一种非对称6-PSS并联机构的工作空间和灵活度研究

针对非对称并联机构在某些方面有着特殊的应用但研究较少的现状，对一种非对称6-PSS并联机构工作空间和灵活度进行了研究，得到了其工作空间和灵活度随结构参数和姿态要求的变化规律，得出此机构可以应用在y轴方向工作空间要求大，而绕x轴转角要求不高的场合。为进一步扩大并联机构的实际应用提供了依据。     

基于生物启发计算的警用流动人口分析系统

目前对流动人口的管理仅停留在数据查询比对和简单统计上,缺少对数据的深层次分析,难以对决策指挥提供支持。针对流动人口的分析问题,提出了构建一个基于生物启发计算的智能分析系统,用于发现流动人口中各类人员的流动模式以及流动人口的趋势性问题,找出异常的流动信息和模式。该系统综合运用了前沿的生物启发计算技术——基于多层染色体基因表达式编程算法、重叠基因表达进化算法、基于概念相似度神经网络分类模型和层次距离计算的聚类算法搭建了一个警用流动人口的分析平台。同时根据实际需求,提出了一种新的基于智能分析结果的分级报警模型。实验表明系统具有较高的性能和实用性。     

基于运动误差约束的平面定位平台拓扑优化设计

针对传统平面定位平台拓扑优化设计过程中无法预先了解输入输出位移映射矩阵的问题,基于固体各向同性材料惩罚法,采用遗传算法优化得到具有最大运动空间时的平面并联机构雅克比矩阵作为平面定位平台输入输出期望位移映射矩阵,在相同输入条件下,建立以机构实际运动与期望运动间误差最小为目标函数和以机构体积为约束函数的平面定位平台拓扑优化模型并求解。通过3D打印方式得到实验样品,实验与仿真分析表明:采用运动误差约束的平面定位平台拓扑优化设计方法,在一定偏差范围能够得到输入输出位移映射矩阵与给定期望映射矩阵相一致的平面定位平台,表明该方法对平面定位平台设计具备有效性,为其后续研究提供基础。     

Particle-in-Cell algorithms for emerging computer architectures

We have designed Particle-in-Cell algorithms for emerging architectures. These algorithms share a common approach, using fine-grained tiles, but different implementations depending on the architecture. On the GPU, there were two different implementations, one with atomic operations and one with no data collisions, using CUDA C and Fortran. Speedups up to about 50 compared to a single core of the Intel i7 processor have been achieved. There was also an implementation for traditional multi-core processors using OpenMP which achieved high parallel efficiency. We believe that this approach should work for other emerging designs such as Intel Phi coprocessor from the Intel MIC architecture.     

GPU accelerated online multi-particle beam dynamics simulator for ion linear particle accelerators

An online beam dynamics simulator is being developed for use in the operation of an ion linear particle accelerator. By employing Graphics Processing Unit (GPU) technology, the performance of the simulator has been significantly increased over that of a single CPU and is therefore viable in the demanding accelerator operations environment. Once connected to the accelerator control system, it can rapidly respond to any control set point changes and predict beam properties along an ion linear accelerator in pseudo-real time. This simulator will be a virtual beam diagnostic tool which is especially useful when direct beam measurements are not available. Details about the code structure design, physics algorithms, GPU implementations, and performance are presented.     

Fast computation of computer-generated hologram using Xeon Phi coprocessor

We report fast computation of computer-generated holograms (CGHs) using Xeon Phi coprocessors, which have massively x86-based processors on one chip, recently released by Intel. CGHs can generate arbitrary light wavefronts, and therefore, are promising technology for many applications: for example, three-dimensional displays, diffractive optical elements, and the generation of arbitrary beams. CGHs incur enormous computational cost. In this paper, we describe the implementations of several CGH generating algorithms on the Xeon Phi, and the comparisons in terms of the performance and the ease of programming between the Xeon Phi, a CPU and graphics processing unit (GPU).     

基于FPGA的高速数据存储系统优化设计

针对遥测系统数据记录装置中数据传输速率与存储速率不匹配的问题,提出Flash的并行存储方案,采用交替双平面的编程方式可以使得存储器的存储速率达到单片Flash最高存储速率的2倍,即60 MB/s;对控制单元FPGA内部双端口RAM的逻辑设计进行改进,解决了数据存储异常的现象。在数据回收方面,提出了多备份的设计思想和备用读数接口的设计方案,已在工程应用中得到成功实践,验证了该数据记录装置的可靠性。     

A simple method to solve the forward displacement analysis of the general six-legged parallel manipulator

In this work a simple method to solve the forward displacement analysis of the general 6-6 fully parallel manipulator is applied. The method is based on generating closure equations upon the unknown coordinates of three points embedded to the moving platform. The method is easy to follow and it is available for both, planar and three-dimensional moving platforms. Numerical examples are included with the purpose to show the application of the method.     

Detecting stable locality-aware predicates

In a large-scale locality-driven network such as in modular robotics and wireless sensor networks, knowing the state of a local area is sometimes necessary due to either interactions being local and driven by neighborhood proximity or the users being interested in the state of a certain region. We define locality-aware predicates (LAP) that aim at detecting a predicate within a specified area. We model the area of interest as the set of processes that are within a breadth-first search tree (BFST) of height k$k$ rooted at the initiator process. Although a locality-aware predicate specifies a predicate only within a local area, observing the area consistently requires considering the entire system in a consistent manner. This raises the challenge of making the complexities of the corresponding predicate detection algorithms scale-free, i.e., independent of the size of the system. Since all existing algorithms for getting a consistent view of the system require either a global snapshot of the entire system or vector clocks of the size of the system, a new solution is needed. We focus on stable LAP, which are those LAP that remain true once they become true. We propose a scale-free algorithm to detect stable LAP within a k$k$-height BFST. Our algorithm can detect both stable conjunctive LAP and stable relational LAP. In the process of designing our algorithm, we also propose the first distributed algorithm for building a BFST within an area of interest in a graph, and the first distributed algorithm for recording a consistent sub-cut within the area of interest. This paper demonstrates that LAPs are a natural fit for detecting distributed properties in large-scale distributed systems, and stable LAPs can be practically detected at low cost.     

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. A major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diverse manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. The Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.