LED大屏幕动态显示的实现

本文介绍了一种基于并行通讯的ＬＥＤ大屏幕动态显示的实现方案，包括发送和接收的软，硬件设计以及多机通讯技术。     

基于波动方程的聚焦点控制照明叠前深度偏移

 基于波动方程的聚焦点控制照明叠前深度偏移技术借助于差分计算，把速度、密度等介质参数的影响体现在差分计算的矩阵方程中，能够自动适应速度场的任意变化，快速傅里叶变换的使用也加速了波场延拓的计算速度。因此此法兼具有限差分偏移方法和傅里叶偏移方法的优点，既可适应速度场的剧烈变化，又可保证对陡倾地层的成像效果，是目前针对复杂构造最有效的成像方法之一。对于单个聚焦点及其周围的成像步骤为：①采用矩形网格情况下绕射走时的有限差分计算方法生成合成算子；②应用合成算子来合成面炮震源和面波记录；③对合成的面炮震源和面波记录做傅里叶有限差分法波动方程叠前深度偏移，得到该聚焦点及其附近区域的成像结果。按照上述成像步骤，将震源波场和炮集记录依据相应的外推公式进行延拓，最终应用成像条件求取成像值。在地质目标处选取多个聚焦点，可以得到面向目标的控制照明偏移成像，在多个层位上选取多个聚焦点进行控制照明叠前深度偏移，可以得到整块区域的成像。通过对Marmousi模型的试算，取得了较好的效果。     

Parallelism in ILU-preconditioned GMRES

A parallel implementation of the preconditioned GMRES method is described. The method is used to solve the discretized incompressible Navier–Stokes equations. A parallel implementation of the inner product is given, which appears to be scalable on a massively parallel computer. The most difficult part to parallelize is the ILU-preconditioner. We parallelize the preconditioner using ideas proposed by Bastian and Horton (P. Bastian, G. Horton, SIAM. J. Stat. Comput. 12 (1991) 1457–1470). Contrary to some other parallel methods, the required number of iterations is independent of the number of processors used. A model is presented to predict the efficiency of the method. Experiments are done on the Cray T3D, computing the solution of a two-dimensional incompressible flow. Predictions of computing time show good correspondence with measurements.     

Locality-preserving hash functions for general purpose parallel computation

Consider the problem of efficiently simulating the shared-memory parallel random access machine (PRAM) model on massively parallel architectures with physically distributed memory. To prevent network congestion and memory bank contention, it may be advantageous to hash the shared memory address space. The decision on whether or not to use hashing depends on (1) the communication latency in the network and (2) the locality of memory accesses in the algorithm.We relate this decision directly to algorithmic issues by studying the complexity of hashing in the Block PRAM model of Aggarwal, Chandra, and Snir, a shared-memory model of parallel computation which accounts for communication locality. For this model, we exhibit a universal family of hash functions having optimal locality. The complexity of applying these hash functions to the shared address space of the Block PRAM (i.e., by permuting data elements) is asymptotically equivalent to the complexity of performing a square matrix transpose, and this result is best possible for all pairwise independent universal hash families. These complexity bounds provide theoretical evidence that hashing and randomized routing need not destroy communication locality, addressing an open question of Valiant.This work was started when the author was a student at Oxford University, supported by a National Science Foundation Graduate Fellowship and a Rhodes Scholarship. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Science Foundation or the Rhodes Trust.     

ConcurrentC语言在分布式系统上的一种高效实现

本文介绍了ＣｏｎｃｕｒｒｅｎｔＣ语言在一分布式系统上的实现。该实现使用了不同于预编译法的编译器的构造方法，同时将Ｃｏｎｃｕｒｒｅｎｔ Ｃ语言中的进程实现为ＵＮＩＸ进程中的用户级线索，是ＣｏｎｃｕｒｒｅｎｔＣ语言在分布式系统上的一种高效实现。     

基于进化计算的兴趣建模

为实现对个体的兴趣建模,提出基于进化计算的建模(Evolutionary Computation Based Modeling,ECBM)方法。该方法采用定性和定量相结合的方式对个体的兴趣系统进行建模,首先通过定性分析构建个体的兴趣模型框架,然后通过进化计算(Evolutionary Computation)方法对模型参数自动进化。实验结果表明,ECBM能够通过数据驱动模式优化模型参数,并利用更新信息进化模型,使其不断逼近真实系统。     

EFFECTIVE PARALLELIZATION TECHNIQUES FOR LOOP NESTS WITH NON-UNIFORM DEPENDENCES

The parallelism of loop nests with non-uniform dependences is difficult to extract and ineffectively explored by the existing parallelization schemes. In this paper, we propose new efficient techniques in extracting parallelism of loop nests with non-uniform dependences using their irregularity. By this way, current highly parallel multiprocessor systems such as multithreaded and clustering multiprocessor systems can be fully utilized. These four mechanisms are (a) parallelization part splitting, (b) partial parallelization decomposition, (c) irregular loop interchange and (d) growing pattern detection. They explore parallelisms of special parallel patterns for nested loops with non-uniform dependences. The loop transformations used in uniform loops are also applied in non-uniform dependence loops after legality tests. We apply the results of classical convex theory and detect special parallel patterns of dependence vectors. We also proposed an algorithm that combines above mechanisms to enhance parallelism. We demonstrate that our technique gives much better speedup and extracts more parallelism than the existing techniques. Thus, we are encouraged by these apparent enhancements to pursue further development.     

A note on circle packing

The problem of packing circles into a domain of prescribed topology is considered. The circles need not have equal radii. The Collins-Stephenson algorithm computes such a circle packing. This algorithm is parallelized in two different ways and its performance is reported for a triangular, planar domain test case. The implementation uses the highly parallel graphics processing unit (GPU) on commodity hardware. The speedups so achieved are discussed based on a number of experiments.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.