Shape control and stability of multicore millimetre-sized capsules using a combined monoaxial dispersion electrospraying–ionotropic gelation technique

Multicore millimetre-sized fish oil-loaded alginate capsules were developed using a combined monoaxial dispersion electrospraying–ionotropic gelation technique and their stability was explored. By adjusting the preparation parameters, the capsule shapes could be irregular, spherical with a short-tail, spherical, fusiform, and fusiform with a long-tail. The continuous phase of the millimetre-sized capsules consisted of hydrophilic alginate calcium and water. Moreover, the water content increased from 17% to 69% with increased alginate concentration (2.5–30 mg mL⁻¹). The capsules prepared with alginate concentration of 10 mg mL⁻¹ or 20 mg mL⁻¹ show a similar loading ratio (about 5–8%) of fish oil during storage. Headspace solid-phase microextraction–gas chromatography mass spectroscopy (HS-SPME-GC-MS) results confirmed the capsules masked the fishy odour of fish oil. Moreover, fish oil slowly migrated from the inside to the outside of the capsules. This work presented a simple method to prepare multicore millimetre-sized capsules with controlled shapes and a basic understanding of the effect of encapsulation using alginate to mask the fish oil odour.     

Optimizing the parallel adaptive indexing algorithm on multi-core CPUs

An improved parallel adaptive indexing algorithm on multi-core CPUs is proposed to solve the problems that the parallel adaptive indexing algorithms cannot take full advantage of the CMP's parallel execution resource, and properly process the sequential query pattern. Based on the optimization of the Refined Partition Merge algorithm, our improved parallel adaptive indexing algorithm combines the Parallel Database Cracking method with the Refined Partition Merge algorithm. In our algorithm, when fewer data chunks are in the index, we use the optimized Refined Partition Merge algorithm so as to reduce the probability of conflict between threads, decrease the waiting time, and increase the utilization of the threads, and when more data chunks are in the index, we use the Parallel Database Cracking method so as to take full advantage of the CMP's parallel execution resources. Besides, we propose an optimization for the robustness, which makes our algorithm suitable for two common query patterns. Experiments show that our method can reduce the query time by 25.7％～33.2％, and suit with common query patterns.     

双线性插值图像放大并行算法的设计与实现

图像处理中对于图像放大常使用双线性插值算法,其运行的快慢直接关系到实时图像处理的速度．文中在多核计算机上设计了双线性插值并行程序,实现了图像放大的快速处理．充分利用多核技术提升多核处理器的资源利用率,缩短执行时间,发挥多核系统的优异性能．实验结果可看出,文中算法对于大规模图像放大呈现出良好的加速处理能力,表明了双线性插值多核并行算法在实时放大图像的应用中是有效及可行的．     

Assembly of finite element methods on graphics processors

Recently, graphics processing units (GPUs) have had great success in accelerating many numerical computations. We present their application to computations on unstructured meshes such as those in finite element methods. Multiple approaches in assembling and solving sparse linear systems with NVIDIA GPUs and the Compute Unified Device Architecture (CUDA) are created and analyzed. Multiple strategies for efficient use of global, shared, and local memory, methods to achieve memory coalescing, and optimal choice of parameters are introduced. We find that with appropriate preprocessing and arrangement of support data, the GPU coprocessor using single‐precision arithmetic achieves speedups of 30 or more in comparison to a well optimized double‐precision single core implementation. We also find that the optimal assembly strategy depends on the order of polynomials used in the finite element discretization. Copyright © 2010 John Wiley & Sons, Ltd.     

深度学习在电网智能调控系统中应用研究

基于对人工智能深度学习的研究,将该技术与电力智能调控深入融合,实现以数据、知识为基础的实时在线分析控制型电力调控。首先,分析深度学习的发展及其对电网调度的影响,针对深度学习在出力预测、状态估计和故障诊断方面的应用进行说明;然后,提出基于人工智能深度学习的电力调控系统构架,分析其与传统调控系统的区别,突出了该系统所实现的功能;接着,采用基于多核图像处理器（MGP）系统,阐明了所提出的智能调控系统实现的方法;最后,通过分析人工智能化调控系统的应用案例,说明该系统具有广泛的应用性。     

图像Laplace变换在异构多核工程科学计算加速协处理器上的实现

基于自主研发的新颖异构多核工程科学计算加速协处理器（ESCA）体系结构,实现了图像Laplace变换算法。针对ESCA架构特点,采用子字并行计算和访存延迟隐藏等机制,进行了一系列并行算法优化,并在四核ESCA处理器原型上对图像Laplace变换算法的进行了性能评测。实验结果表明,对于计算密集型计算任务,ESCA处理器具有良好的计算加速效果。     

基于C6678多核数字信号处理器的声纳信号并行处理设计

针对多核并行机制下,共享资源争夺激烈,硬件能力提升难以切实转变成程序效率提高的难题,通过协调存储器访存和核间同步等关键问题,研究了一种基于C6678多核数字信号处理器的声纳信号多级并行处理方法,包括核间流水线设计、数据传输与中央处理器并行设计和指令流线设计。以声纳二维相控方位滤波为例,介绍各级设计的实现方法,逐个测试并行性能,并编制实时处理软件。测试结果表明,该方法能够实现存储器访问和中央处理器运算并行,极大提高程序执行效率。通过采用该方法开发的实时处理系统,具有集成度高和实时性强的优点,获得了高航速下海中浮球和配重条石清晰的实时成像效果,具有工程应用价值。     

Generation of large finite‐element matrices on multiple graphics processors

This paper presents techniques for generating very large finite‐element matrices on a multicore workstation equipped with several graphics processing units (GPUs). To overcome the low memory size limitation of the GPUs, and at the same time to accelerate the generation process, we propose to generate the large sparse linear systems arising in finite‐element analysis in an iterative manner on several GPUs and to use the graphics accelerators concurrently with CPUs performing collection and addition of the matrix fragments using a fast multithreaded procedure. The scheduling of the threads is organized in such a way that the CPU operations do not affect the performance of the process, and the GPUs are idle only when data are being transferred from GPU to CPU. This approach is verified on two workstations: the first consists of two 6‐core Intel Xeon X5690 processors with two Fermi GPUs: each GPU is a GeForce GTX 590 with two graphics processors and 1.5 GB of fast RAM; the second workstation is equipped with two Tesla C2075 boards carrying 6 GB of RAM each and two 12‐core Opteron 6174s. For the latter setup, we demonstrate the fast generation of sparse finite‐element matrices as large as 10 million unknowns, with over 1 billion nonzero entries. Comparing with the single‐threaded and multithreaded CPU implementations, the GPU‐based version of the algorithm based on the ideas presented in this paper reduces the finite‐element matrix‐generation time in double precision by factors of 100 and 30, respectively. Copyright © 2012 John Wiley & Sons, Ltd.     

Designing a fine‐grained parallel differential evolution with Pareto tournaments for solving an optical networking problem

The future of designing optical networks focuses on the wavelength division multiplexing technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of optical fiber. However, when it is required to establish a set of demands, a problem comes up. This problem is known as routing and wavelength assignment problem. In this work, we have tackled the static routing and wavelength assignment problem by using multiobjective evolutionary computing. The algorithm applied is the differential evolution but modified with the Pareto tournaments concept for being adapted to the multiobjective context. By using OpenMP, an application programming interface that supports multiplatform shared memory multiprocessing programming, we have demonstrated that this algorithm is highly suitable to be parallelized. We have performed several experiments in multicore systems with two, four, and eight cores, obtaining 97.57% of mean efficiency. To ensure that our heuristic obtains relevant results, we have compared it with a parallel version of the standard fast nondominated sorting genetic algorithm. Finally, in order to show the goodness and effectiveness of the differential evolution with Pareto tournaments algorithm when dealing with this problem, we present diverse multiobjective comparisons with the nondominated sorting genetic algorithm and other approaches published in the literature. Copyright © 2013 John Wiley & Sons, Ltd.