基于OpenCL的双调排序算法的优化

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OpenCL异构并行计算：原理、机制与优化实践

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8.3.2 存储器层次
9 OpenCL计算二维卷积
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9 OpenCL计算二维卷积
9.1 测试平台信息
9.2 AMD X86 CPU串行实现
9.3 简单 OpenCL实现
9.4 使用常量存储器优化
9.5 使用局部存储器优化
9.6 一个工作项同时计算多个输出
9 OpenCL计算二维卷积
4.16.4 设备队列的使用示例
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4 OpenCL C语言
4.16 设备队列
5 OpenCL存储器对象
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5 OpenCL存储器对象
5.1 缓冲区 5.3 管道
5.5 共享虚拟存储器
5.2 图像对象和采样器对象
5.4 存储器对象数据传输
5.6 存储器一致性模型
5 OpenCL存储器对象

5.5.1 SVM 缓冲操作
5.5.2 SVM 类型和特性
5.5.3 相关示例
5 OpenCL 存储器对象
5.6 存储器一致性模型

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5.6.1 存储器次序规则 5.6.2 原子操作的存储器次序规则
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5.6.3 栅栏操作的存储 5.6.4 工作组函数的存
8.3 ARM Mali GPU架构
8.4 本章小结
8 OpenCL到主流GPU处理器的映射
8.1.1 AMD Cayman架
构GPU
8.1.2 AMD GCN 架构的GPU
8.1 AMD家族GPU
8 OpenCL到主流GPU 处理器的映射
8.2 NVIDIA CUDA兼容的 GPU

1 异构并行计算的过去、现状和未来

基于OpenCL的FPGA设计优化方法研究

（ＵｎｉｖｅｒｓｉｔｙｏｆＥｌｅｃｔｒｏｎｉｃＳｃｉｅｎｃｅａｎｄＴｅｃｈｎｏｌｏｇｙｏｆＣｈｉｎａ，Ｃｈｅｎｇｄｕ６１００３６，Ｃｈｉｎａ）
Ｏｐｔｉｍｉｚａｔｉｏｎｍｅｔｈｏｄｒｅｓｅａｒｃｈｏｆ０ｐｅｎＣＬ— ｂａｓｅｄＦＰＧＡｄｅｓｉｇｎ
ＦａｎＸｉｎｇｓｈａｎ，ＰｅｎｇＪｕｎ，ＨｕａｎｇＬｅｔｉａｎ
ＥｍｂｅｄｄｅｄＴｅｃｈｎｏｌｏｇｙ
基于ＯｐｅｎＣＬ的ＦＰＧＡ设计优化方法研究术
范兴山，彭军，黄乐天
（电子科技大学，四川成都６１００３６）
摘要：ＦＰＧＡ因其强大的运算能力成为了众多高性能应用的最佳选择，但其传统的开发方法存
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OpenCL用于计算机领域的13个经典案例（转）

OpenCL用于计算机领域的13个经典案例（转）摘要：当使用加速器和OpenCL时，哪种类型的算法更加快速？来自弗吉尼亚理工大学的Wu Feng教授和他的团队例举了一份算法列表，分享了OpenCL常被用于计算机领域的13个经典案例。

哪种算法可以最好的映射GPU及矢量处理器呢？换句话说，当使用加速器和OpenCL时，哪种类型的算法更加快速？来自弗吉尼亚理工大学的Wu Feng教授和他的团队例举了一份算法列表，分享了OpenCL常被用于计算机领域的13个经典案例。

有人将其称之为OpenCL计算领域的13个“小巨人”。

一、Dense Linear Algebra（稠密线性代数）经典的向量和矩阵运算，传统上可分为1级（矢量/矢量vector/vector），2级（矩阵/矢量），3级（矩阵/矩阵），应用范围极其广泛。

应用范围：•线性代数：LAPACK， ATLAS。

•Clustering algorithms （聚类算法）/ Data-mining（数据挖掘）：StreamCluster， K-均值算法。

正常情况下执行循环，但大多数情况下可轻易在OpenCL进行并行计算。

二、Sparse Linear Algebra（稀疏线性代数）乘法运算主要是由零矩阵组成。

通过移动对角矩阵周围的非零元素，使计算更加高效。

应用范围：•有限元素分析。

•偏微分方程式。

使用OpenCL有两种方法：通过一些列的操作行为解决该问题，这将导致很大一部分开销；第二种方法是使用一些列连续的逐次逼近法，将函数误差最小化。

三、Spectral Methods（光谱法）各种结构的物质都具有自己的特征光谱，光谱分析法就是利用特征光谱研究物质结构或测定化学成分的方法。

光谱方法可用来解决常微分方程（ODEs），偏微分方程（PDEs）以及包含微分方程增值问题。

应用范围：•流体动力学。

•量子力学。

•天气预测。

利用OpenCL针对每个硬件架构有各种FFT实施方法。

github上的opencl demo实例-概述说明以及解释

github上的opencl demo实例-概述说明以及解释1.引言1.1 概述OpenCL是一种开放的、跨平台的并行计算框架，可以用于在各种计算设备上进行高性能计算，包括CPU、GPU、FPGA等。

它提供了一套标准化的编程接口，使得开发者们能够更加方便地利用计算设备的并行能力进行开发。

在GitHub上，有很多开源的OpenCL Demo可以供开发者学习和参考。

这些Demo通常包含了一些基本的OpenCL应用案例，如矢量加法、矩阵乘法等。

通过阅读并分析这些Demo代码，开发者可以更深入地了解OpenCL的一些基本概念和编程模型，以及如何在实际应用中进行并行计算。

本文将对一些在GitHub上的OpenCL Demo进行介绍和分析，帮助读者理解OpenCL的基本原理和应用。

我们将选取一些典型的Demo实例进行详细的分析，包括其功能、实现方式、性能优化等方面的内容。

通过这些实例，读者可以更好地掌握OpenCL的使用方法，并能够借鉴其中的优秀实践经验。

通过本文的阅读，读者可以对OpenCL的概念和应用有一个全面的了解，同时可以学习到一些在实际开发中使用OpenCL的技巧和经验。

希望读者通过本文的学习能够在自己的项目中充分利用OpenCL的并行计算能力，以提高程序的性能和效率。

接下来，我们将首先介绍OpenCL的基本概念和原理，以便读者对其有一个基本的了解。

然后，我们将选择一些优秀的OpenCL Demo进行分析，从中学习并借鉴其中的开发技巧和经验。

最后，我们将对这些Demo 进行总结和评价，并展望OpenCL在未来的发展方向。

1.2 文章结构文章结构部分的内容应该包括以下信息：文章结构部分旨在向读者介绍本篇文章的组织结构和内容安排。

本文将分为引言、正文和结论三个部分来展开讨论。

在引言部分，将首先对本文的主题进行概述，介绍OpenCL Demo在GitHub上的重要性和应用价值。

接着，引言部分将描述文章的结构和内容安排，以便读者能够更好地理解整篇文章的目的和内容。

OpenCL Snort性能优化与并行设计说明书

Parallel Design and Performance Optimization based on OpenCLSnortHongying Xie*,Yangxia Xiang †, Caisen Chen †*Unit 61175, China†Academy of Armored Force Engineering, ChinaKeywords: OpenCL snort, GPU, AC algorithm, parallel programmingAbstractWith the rapid increasement of the network speed and number of threats which hide in the network poses enormous challenges to network intrusion detection systems (NIDS). As the most popular NIDS, snort can run as a single threaded application. However, it may not be able to detect intrusions in real-time especially in networks with high traffic. In this paper, a parallel module OpenCL Snort (OCLSnort) is introduced: realize parallel pattern matching algorithm using GPU and innovate new architecture which is more suitable for the parallel algorithm. The result showed that OCLSnort can detect the attacks correctly and effectively, the new system not only has markedly improved on throughput, also effectively reduced the CPU utilization and memory usage.1 IntroductionIntrusion detection systems (IDSs) are of critical importance to the integrity of computer networks due to massive growth in the data transmission speed and the frequency of attacks. With the rapid development of computer network, more and more data need to be searched, analyzed and detected whether they have threat or not. Such as network monitoring application snort, which is an open source network intrusion prevention and detection system (IDS/IPS) developed by Sourcefire. Combining the benefits of signature, protocol, and anomaly-based inspection, and as so far, Snort is the most widely deployed IDS/IPS technology worldwide [1].In snort, they are using pattern matching algorithm such as AC, BM algorithm to detect thread. Pattern matching is one of the core operations used by applications such as traffic classification [2], intrusion detection systems [3] and content monitoring filters [1]. Unfortunately, packet detecting part occupies the most of the time of the whole processing time in modern NIDSes [4,5] and this operation has significant overheads in terms of both memory space and CPU cycles, so when the data or packet which will be detected is very large, there will be packet-losing problem about snort.Several research efforts have used GPU for security purposes, including cryptography [6], data carving [7] and intrusion detection [8]. And Jacob and Brodley were the first that tried to use the GPU as a pattern matching engine for NIDS in PixelSnort [8]. They changed KMP algorithm to parallel version but the performance result is not very ideal. This paper is organized as follows: In Section 2 and 3, two methods to realize OpenCL snort are presented. In section 4, we evaluate our implementation and compare with the original snort. Experimental results and analysis are given. Finally, conclusions are given in Section 5.2 ArchitectureThe overall architecture of Snort NIDS is shown in Fig.1 and the OpenCL version Snort’s architecture is showed in Fig.2. From Fig.1 and Fig.2, there are some differences between the original snort and the new version snort, one is collecting packets at packet classification part; the other is detecting packet content at packet detecting part.g ss2.1 Packet detecting using OpenCL AC algorithmFor the multi-pattern matching algorithm, the first thing is to build DFA such as Fig.3, and this section is finished before2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)the beginning of the packet detected in snort. In our design of the OpenCL version Snort, the realized DFA is represented as a two-dimensional state table array that is mapped on the memory space of the GPU. The dimensions of the array are equal to the number of states and the size of the alphabet (256 in our case), respectively. Each cell contains the next state to move, as well as an indication of whether the state is a final state or not.Fig.3 AC State Machine of P atterns “he”, “hers”, “his”, “she” Fig.3 shows a state machine of patterns which used in our OpenCL AC algorithm, from this figure we can see that the difference between original AC state machine and OpenCL AC state machine is that whether it is needed about failure transitions. The failure transitions are used to back-track the state machine to recognize patterns in any location of an input stream. Given a current state and an input character, the original AC machine first looks up the valid transition table to check whether there is a valid transition for the input character; otherwise, the machine looks up the failure transition table and jumps to the failure state where the failure transition points. Then the machine regards the same input character until the character causes a valid transition. In our OpenCL version snort, we used OpenCL to change the AC algorithm for parallelism based on PFAC [9]. The idea of the parallel algorithm of AC is: Give an input stream have N byte, we will create N threads corresponding to N byte. And for each thread, it is only responsible for identifying the pattern starting at the thread starting position. So in OpenCL AC algorithm, the failure transitions of the AC state machine can all be removed as well as the self-loop transition of the initial state. And the whole process of the OpenCL AC is showed by Fig.4.Fig.4 OpenCL AC Algorithm Execution ProcessThere are several characteristics of the OpenCL AC algorithm. First, although it creates huge amounts of threads, most threads have a high probability of terminating very early because a thread in OpenCL AC is only responsible for matching the pattern beginning at its starting position. Second, the maximum detection length of each thread is the maximumlength in whole patterns, and based on this, when the larger the input stream is, the faster the detect speed is. And finally, the failure transitions are all removed when we are using OpenCL AC, and this simplifies the algorithm and the thread can detect the input stream automatically without rollback. 2.2 Packet collecting and transfer to GPUBefore the packet detecting in GPU, the first thing must to consider is how and how many packets will be transferred from the network to the GPU memory. The simplest method is according to the original snort architecture, transfer one packet to GPU for processing once time. However, as we know, the TCP or UDP packet size is usually hundreds byte,the performance is much better batching many small transfers into a large one than making each transfer separately [16].Thus, we realized the two methods (1) using original snort architecture, transferring one packet to GPU once time, and (2)change packet classification part, transferring more than onepackets to GPU once time and get the performancecomparison based on two methods. As we know, the process flow of original snort is showed as Fig.1: capture a packet from network once time, then packet analysis and classification, detecting packet and output the result finally. Using method (1), the process flow can be changed as follows: capture a packet from network, packet analysis and classification is not changed, then transfer the packet to GPU and detecting it using OpenCL AC algorithm, then transfer the results to CPU and output the result finally. So using method (1), we changed Detection part, using OpenCL AC take the place of original ac algorithm and the other part of snort ’s architecture are not changed, processing packet one by one.And using method 1, the performance improvement is not exciting, there are two reasons: (1) the DMA time occupied most of the time; (2) the input stream transferring to GPU only have hundreds byte each time. It does not make full use of GPU resources. Based on this, we proposed a new method that can transfer more than one packets to GPU, the architecture of OpenCL Snort are showed by Fig.2. From Fig.2 we can see the difference between OpenCL Snort and original snort is processing packet number once time. In OpenCL version Snort, we change the interface to realizecapture multi-packets at the beginning of snort and then deal with packets, transfer multi-packets to GPU once time, and finally output alerts/logs.3 Implementation In this section, we are showed the implementation details about the OpenCL version snort. In snort, they are using different rules to detecting whether the packet has threatened or not according to packet type. Different rules create different state transition tables. So we are focus on the packets collecting and the state transition tables correspond to packets part when using method (2) transfer multi-packets to GPU once time.3.1 Transferring a Single Packet to GPUIn this approach, when capturing a packet from network, snort will decode and classify it, then send it to GPU for detecting,send the result to CPU and finally output the result.Assume the packet has N characters, the algorithm will create N threads in GPU if the device has this ability, and else they will create maximum threads which under the device’s ability, then each thread will loop many times to detecting the wholeThis method is very simple, because there is only one packet, and the state transition table which transferred to GPU also has one. So this method need not to find out which state transition table is corresponding to which packet. A drawback of this approach is that the input stream is very small and the DMA time occupied most of the time, so the GPU is not utilized effectively.3.2 Transferring Multi-packets to GPUIn this approach, we will mark the packets which we interfered to GPU as unique packetID, and give a unique tableID for each state transition table which finished creation process and transfer all the state transition tables to GPU. The whole process will be finished at the initialization phase of snort.Using this approach to detect packets, the way to creating threads is the same as method (1), and the difference is the packet must correspond to the state transition table. And this could be solved adding elements packetID and tableID to struct ACSM, and we will also transfer those elements to GPU. In the OpenCL algorithm, we must to judge the packet boundaries in order to get the correct results. The process flow is showed by Fig.6, and example of packets collecting process is showed by Fig.7. From Fig.7, each packet corresponds to a state transition table, so when we transfer packets to GPU, we must to determine the transition table’s address corresponding to each packet.Although this method is complicated comparing with method (1), the input stream transferring into GPU once time is muchindex n4 EvaluationPattern matching is the most critical operation in the snort system. Usually pattern matching algorithm can be classified into single pattern matching algorithm (such as KMP) and multi-patterns algorithm (such as AC).In this section, we explore the performance of our implementation. We realize the two approaches in Snort and compare the two methods with the original snort respectively. In processing multi-packets method, we change the parameter about the collecting packet number once time then get the average time about processing one packet.In our experiments we used an AMD A10-4600m computer, the CPU in this computer is 2.3GHz APU with Radeon™ HD Graphics 4 processor , 8G memory and GPU is AMD Radeon HD 7660G card, the operating system is Ubuntu12.04 64-bit. We get the packets data LLS DDOS 1.0-inside.dump from MIT Lincoln Laboratory [17] as the detected data, we also using snort to dump some small packets date set using the detected data LLS DDOS 1.0-inside.dump, such as contain 200 packets date set, 1000 packets date set, 10000 packets date set and 20000 packets date set, and we read the packets from disk rather than network in order to get the same speed of capture packet in different experiments. We also using the default rules file when using different version snort and this can ensure the correctness of the result.For all experiments, we disregard the time spent in the initialization phase of snort as well as the log of the alerts to the disk or terminal. Even though it only takes just a few seconds to load rule files and build its internal structures. And we used the full AC implementation to measure the performance in original snort.4.1 Performance Comparison between One Packet OpenCL snort and Original snortIn this experiment, input1, input2 and input3 are three different size detected packets and the packets size is 200, 1000 and 10000 respectively. We change the input packet numbers to get the performance data about one packet OpenCL snort and original snort and the performance data is showed by Fig.8. From Fig.8, (1) with the increase of input packet size, the throughput of two methods becomes large; (2)using one packet OpenCL snort, the throughput is not batter than the original snort’s throughput, because the local memory is not large enough, the state transition table is stored in Global memory, when judge the current character meet the conversion criteria or not each time, the algorithm must access the global memory once time; and most of threads are terminated at the beginning of the algorithm, and the GPU’s utilization is not high.Fig.8 Performance Comparison4.2 Performance Analysis about Multi-Packets OpenCL snortIn this experiment, we get the performance comparison about multi-packets OpenCL snort and one packet OpenCL snort. Before this comparison, first thing we must to ensure is when we transfer how many packets to GPU, the algorithm will get the best performance and maximum throughput. Fig.9 showed the algorithm’s performance comparison when transferring different number of packets to GPU. From Fig.9 we can see with the number’s difference, the throughput has some difference as well. When the number which transfers to GPU once time is 30, the throughput is 4.78Gbits/sec, when the number is 100, the throughput is up to 6.43Gbits/sec. And when the number changes from 150 to 200, the throughput grows slowly and then it has a downward trend. So we select 200 as the number which transfers to GPU once time.Fig.9 Performance Comparison of Multi-packets OpenCLSnortThe next experiment we are focus on is the performance comparison about the three version snort: original snort, one packet OpenCL snort and multi-packets OpenCL snort. And the result is showed by Fig.10. In this figure, input1, input2 and input3 are three different size detected packets as the same as Fig.9, the packets size is 150, 1200 and 10000 respectively. From the result, we can see the multi-packets OpenCL snort’s throughput is about two times faster than other two methods. And the GPU’s utilization in multi-packets OpenCL snort is much higher than the one packet OpenCL snort.Fig.10 Performance Comparison on Different Versions 5 ConclusionsIn this paper, we have proposed two OpenCL version snort, one packet OpenCL snort and multi-packets OpenCL snort in order to accelerate packet detecting by GPU. And the result showed that although one packet OpenCL snort’s throughput is about 20% slower than original snort, multi-packets OpenCL snort is about 2 times faster than original snort, and this system was able to achieve a maximum throughput of 6.758Gbit/s.AcknowledgementsThis work is supported by the National Natural Science Foundation of China under Grant No. 61402528, all support is gratefully acknowledged.References[1] Snort: : Home Page. /.[2] Application Layer Packet Classifier for Linux. http://17-/.[3] Clam AntiVirus. /.[4] S. Antonatos, K. Anagnostakis, and E. Markatos.Generating realistic workloads for network intrusion detection systems. In Proceedings of the 4th ACM Workshop on Software and Performance, (2004).[5] J. B. D. Cabrera, J. Gosar,W. Lee, and R. K. Mehra. Onthe statistical distribution of processing times in network intrusion detection. In 43rd IEEE Conference on Decision and Control, 75-80, (2004).[6] D. L. Cook, J. Ioannidis, A. D. Keromytis, and J. Luck.Cryptographics: Secret key cryptography using graphics cards. In Proceedings of RSA Conference, Cryptographer’s Track. 334-350, (2005).[7] G. G. R. I. Lodovico Marziale and V. Roussev. Massivethreading: Using GPUs to increase the performance of digital forensics tools. Digital Investigation. 73–81. [8] N. Jacob and C. Brodley. Offloading IDS computation tothe GPU. In Proceedings of the 22nd Annual Computer Security Applications Conference on Annual Computer Security Applications Conference, Washington, DC, USA, IEEE Computer Society. 371–380, (2006).[9] Lin CH, Tsai SY, Liu CH, Chang SC, Shyu. JMAccelerating string matching using multi-threaded algorithm on gpu. In: GLOBECOM, 1-5, (2010). [10] C. IOS. IPS deployment guide. /.。

双调排序算法

双调排序算法双调排序算法是一种常用的并行排序算法，它在并行计算机和多核处理器系统中得到广泛应用。

它的特点是简单易懂，且具有良好的并行性能。

在介绍双调排序算法之前，我们先来了解一下排序算法的基本概念。

排序算法是将一组无序的数据按照一定的规则进行排列，使其按照特定的顺序呈现出来。

常见的排序算法有冒泡排序、插入排序、选择排序、快速排序等。

而双调排序算法则是一种基于比较和交换的排序算法。

它的思想是先将待排序的序列分解成若干个子序列，然后分别对每个子序列进行排序，最后再将这些子序列合并成一个有序的序列。

具体地说，双调排序算法的过程如下：1.首先，将待排序的序列分成两部分，分别进行升序排序。

2.然后，我们对每一对子序列进行比较和交换操作，使得整个序列变成单调不减的。

3.接下来，我们将整个序列分成两部分，分别进行降序排序。

4.最后，我们再对每一对子序列进行比较和交换操作，使得整个序列变成单调不增的。

双调排序算法的核心思想就是通过不断比较和交换操作，将序列逐渐变得有序。

它的优势在于其并行性能较好，可以有效地利用并行计算机和多核处理器系统的优势，提高排序的效率。

然而，双调排序算法也存在一些限制。

首先，它要求待排序的序列的长度必须是2的幂次方，否则需要进行填充。

其次，双调排序算法的实现较为复杂，需要额外的存储空间和计算资源。

总的来说，双调排序算法是一种常用的并行排序算法，它通过比较和交换操作将序列逐渐变得有序。

尽管它具有一定的限制，但在并行计算机和多核处理器系统中仍然具有广泛应用前景。

通过合理地利用并行性能，双调排序算法可以提高排序的效率，满足日益增长的数据处理需求。

CLSIFT基于OpenCL的SIFT算法实

CLSIFT：实验结果
以lena.jpg为测试用例，将CLSIFT与OpenCV和 SIFTGPU进行了比较，如图所示。
Institute of Software,Chinese Academy of Sciences
使用一组内容相同但大小不一的图像作为测试用例，测试CLSIFT，SIFTGPU以及OpenCV的性能变化趋势
Time
经实际测试，在维护队列但直接计算而不避免条件分歧时，运行速度和没有队列时基本一致，维护队列开销小
Institute of Software,Chinese Academy of Sciences
CLSIFT:负载平衡
关键点在图像中不同部分有多有少图像中同一区域中的关键点，在尺度空间的不同层次应该服从均匀分布 Workgroup当完成尺度空间中一组层次的关键点定位后，移动另一个区域继续搜索。使得不同work group负载趋于平衡
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OpenCV(CPU) Our CLSIFT(HD7770)
SIFTGPU(HD7770)
Institute of Software,Chinese Academy of Sciences
CLSIFT:总结
数据重用与计算中间结果重用
合理的并行粒度
避免条件分歧与负载平衡
基于opencl的sift算法实现及优化王伟俨中国科学院软件研究所instituteofsoftwarechineseacademyofsciencessift应用背景?scaleinvariancefeaturetransformsift是一种图像特征变换?尺度不变性?旋度不变性?光照不变性?部分视角不变性?十分适合用于图像匹配进而在物体识别机器人定位与导航图像拼接三维建模手势识别视频跟踪笔记鉴定指纹与人脸识别等很多场景有用武之地由于时间复杂度难以在实时环境下应用

基于OpenCL的尺度不变特征变换算法的并行设计与实现

基于OpenCL的尺度不变特征变换算法的并行设计与实现许川佩;王光【摘要】针对尺度不变特征变换(SIFT)算法实时性差的问题,提出了利用开放式计算语言(OpenCL)并行优化的SIFT算法.首先,通过对原算法各步骤进行组合拆分、重构特征点在内存中的数据索引等方式对原算法进行并行化重构,使得算法的中间计算结果能够完全在显存中完成交互;然后,采用复用全局内存对象、共享局部内存、优化内存读取等策略对原算法各步骤进行并行设计,提高数据读取效率,降低传输延时;最后,利用OpenCL语言在图形处理单元(GPU)上实现了SIFT算法的细粒度并行加速,并在中央处理器(CPU)上完成了移植.与原SIFT算法配准效果相近时,并行化的算法在GPU和CPU平台上特征提取速度分别提升了10.51～19.33和2.34～4.74倍.实验结果表明,利用OpenCL并行加速的SIFT算法能够有效提高图像配准的实时性,并能克服统一计算设备架构(CUDA)因移植困难而不能充分利用异构系统中多种计算核心的缺点.【期刊名称】《计算机应用》【年(卷),期】2016(036)007【总页数】6页(P1801-1806)【关键词】尺度不变特征变换算法;开放式计算语言;复用内存对象;细粒度并行;异构系统【作者】许川佩;王光【作者单位】桂林电子科技大学电子工程与自动化学院,广西桂林541004;广西自动检测技术与仪器重点实验室(桂林电子科技大学),广西桂林541004;桂林电子科技大学电子工程与自动化学院,广西桂林541004;广西自动检测技术与仪器重点实验室(桂林电子科技大学),广西桂林541004【正文语种】中文【中图分类】TP391.4以尺度不变特征变换(Scale Invariant Feature Transform， SIFT)算法[1]为代表的基于特征的图像匹配方法近几年发展迅速，该算法对光照、角度或尺度变化的图像都有较好的匹配精度和适应性，但实时性差。

基于OpenCL的Salsa20算法实现与优化

０引言当前环境中，随着通信技术的快速发展，网络带来快捷性的同时，也伴随着出现了诸多信息安全隐患，使得人们对隐私和信息保护越来越重视。

在传输层采用基于流加密算法的Salsa20可以有效阻止信息泄露，防止通信关系被暴露，成为保障用户信息隐秘传输的有力手段。

在物联网的世界，日常设备之间信息相互交换十分频繁，使其非常容易受到安全攻击。

而基于Salsa20的流密码算法采用轮函数迭代的设计结构，利用AES中的行移位和列混合思想，具有较少的参数、较小的内存占用空间和较少的执行周期，在资源受限的设备上性能良好，可以设计用于物联网中资源受限的设备，能够实施其当前无法提供的安全服务，以保护用户的数据隐私并保护系统免受攻击。

Salsa20算法作为入选eSTREAM计划的序列密码加密算法，对其进行研究具有重大意义。

Salsa20旨在提供非常快速的软件加密性能，即使与AES相比也不会影响安全性。

OpenCL全称Open Computing Language，是一个面向异构平台并行编程框架标准，被广泛应用于密码分析系统。

OpenCL的优势是使程序员能够控制所有功能系统中的各个可用处理单元。

OpenCL用于编写跨异构平台执行的程序的框架。

其中，该异构平台由中央处理单元、图形处理单元、数字信号处理器、现场可编程门阵列和其他组件组成处理器或硬件加速器。

通过将流密码算法Salsa20在OpenCL中实现，可以获得加速，并且可以有效提高算法执行速度。

本篇文章结构如下：第1节介绍了Salsa算法和基于OpenCL的高性能计算平台；第2节分析密码算法原理；第3节测试和评估方法；第4节展望未来的工作。

１S a ls a20算法和Ope n CL介绍1.1Salsa20算法简介流密码因为安全性高和加密速度快等优点，非常适合在网络加密数据传输和受限资源场合应用。

Salsa20密码长度可灵活变化，且具有运算速度快等优点，被Shadowsocks推荐使用。

基于OpenCL的图像模糊化算法优化方法探讨

2016年第1期信息与电脑China Computer&Communication算法语言１　图像模糊算法优化的现状Blur算法是指将图像中的某一个像素点用四周均值来替代的算法。

当前，国外国内的学者针对Blur函数优化GDP 已经有了很多研究，在展开数据类型处理时都是Float类型，为了便于编码，废弃了处理图像边沿像素点的方法。

在平常生活中人们大多时候看到的都是彩色图像，所以像素点呈现出多样化并不是单独的Float类型。

在AMDCPU中一个字节的char与Float（四字节）相比，前者要比后者在优化和处理起来更加困难。

因此在下文中优化的图像像素类型为8UC4和8UC1，其中8U指的是没有符号的八位整数，CI表示一个通道，C4表示有四个通道，例如通道数为一时指黑白色图像，而有四个通道是彩色的通道数。

以下算法优化中，对图像边界像素进行计算时，像素点如果不在该范围之内，就用0来表示。

在采用模糊算法时，可以更加快速、灵活的得出Blur函数，因此研究图像模糊算法优化十分必要。

２　算法优化OpenCL标准把内核程序中用到的内存分为局部、私有、常量、全局内存和图像对象这五种类型。

这些内存类型通常在AMD GPU中相关的硬件资源中分别代表LDS，通用寄存器、常量内存、全局内存、L1/L2 Cache。

在OpenCL 内核程序优化的重要部分是应以自身算法的特性为依据，将GPU存储层的潜能充分发掘出来。

此外，OpenCL规范中利用NDRange将N维索引空间定义出来，同时选定工作组空间的所有维度上工作节点数量，在选定整个过程中采用恰当的算法展开计算非常重要。

文章中在优化Blur函数并展开实现时采用两种方法：储存对象时采用图像对象，线程组内的很多各线程不采用LDS方法来进行数据共享；储存对象时利用图像对象并且运用LDS进行数据共享。

2.1 图像对象Opencl标准为了能够处理图像创设了图像对象类型，在图像对象中把过滤模式和越界两种处理方式纳入其中，并且有如write/read_imageui此类，专门对图像进行读写接口函数，这对编程人员而言在编程图像对象编码时其代码更为简洁。

双向选择排序算法的优化研究

双向选择排序算法的优化研究双向选择排序算法是一种经典的排序算法，也叫做“鸡尾酒排序算法”或者“快乐小时排序算法”。

它通过反复进行左右两边的排序，从而实现了对整个数组的排序。

但是，这个算法在效率上还有很多改进的空间。

一、双向选择排序算法的基本流程双向选择排序算法的基本流程是先从左往右遍历数组，找到最大的数，放到右边，然后从右往左遍历数组，找到最小的数，放到左边。

然后继续从左往右遍历，找到第二大的数，放到右边，再从右往左遍历，找到第二小的数，放到左边。

不断循环这个流程，直到整个数组有序。

二、双向选择排序算法的缺点虽然双向选择排序算法简单易懂，但是它的效率比较低。

这主要是由于它的两个循环都是从数组的两端开始的，每一趟排序只能将一个数放到正确的位置上。

如果数组中有大量逆序对，那么每一趟排序对这些逆序对的交换仍然只能移动一个数，因此排序的效率就会很低。

三、双向选择排序算法的优化措施为了提高双向选择排序算法的效率，有以下几种优化措施：1.增加一个起始位置和结束位置。

每一趟排序开始时，记录下当前起始位置和结束位置。

在排序过程中，如果一段区间已经排序完成，就缩小这个区间，不再对已经排好序的数进行无谓的比较和交换。

2.每一趟排序统计有多少逆序对，如果逆序对个数较多，就采用快排等更高效的算法进行排序。

这样可以大大减少比较和移动的次数。

3.引入“鸡尾酒搅拌”（Shaker Sort）策略。

即每一趟排序可以交替从左侧或右侧开始，这样可以缓解逆序对的影响，减小排序的时间复杂度。

四、结论双向选择排序算法虽然简单易懂，但是它的效率比较低，如果排序的数组中有大量逆序对，那么效率就更低。

为了提高双向选择排序算法的效率，可以采用增加起始位置和结束位置、快排统计逆序对、鸡尾酒搅拌等优化措施。

这些措施可以有效地降低排序的时间复杂度，从而提高排序的效率。

基于OpenCL的尺度不变特征变换算法的并行设计与论文

基于OpenCL的尺度不变特征变换算法的并行设计与论文第1篇：基于OpenCL的尺度不变特征变换算法的并行设计与论文针对尺度不变特征变换(sift)算法实时*差的问题，提出了利用开放式计算语言(opencl)并行优化的sift算法。

首先，通过对原算法各步骤进行组合拆分、重构特征点在内存中的数据索引等方式对原算法进行并行化重构，使得计算机网络算法的中间计算结果能够完全在显存中完成交互;然后，采用复用全局内存对象、共享局部内存、优化内存读取等策略对原算法各步骤进行并行设计，提高数据读取效率，降低传输延时;最后，利用opencl语言在图形处理单元(gpu)上实现了sift算法的细粒度并行加速，并在*处理器(cpu)上完成了移植。

与原sift算法配准效果相近时，并行化的算法在gpu和cpu平台上特征提取速度分别提升了10.51～19.33和2.34～4.74倍。

实验结果表明，利用o 未完，继续阅读 >第2篇：基于遗传算法的优化设计论文1数学模型的建立影响抄板落料特*的主要因素有:抄板的几何尺寸a和b、圆筒半径Ｒ、圆筒的转速n、抄板安装角β以及折弯抄板间的夹角θ等［4，9］。

在不同的参数a、β、θ下，抄板的安装会出现如图1所示的情况。

图1描述了不同参数组合下抄板的落料特*横截面示意图。

其中，图1(a)与图1(b)、图1(c)、图1(d)的区别在于其安装角为钝角。

当安装角不为钝角且ob与oc的夹角σ不小于od与oc夹角ψ时(即σ≥ψ)，会出现图1(b)所示的安装情况;当σ＜ψ时，又会出现图1(c)与图1(d)所示的情况，而两者区别在于，η+θ是否超过180°，若不超过，则为图1(c)情况，反之则为图1(d)情况。

其中，点a为抄板上物料表面与筒壁的接触点或为物料表面与抄板横向长度b边的交点;点b为抄板的顶点;点c为抄板折弯点;点d为抄板边与筒壁的交点;点e为ob连线与圆筒内壁面的交点;点f为oc连线与圆筒内壁面的交点。

openeuler 指令重排

一、概述随着信息技术的飞速发展，开源软件在全球范围内得到了广泛的应用和推广。

作为开放、自由的操作系统，Linux在服务器、嵌入式设备和移动设备等方面都有着广泛的应用。

openeuler作为Linux领域的一支新生力量，致力于构建一个开放、协作的Linux发行版，为用户提供稳定、高效的操作系统，满足不同行业、不同场景的需求。

在openeuler的发展过程中，指令重排是一个重要的技术，本文将对openeuler指令重排进行深入探讨。

二、openeuler指令重排的概念1. openeuler指令重排的定义openeuler指令重排是指在Linux操作系统中，为了提高程序执行效率和降低内存访问成本，对指令序列进行重新排列的操作。

通过指令重排，可以使程序在执行过程中充分利用CPU的性能，并使处理器的流水线得到更高效的利用，从而提高程序的执行速度。

2. openeuler指令重排的原理openeuler指令重排主要基于现代处理器的特性进行优化。

通过对程序的指令序列进行重新排列，可以使处理器在执行过程中更好地预测分支、提高指令级并行度，并充分利用处理器的多级缓存等特性，从而实现程序的高效执行。

三、openeuler指令重排的优势1. 提高程序执行效率openeuler指令重排可以充分利用现代处理器的特性，将程序指令序列重新排列，使得程序在执行过程中可以更好地利用CPU的性能，从而提高程序的执行效率。

2. 降低内存访问成本通过指令重排，可以使处理器更好地利用多级缓存，减少对内存的访问次数，从而降低内存访问成本，提高程序的性能。

3. 优化处理器的流水线利用指令重排可以使程序指令的执行顺序更符合处理器的流水线结构，提高处理器的流水线利用率，从而加速程序的执行。

4. 改善程序的并发执行能力通过指令重排，可以提高程序的指令级并行度，使得处理器可以更好地并行执行程序指令，从而改善程序的并发执行能力。

四、openeuler指令重排的实践1. openeuler指令重排的实现方式openeuler指令重排可以通过编译器的优化、指令级并行度的调整、代码重组等方式来实现。