhead and increase scalability. We apply our framework to four case studies, including text filter, two fish, ferret, ded up. And our framework reduces execution time of TBB by 72% in best case and 20% on average on an 8 core machine.

The Cell Broadband Engine is a typical heterogeneous chip multiprocessor which provides potential high performance for computing-intensive applications. Our researches focus on how to use Cell to speed up block cryptography applications. In this paper, we propose a mapping framework for block cryptography working in ECB mode and corresponding optimizing strategy. We take four algorithms(RC5, 3DES, AES, and Twofish) as benchmark and implement these four algorithms using Cell programming language. In order to enhance the performance, we present an optimizing strategy and evaluate the effects of the optimizing methods including compiler optimization, dual buffering, vectorization, and loop unrolling. The experiments indicate that all these four algorithms can obtain 5-20 times speedup compared with traditional processors, which shows that our mapping framework and optimizing strategy are effective for the block cryptography algorithms.