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1. chinaXiv:202106.00114 [pdf]

RLEPSO:Reinforcement learning based Ensemble particle swarm optimizer

Subjects: Computer Science >> Computer Software

Evolution is the driving force behind the evolution of biological intelligence. Learning is the driving force behind human civilization. The combination of evolution and learning can form an entire natural world. Now, reinforcement learning has shown significant effects in many places. However, Currently, researchers in the field of optimization algorithms mainly focus on evolution strategies. And there is very little research on learning. Inspired by these ideas, this paper proposes a new particle swarm optimization algorithm Reinforcement learning based Ensemble particle swarm optimizer (RLEPSO) that combines reinforcement learning. The algorithm uses reinforcement learning for pre-training in the design phase to automatically find a more effective combination of parameters for the algorithm to run better and Complete optimization tasks faster. Besides, this algorithm integrates two robust particle swarm variants. And it sets the weight parameters for different algorithms to better adapt to the solution requirements of a variety of different optimization problems, which significantly improves the robustness of the algorithm. RLEPSO makes a certain number of sub-swarms to increase the probability of finding the global optimum and increasing the diversity of particle swarms. This proposed RLEPSO is evaluated on an optimization test functions benchmark set (CEC2013) with 28 functions and compared with other eight particle swarm optimization variants, including three state-of-the-art optimization algorithms. The results show that RLEPSO has better performance and outperforms all compared algorithms.

submitted time 2021-06-29 Hits3931Downloads305 Comment 0

2. chinaXiv:202106.00111 [pdf]

Zero-knowledge Based Proof-chain: A methodology for blockchain-partial system

Yuqi Bai; Lei Luo; Shixin Liang
Subjects: Computer Science >> Computer Software

Intuitively there is drastic distinction between the “pure” decentralized block-chain systems like Defis and those that only utilizes block-chain as an enhancing technology but remains centralized with real-world business model and conventional technologies like database, application server etc. Our study explores extensively this distinction from a methodological point of view, classifies them into blockchain-complete and blockchain-partial, analyzes key features of the two types, and reveal the root cause of this distinction. We analyze the function or, in more strong words, the “ultimate purpose” of blockchain in the blockchain-partial systems, and present a conceptual model we named proof-chain that quite satisfactorily represented the general paradigm of blockchain in blockchain-partial systems. A universal tension between strength of proof-chain and privacy is then revealed and the zero-knowledge based proof-chain takes shape. Several case studies demonstrate the explaining power of our proof-chain methodology. We then apply proof-chain methodology to the analysis of the ecosystem of a collaborating group of blockchain-partial systems, representing the paradigm of public and private data domain whose border the proof-chain crosses. Finally, some derived guidelines from this methodology speaks usefulness of our methodology.

submitted time 2021-06-29 Hits4381Downloads398 Comment 0

3. chinaXiv:202103.00068 [pdf]


Subjects: Computer Science >> Computer Software

This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient.

submitted time 2021-03-01 Hits5359Downloads556 Comment 0

4. chinaXiv:201703.00905 [pdf]

Applying Ricci flow to Manifold Learning

Li, Yangyang; Lu, Ruqian
Subjects: Computer Science >> Computer Software

Traditional manifold learning algorithms often bear an assumption that the local neighborhood of any point on embedded manifold is roughly equal to the tangent space at that point without considering the curvature. The curvature indifferent way of manifold processing often makes traditional dimension reduction poorly neighborhood preserving. To overcome this drawback we propose a new algorithm called RF-ML to perform an operation on the manifold with help of Ricci flow before reducing the dimension of manifold.

submitted time 2017-04-10 Hits2960Downloads1590 Comment 0

5. chinaXiv:201703.00180 [pdf]


贺鹏; 姜海洋; 谢高岗
Subjects: Computer Science >> Computer Software

网络入侵检测与防御系统在当前的IP 网络安全领域中扮演着重要的角色,互联网流量的激增和单核处理器在数据包处理上存在的瓶颈,使得传统的运行于单核上的单线程网络入侵检测与防御系统已经远远不能满足网络发展的需求。为了解决这个问题,本文以主流单线程网络入侵检测与防御系统软件Snort 为基础,设计了一个基于软件流水的并行入侵检测系统pSnort,将传统的Snort 划分为2 个阶段,通过将其中最耗时的处理阶段并行化,以达到提升性能的目的。同时,通过程序设计,pSnort 避免了由于并行化而带来的严重的同步/互斥问题。经过试验,pSnort在Intel Quad-core Xeon 通用平台上可以获得超过1Gbps 的包处理速度。相对于传统的Snort,pSnort 最高能获得147%的性能提升以及2.5 倍加速比。

submitted time 2017-03-09 Hits2655Downloads1894 Comment 0

6. chinaXiv:201606.00053 [pdf]


王睿; 姚二林; 陈明宇; 谭光明
Subjects: Computer Science >> Computer Software

随着高性能计算系统规模的不断扩大,节点失效愈加频发。传统的容错技术大都基于检查点(checkpoint)方式。但是,检查点技术的开销随着系统规模的扩大而不断增加,在百亿亿次(Exaflops)规模下其容错效率难以满足系统需求。算法失效恢复技术相比检查点方式具有更高的效率。然而,该技术依然基于停等模式。对于大规模系统,停等模式在很大程度上会影响程序的并行效率。本文提出了一种非停等的算法级容错策略——热替换策略。在程序运行过程中若发生节点失效,不用停等恢复失效节点上的数据,而用冗余节点替换失效节点,使计算能继续进行。最终的正确结果可以通过一个线性变换求出。为了论证方案的有效性,我们结合MPICH 的容错特性实现了容错的High Performance Linpack (HPL),并评估了方案的性能。实验结果表明,即使在小规模下,我们的方案的性能也明显优于算法失效恢复技术。

submitted time 2016-06-08 Hits2761Downloads1934 Comment 0

7. chinaXiv:201606.00052 [pdf]


付斌章; 韩银和; 李华伟; 李晓维
Subjects: Computer Science >> Computer Software

在众核处理器系统中,片上网络常被用来提供高带宽、低延迟、高可靠的片上网络通信。为了减少网络拥塞、提高网络性能,流量平衡路由算法获得研究人员的广泛关注。流量平衡算法通常利用完全自适应路由算法来提供路径分集,而当前的完全自适应路由算法或者需要较多的虚通道或者假设一个保守的流控策略。一方面虚通道是比较昂贵的资源,另一方面保守的流控策略则有可能造成网络性能的下降。因此研究人员提出利用应用程序的流量信息来提升路由性能。这些算法在不使用虚通道的基础上可以针对不同的流量特性进行重构,从而实现路由自适应度的按需分配。按照使用的流量信息类型,流量感知的可重构路由算法可以分为离线和在线算法。离线算法需要事先知道程序的流量特征,因此他们大多针对应用程序定制的多核片上系统。在线算法则是根据在线收集的流量信息进行重构,因此可以用于通用处理器系统。本文将讨论最近国际上提出的两种著名的离线算法,并重点介绍本文作者在2011 年国际计算机体系结构大会(ISCA 11)上发表的基于算盘转向模型的在线可重构路由算法。

submitted time 2016-06-08 Hits2604Downloads1697 Comment 1

8. chinaXiv:201606.00051 [pdf]

CPU/ATI GPU 混合体系结构上DGEMM 的性能研究

李佳佳; 李兴建; 谭光明
Subjects: Computer Science >> Computer Software

本文报道了我们在CPU/ATI GPU 混合体系结构上优化双精度矩阵乘法(DGEMM)的工作。在真实应用中, CPU 与图形处理器(GPU)之间的数据传输是影响性能的关键因素。由于软件流水可以降低数据传输开销,我们提出了三种软件流水算法,分别是双缓存(Double Buffering)、数据重用(Data Reuse)和数据存储优化(Data Placement)。在AMD 公司的图形处理器(GPU)ATI HD5970 上,优化后DGEMM性能达到758 GFLOP/s,对应效率为82%,是ACML-GPU v1.1 性能的两倍。在Intel Westmere EP 和ATIHD5970 组成的异构系统上,性能达到844 GFLOP/s,效率为80%。我们进一步考察了多个CPU 和多个GPU上DGEMM 的扩展性,详细分析了体系结构方面的影响因素。分析表明,PCIe 总线和内存总线的竞争是异构系统上程序性能降低的重要影响因素。

submitted time 2016-06-08 Hits2829Downloads2006 Comment 0

9. chinaXiv:201606.00050 [pdf]


叶笑春; 范东睿; 陈明宇; 吕慧伟
Subjects: Computer Science >> Computer Software

随着芯片内部处理器核数的增多,多核处理器逐渐有向众核方向发展的趋势。而众核这一全新的体系结构给计算机模拟带来了挑战。串行模拟已经难以满足速度的需求,必须充分利用现有并行宿主机的多核资源,在保证不损失模拟精度的前提下提升模拟速度。本文以众核和众核集群两种体系结构为例,说明并行模拟技术在计算机并行体系结构模拟中的必要性和可行性,在众核模拟中,做到精度不变,模拟速度提升10 倍;在众核集群模拟中,所模拟的处理器小核总数达到千核规模,并实现了混合的编程运行环境,为该结构的可扩展性测试提供了基础。

submitted time 2016-06-08 Hits2282Downloads1446 Comment 0

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