一种新的D NA序列图形表达的方法

针对当前DNA序列图形表达模式中存在简并现象的相关问题,提出了一种新的二元符号图形表达方式。将四类碱基的编码过程看成是构成DNA序列的元素在直角坐标平面上的移动过程,以两种不同的标志符号来解决可能出现的元素重叠情况。此方案所标志的图形不存在自交现象,从而在DNA序列和图形表达之间建立了一一对应的关系。通过实例说明该方法在对无向图和有向图表达中均能有效地降低图形简并度,并引入人工代谢系统中的编码模式作为分析工具对DNA序列进行比较分析;以代谢中间物值作为参数,研究不同物种的DNA序列之间的相似性。实例分析表明,该参数能较好地表征不同物种之间的相似性程度高低,是一种简便可行的DNA序列特征的比较方法。     

并行型Ramsey数DNA计算模型

求解Ramsey数的困难在于需要搜索的解空间太大,而传统的电子计算机无法在有效的时间和存储空间上进行求解.由于DNA计算具有巨大的并行性和高密度存储能力等优点,文中研究了Ramsey数的DNA计算模型.针对传统的Ramsey数DNA计算模型存在的DNA序列量过多和序列过长的不足,利用DNA分子的特性以及生物操作将非解尽可能较早地消除,提出了并行型Ramsey数DNA计算模型,并以R(3,10)为例,给出了具体的求解步骤.     

DNA序列分析系统的开发应用

20世纪90年代,人类基因组计划的启动,有力推动了DNA测序工作的发展。寻找某些特征片段(功能片段)在序列中的分布规律,对遗传学、生物信息学等都有重要的应用意义。在教学、研究中发现,应用数学分析软件MATLAB的字符串处理功能,可以容易地达到功能片段分析的目的,本系统通过分析DNA序列链之间的关联程度,构造出特征矩阵,根据模糊C均值算法较准确的对DNA序列的集合进行了分类,同时利用matlab的图像显示功能将聚类的最终结果清楚明了的显示在图像中,使用户能清楚的看到聚类效果。本系统主要研究了DNA链碱基序列分析、多个DNA链特征矩阵提取、模糊C均值聚类算法分类DNA等三大部分。首先该系统对DNA序列的总长度和功能序列的长度进行了测量,利用一维数组确定功能片段在DNA序列中的位置特征,从而完成了对DNA碱基序列的分析;其次该系统对用户给出的数个DNA链进行序列之间的特征分析,统计出每个序列的(A,T,C,G)碱基密度,得到一个特征矩阵,有效的为模糊聚类分析方法提供数据来源。最终该系统应用模糊C均值聚类算法,利用特征矩阵的数值,将数个DNA序列聚类并分为两类。     

DNA序列分析系统的开发应用

20世纪90年代,人类基因组计划的启动,有力推动了DNA测序工作的发展。寻找某些特征片段（功能片段）在序列中的分布规律,对遗传学、生物信息学等都有重要的应用意义。在教学、研究中发现,应用数学分析软件MATLAB的字符串处理功能,可以容易地达到功能片段分析的目的,本系统通过分析DNA序列链之间的关联程度,构造出特征矩阵,根据模糊C均值算法较准确的对DNA序列的集合进行了分类,同时利用matlab的图像显示功能将聚类的最终结果清楚明了的显示在图像中,使用户能清楚的看到聚类效果。本系统主要研究了DNA链碱基序列分析、多个DNA链特征矩阵提取、模糊C均值聚类算法分类DNA等三大部分。首先该系统对DNA序列的总长度和功能序列的长度进行了测量,利用一维数组确定功能片段在DNA序列中的位置特征,从而完成了对DNA碱基序列的分析;其次该系统对用户给出的数个DNA链进行序列之间的特征分析,统计出每个序列的（A,T,C,G）碱基密度,得到一个特征矩阵,有效的为模糊聚类分析方法提供数据来源。最终该系统应用模糊C均值聚类算法,利用特征矩阵的数值,将数个DNA序列聚类并分为两类。     

新型灰色Verhulst 预测模型的参数特性

在灰色Verhulst 模型建模机理的基础上, 考虑相关因素对系统预测精度的影响, 构建一种新型灰色Verhulst 模型. 分析该模型参数在系统特征序列与相关因素序列经数乘变换前后的量化关系, 并分析数乘变换对该新模型建模精度的影响程度. 研究结果表明, 新型灰色Verhulst 模型的建模精度与系统相关因素序列的数乘变换有关, 而与系统特征序列的数乘变换无关. 研究结论认为, 利用数乘变换可降低该模型的建模复杂性.

     

一维时间序列分形维数算法对比分析

分形维数在一维时间序列的分形特性分析中应用非常广泛,其计算方法多种多样,但是相关计算方法的全面对比鲜见文献报道。针对常用的八种一维时间序列分形维数计算方法,以WCF合成时间序列为研究对象,分别就算法的准确性和效率,对数据长度的依赖性进行分析对比。结果表明：准确性较好的三种算法是FA,DFA和Higuchi算法;而运算效率最高的是Sevcik,Katz和Castiglioni算法,但是它们的准确性偏低,而FA和Higuchi算法在计算时间上略微增加,但准确性比较高;在数据长度为4 096点时,各算法的计算值基本稳定,尤其是FA、Higuchi和DFA算法,在数据长度为4 096点时,计算值与理论值比较吻合。由此可以得出结论,Higuchi和DFA算法在计算一维时间序列的分形维数时性能优越,在相关的计算中优先选择。     

基于矩阵图谱表达法的蛋白质序列的相似性分析

在DNA序列的混沌游走方法（CGR）及DNA序列的4线图谱表达方法（4-LGR）的基础上,提出了一种新型DNA序列的表达方法—矩阵图谱表达法（MGR）,并进一步,在DNA序列的上述三种表达式基础上,分别推广建立了基于经典HP模型的蛋白质序列的图谱表达法,对蛋白质序列的相似性进行了比较验证。研究表明：矩阵图谱表达方法不仅能够说明蛋白质序列间的相似性,而且与传统的方法相比,该方法更具有灵活性和变通性。     

非线性时间序列混沌特性分析及短期预测

为提高非线性时间序列预测的准确性和可靠性,采用基于混沌理论的方法对时间序列进行分析和预测.在研究关联维数和最大Lyapunov指数算法基础上,利用关联维数和最大Lyapunov指数判定时间序列的混沌特性,根据混沌特性参数建立预测模型,并对非线性时间序列进行预测.以上海证券交易所股票价格指数时间序列为实例验证预测模型,研究结果表明,基于混沌特性参数建立的预测模型具有较好的预测能力和预测精度,证明用该方法预测非线性时间序列具有可行性.     

m序列指令遥控系统研究初步

本文介绍了一种用m序列的平移等价序列作为指令的遥控系统.利用m序列的自相关函 数的双值特性,使指令具有纠错能力,而不增加设备的复杂程度.     

基于混沌的跳频通信系统

利用混沌信号取代跳频通信中的伪随机序列,其关键问题是混沌序列的相关特性和发射系统接收系统的混沌同步。该文提出利用Chen’s混沌序列构造跳频码,分析了该混沌序列的相关特性,并与Lorenz混沌序列进行比较,并讨论了存在于扰下的同步性能。应用MATLAAB进行仿真,结果表明该混沌序列具有良好的相关特性和稳定的同步性能,应用于跳频通信可获得较好的效果。     

CUDA并行技术与数字图像几何变换

CUDA是GPU通过并发执行多个线程以实现大规模快速并行计算能力的技术,它能使对GPU编程变得更容易。介绍了CUDA基本特性及主要编程模型,在此基础上,提出并实现了基于NVIDIA CUDA技术的图像快速几何变换。采用位置偏移增量代替原变换算法中大量乘法运算,并把CUDA技术的快速并行计算能力应用到数字图像几何变换中,解决了基于CPU的传统图像几何变换运算效率低下的问题。实验结果证明使用CUDA技术,随着处理图像尺寸的增加,对数字图像几何变换处理效率最高能够提高到近100倍。     

Minimal reversible circuit synthesis on a DNA computer

DNA computing has attracted the attention of many a researcher in recent years for its applicability to solve computationally hard problems. It can over-perform conventional computers with its inherent massively parallelism nature. On the other hand, proper synthesis of reversible circuit is a well researched computing problem in recent days for its extremely low power consumption (ideally zero) and inherent reversible nature of reversible logic. Minimum synthesis of a reversible truth table means finding the reversible circuit made up of reversible gates satisfying given truth table with minimum cost. But none of the approaches running on conventional computers can perform minimum synthesis till date without using brute-force DFS tree search. On the other hand, DFS tree-search approach can not be reasonably implemented for larger circuits as searching a DFS tree is extremely costly on a conventional computer. In this paper, first, a procedure to search a DFS tree in constant time has been proposed to run on a DNA computer. Second, another procedure has also been proposed to apply a reversible gate on a reversible truth table in linear time that can be used to generate a DFS tree library. Finally, the generated library can then be searched in constant time to get minimum reversible circuit given a reversible truth table. An analytical feasibility study has been done and a novel methodology has been developed that can extend enormous scope of future research in this area. To the best of authors’ knowledge this is a pioneering approach to bridge reversible computing and DNA computing.     

通用线程库的设计与实现

随着采集、处理、解释一体化地震勘探软件的发展,构建跨硬件、操作系统的云计算平台成为必要;而云计算平台中,通用线程库成为开发有大计算量算法的地震勘探软件的关键.通过对线程模型和同步机制的分析,分类整理出线程的属性、线程的控制、线程执行流的构建和同步的属性及操作;最后在这些通用的组件基础上,通过对各种本地线程库的封装,实现基于任意本地线程库的跨平台通用线程库.     

MPFFT: An Auto-Tuning FFT Library for OpenCL GPUs

Fourier methods have revolutionized many fields of science and engineering,such as astronomy,medical imaging,seismology and spectroscopy,and the fast Fourier transform(FFT) is a computationally efficient method of generating a Fourier transform.The emerging class of high performance computing architectures,such as GPU,seeks to achieve much higher performance and efficiency by exposing a hierarchy of distinct memories to software.However,the complexity of GPU programming poses a significant challenge to developers.In this paper,we propose an automatic performance tuning framework for FFT on various OpenCL GPUs,and implement a high performance library named MPFFT based on this framework.For power-of-two length FFTs,our library substantially outperforms the clAmdFft library on AMD GPUs and achieves comparable performance as the CUFFT library on NVIDIA GPUs.Furthermore,our library also supports non-power-of-two size.For 3D non-power-of-two FFTs,our library delivers 1.5x to 28x faster than FFTW with 4 threads and 20.01x average speedup over CUFFT 4.0 on Tesla C2050.     

An efficient scheduler of RTOS for multi/many-core system

Recently there is a trend to broaden the usage of lower-power embedded media processor core to build the future high-end computing machine or the supercomputer. However the embedded solution also faces the operating system (OS) design challenge which the thread invoking overhead is higher for fine-grained scientific workload, the message passing among threads is not managed efficiently enough and the OS does not provide convenient enough service for parallel programming. This paper presents a scheduler of master-slave real-time operating system (RTOS) to manage the thread running for the distributed multi/many-core system without shared memories. The proposed scheduler exploits the data-driven feature of scientific workloads to reduce the thread invoking overhead. And it also defines two protocols: (1) one is between the RTOS and application program, which is used to reduce the burden of parallel programming for the programmer; (2) another one is between the RTOS and networks-on-chip, which is used to manage the message passing among threads efficiently. The experimental results show that the proposed scheduler can manage the thread running with lower overhead and less storage requirement, thereby, improving the multi/many-core system performance.     

Canny边缘检测算法在飞腾平台上的实现与优化

为实现国产飞腾DSP平台对底层图像库的支持,针对原始Canny边缘检测算法计算时间过长的问题,设计一种面向FT-M7002平台的Canny梯度计算并行算法。基于FT-M7002高性能处理架构,采用单指令流多数据流向量化方式增强DSP内核指令的并行处理能力,根据FT-M7002平台向量存储器的层次结构特征,分析Canny梯度计算并行算法的访存模式,通过首地址偏移取址解决不连续访存问题,并结合双缓冲方式完成数据传输与数据计算。实验结果表明,在与原始Canny算法具有相同检测精度的情况下,该算法在卷积核大小为3×3、5×5、7×7时整体运行速度提升了1.490~2.112倍,缩小了与主流加速器件在数字图像处理领域的性能差距。     

基于CUDA的并行粒子群优化算法的设计与实现

针对处理大量数据和求解大规模复杂问题时粒子群优化(PSO)算法计算时间过长的问题, 进行了在显卡(GPU)上实现细粒度并行粒子群算法的研究。通过对传统PSO算法的分析, 结合目前被广泛使用的基于GPU的并行计算技术, 设计实现了一种并行PSO方法。本方法的执行基于统一计算架构（CUDA）, 使用大量的GPU线程并行处理各个粒子的搜索过程来加速整个粒子群的收敛速度。程序充分使用CUDA自带的各种数学计算库, 从而保证了程序的稳定性和易写性。通过对多个基准优化测试函数的求解证明, 相对于基于CPU的串行计算方法, 在求解收敛性一致的前提下, 基于CUDA架构的并行PSO求解方法可以取得高达90倍的计算加速比。     

基于动态profiling技术的流水粒度调优

结点间流水是解决数据分布和计算分割不一致时的一种重要的并行发掘技术.结点间流水通过计算与通信的重叠获得并行度.精确的流水粒度是获得良好的流水性能的关键.流水分块取决于很多因素,如程序规模、程序的访问模式、结点规模、结点的计算能力和存储体系、通信系统的性能、通信库开销等等.提出了动态profiling方式并实现在流水粒度的推导中,运行时信息收集部分典型分块,结合代价模型推导流水粒度,该模型考虑局部性优化;探索如何减少插桩执行的开销的同时保证代价模型的精度.实验证明,这种方式有更好的适应性,能获得较好的流水并行.     

A framework for parallel computational physics algorithms on multi-core: SPH in parallel

In this paper, a simulation framework that enables distributed numerical computing in multi-core shared-memory environments is presented. Using multiple threads allows a single memory image to be shared concurrently across cores but potentially introduces race conditions. Race conditions can be avoided by ensuring each core operates on an isolated memory block. This is usually achieved by running a different operating system process on each core, such as multiple MPI processes. However, we show that in many computational physics problems, memory isolation can also be enforced within a single process by leveraging spatial sub-division of the physical domain. A new spatial sub-division algorithm is presented that ensures threads operate on different memory blocks, allowing for in-place updates of state, with no message passing or creation of local variables during time stepping. Additionally, the developed framework controls task distribution dynamically ensuring an events based load balance. Results from fluid mechanics analysis using Smoothed Particle Hydrodynamics (SPH) are presented demonstrating linear performance with number of cores.     

A GPU-Accelerated In-Memory Metadata Management Scheme for Large-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50％under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.