Load-balanced parallel streamline generation on large scale vector fields

Because of the ever increasing size of output data from scientific simulations, supercomputers are increasingly relied upon to generate visualizations. One use of supercomputers is to generate field lines from large scale flow fields. When generating field lines in parallel, the vector field is generally decomposed into blocks, which are then assigned to processors. Since various regions of the vector field can have different flow complexity, processors will require varying amounts of computation time to trace their particles, causing load imbalance, and thus limiting the performance speedup. To achieve load-balanced streamline generation, we propose a workload-aware partitioning algorithm to decompose the vector field into partitions with near equal workloads. Since actual workloads are unknown beforehand, we propose a workload estimation algorithm to predict the workload in the local vector field. A graph-based representation of the vector field is employed to generate these estimates. Once the workloads have been estimated, our partitioning algorithm is hierarchically applied to distribute the workload to all partitions. We examine the performance of our workload estimation and workload-aware partitioning algorithm in several timings studies, which demonstrates that by employing these methods, better scalability can be achieved with little overhead.     

Optimal Error Estimates of the Legendre Tau Method for Second-Order Differential Equations

In this paper, we prove that the Legendre tau method has the optimal rate of convergence in L ²-norm, H ¹-norm and H ²-norm for one-dimensional second-order steady differential equations with three kinds of boundary conditions and in C([0,T];L ²(I))-norm for the corresponding evolution equation with the Dirichlet boundary condition. For the generalized Burgers equation, we develop a Legendre tau-Chebyshev collocation method, which can also be optimally convergent in C([0,T];L ²(I))-norm. Finally, we give some numerical examples.     

A system-level model for a silicon thermal flow sensor

A system-level model with lumped parameters for a thermal flow sensor is presented. The model is built with 13 circuit cells consisting of thermal resistors and thermal capacitors in SPICE. The circuit cell originates from the heat conduction equation using the Finite Differential Method, including the 2-D thermal conduction cell, the convection cell, and the thermal capacity in the chip. Based on the thermal model of the flow sensor, the 2-D temperature distribution of the chip can be calculated with SPICE in both the constant power mode (CP) and constant temperature difference mode (CTD). As an example, the system level model of the thermal anemometer in the CTD mode was established in PSPICE. Wind tunnel test was carried out to verify the system model, and show a reasonable agreement with the simulation results, with an error less than 8%.     

VLCC中的DAG并行算法

基于组件的密码学虚拟实验室（VLCC）采用有向无环图（DAG）的拓扑排序机制管理组件。在分析VLCC各组件之间的数据依赖和运行次序关系的基础上,提出一种新的基于Java多线程机制和“唤醒”机制的DAG并行算法。与拓扑排序算法相比,具有低算法时间复杂度的特点。实验结果表明,系统在新算法下较大地缩短了系统运行时间,提高资源使用效率和用户满意度,能更好地完善VLCC。     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.     

Investigation of point-to-point performance test of touch trigger probes on coordinate-measuring machines

A major factor contributing to the total measuring error of coordinate measuring machines (CMMs) is the performance of the probing sub-system. Probing test methods are typically used to detect errors due to the probing sub-system. The probe performance evaluation method specified in the ANSI B89 standard is investigated in this paper. The sampling plan associated in the probe performance evaluation was tested by using experimental probing data from a CMM. Research findings indicate that the performance of touch trigger probes is overestimated due to a systematic bias in the vertical direction of the best-fit reference ball center in the probe performance test. A two-latitude sampling plan synthesis method based on a pretravel model for touch trigger probes is proposed in this paper. The proposed method can be used to accurately identify the reference ball center in the performance test of touch trigger probes.     

Pseudoelastic behavior in Y1−xPrxBa2Cu3Oy compounds

Tensile tests show that ferroelastic loops always occur at 100–150 K, 200–250 K and room temperature in the Y_1–xPr_xBa₂Cu₃O_y samples withx=0 and 0.1 for whichT _c is 92 K and 82.5 K respectively, and the shape memory effect is always observed in the compact sample near 130 K, just similar to that of thermal elastic martensitic alloys. The loss fraction (W/W) which is proportional to the area of ferroelastic loop as a function of temperature shows that there always exist static hysteresis (W/W) peaks at 130 K and 110 K which are attributed to the phaselike transition (PLT) characterized by the jump of lattice parameters. No ferroelastic loops and shape memory effect are observed in the range of 100 K to 150 K for the lowerT _c samples withx=0.3, 0.4 and 0.6.     

水和甲醇存在下MAMS的消失反应动力学研究

通过实验研究了甲基丙烯酰胺硫酸盐（ＭＡＭＳ）与甲醇、水进行反应的动力学，并对该反应的机理进行了探讨。