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IEEE international conference on Image processing (ICIP)

作者： Tang, Mingkai Li, Zhuozhang Yang, Zhuo Zhan, Yinwei Su, Jia Yu, Wenxin Guangdong Univ Technol Sch Comp Guangzhou Guangdong Peoples R China Capital Normal Univ Coll Informat Engn Beijing Peoples R China Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728163956

Polar Fourier analysis can extract orthogonal and rotation invariant features and demonstrate superior performances in many image processing tasks. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics processing Unit (GPU), this research presents GPU accelerated polar Fourier analysis. Proposed parallel algorithm is based on mathematical properties of polar Fourier analysis and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result proposed method is over 170 times faster. Wide range of applications that using polar Fourier analysis will inspired from this work.

关键词： polar Fourier analysis Graphics processing Unit GPU

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features 10th

Research on Integrated Detection of SQL Injection Behavior B...

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： Li, Ming Liu, Bo Xing, Guangsheng Wang, Xiaodong Wang, Zhihui College of Intelligence Science and Technology National University of Defence Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9789811584619

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile, the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Towards efficient and secure analysis of large datasets 44

Towards efficient and secure analysis of large datasets

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44th Annual IEEE-Computer-Society international conference on Computers, Software, and applications (COMPSAC)

作者： Cimato, Stelvio Nicolo, Stefano Univ Milan Dipartimento Informat Milan Italy

ISBN: (纸本)9781728173030

One of the promises of the "big data" revolution is that trough the analysis of large datasets people will benefit from the solution to many different problems obtained by the deployment of advanced machine learning models. One of the challenges of this standard approach, is that information needs to be centralized on the data center or the machine where the training phase is performed, posing many concerns about privacy. In this paper we take a step towards secure and efficient processing of distributed large datasets, where original data reside at different locations and are processed in a privacy preserving way. In particular we rely on the available technologies to achieve the secure design of a machine learning model by performing the training phase on encrypted data. The case study we examine is focused on the forecasting of energy production by wind farms situated in different locations. We show in detail how the machine learning model is created on the basis of the available datasets, we compare the results with the ones produced by the previous models, and discuss also their performances.

关键词： machine learning privacy preserving techniques secure multi-party computation

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Dynamic Loop Scheduling Using MPI Passive-Target Remote Memory Access 27

Dynamic Loop Scheduling Using MPI Passive-Target Remote Memo...

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27th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Eleliemy, Ahmed Ciorba, Florina M. Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (纸本)9781728116440

Scientific applications often contain large computationally-intensive parallel loops. Loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Moreover, it considers the novel features in the latest MPI standards, such as passive-target remote memory access, shared-memory window creation, and atomic read-modify-write operations. To evaluate the proposed approach, five well-known DLS techniques, two applications, and two heterogeneous hardware setups have been considered. The DLS techniques implemented using the proposed approach outperformed their counterparts implemented using the traditional master-worker execution model.

关键词： Dynamic loop scheduling distributed-memory systems Master-worker execution model MPI Passive-target remote memory access

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Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

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21st international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In Software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous Software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous Software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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Vector Runahead 21

Vector Runahead

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ACM/IEEE 48th Annual international Symposium on Computer Architecture (ISCA)

作者： Naithani, Ajeya Ainsworth, Sam Jones, Timothy M. Eeckhout, Lieven Univ Ghent Ghent Belgium Univ Edinburgh Edinburgh Midlothian Scotland Univ Cambridge Cambridge England

ISBN: (纸本)9781665433334

The memory wall places a significant limit on performance for many modern workloads. These applications feature complex chains of dependent, indirect memory accesses, which cannot be picked up by even the most advanced microarchitectural prefetchers. The result is that current out-of-order superscalar processors spend the majority of their time stalled. While it is possible to build special-purpose architectures to exploit the fundamental memory-level parallelism, a microarchitectural technique to automatically improve their performance in conventional processors has remained elusive. Runahead execution is a tempting proposition for hiding latency in program execution. However, to achieve high memory-level parallelism, a standard runahead execution skips ahead of cache misses. In modern workloads, this means it only prefetches the first cache-missing load in each dependent chain. We argue that this is not a fundamental limitation. If runahead were instead to stall on cache misses to generate dependent chain loads, then it could regain performance if it could stall on many at once. With this insight, we present Vector Runahead, a technique that prefetches entire load chains and speculatively reorders scalar operations from multiple loop iterations into vector format to bring in many independent loads at once. Vectorization of the runahead instruction stream increases the effective fetch/decode bandwidth with reduced resource requirements, to achieve high degrees of memory-level parallelism at a much faster rate. Across a variety of memory-latency-bound indirect workloads, Vector Runahead achieves a 1.79 x performance speedup on a large out-of-order superscalar system, significantly improving on state-of-the-art techniques.

关键词： Out of order Program processors Prefetching Computer architecture Bandwidth parallel processing Standards

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Impact of CUDA and OpenCL on parallel and distributed Computing

Impact of CUDA and OpenCL on Parallel and Distributed Comput...

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international Conf on Electrical and Electronic Engineering (ICEEE)

作者： Abu Asaduzzaman Alec Trent S. Osborne C. Aldershof Fadi N. Sibai Wichita State University Wichita Kansas USA Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

ISBN: (纸本)9781665440714;9781665411615

Along with high performance computer systems, the Application Programming Interface (API) used is crucial to develop efficient solutions for modern parallel and distributed computing. Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) are two popular APIs that allow General Purpose Graphics processing Unit (GPGPU, GPU for short) to accelerate processing in applications where they are supported. This paper presents a comparative study of OpenCL and CUDA and their impact on parallel and distributed computing. Mandelbrot set (represents complex numbers) generation, Marching Squares algorithm (represents embarrassingly parallelism), and Bitonic Sorting algorithm (represents distributed computing) are implemented using OpenCL (version 2.x) and CUDA (version 9.x) and run on a Linux-based High Performance Computing (HPC) system. The HPC system uses an Intel i7-9700k processor and an Nvidia GTX 1070 GPU card. Experimental results from 25 different tests using the Mandelbrot Set generation, the Marching Squares algorithm, and the Bitonic Sorting algorithm are analyzed. According to the experimental results, CUDA performs better than OpenCL (up to 7.34x speedup). However, in most cases, OpenCL performs at an acceptable rate (CUDA speedup is less than 2x).

关键词： Performance evaluation High performance computing Graphics processing units Computer architecture parallel processing Hardware Generators

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Subarray Skyline Query processing in Array Databases 2021

Subarray Skyline Query Processing in Array Databases

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33rd international conference on Scientific and Statistical Database Management (SSDBM)

作者： Choi, Dalsu Yoon, Hyunsik Chung, Yon Dohn Korea Univ Seoul South Korea

ISBN: (纸本)9781450384131

With the generation of large-scale spatial data in various fields, array databases that represent space as an array have become one of the means of managing spatial data. Each cell in an array tends to interact with one another;therefore, instead of considering a single cell, considering a concept of subarray is required in some applications. In addition, each cell has several attribute values to indicate its features. Based on the two observations, we propose a new type of query, subarray skyline, that provides a way to find meaningful subarrays or filter less meaningful subarrays considering attributes. We also introduce an efficient query processing method, ReSKY, in centralized and distributed settings. Through extensive experiments using an array database and real datasets, we show that ReSKY has better performance than the existing techniques.

关键词： subarray skyline array databases distributed processing

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distributed distributionally robust optimization with non-convex objectives 22

Distributed distributionally robust optimization with non-co...

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Proceedings of the 36th international conference on Neural Information processing Systems

作者： Yang Jiao Kai Yang Dongjin Song Tongji University University of Connecticut

ISBN: (纸本)9781713871088

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the distributed distributionally robust optimization (DDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

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Hybrid multi-projection method using sparse approximate inverses on GPU clusters

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international JOURNAL OF HIGH PERFORMANCE COMPUTING applications 2020年第3期34卷 282-305页

作者： Moutafis, Byron E. Gravvanis, George A. Filelis-Papadopoulos, Christos K. Democritus Univ Thrace Dept Elect & Comp Engn Sch Engn Univ Campus GR-67100 Xanthi Greece

The state-of-the-art supercomputing infrastructures are equipped with accelerators, such as graphics processing units (GPUs), that operate as coprocessors for each workstation of the distributed memory system. The multi-projection type methods are a class of algebraic domain decomposition methods based on semi-aggregation techniques. The multi-projection type methods have improved convergence behavior, as the number of subdomains increases, due to the corresponding augmentation of the semi-aggregated local linear systems with more coarse components, while the number of fine components is reduced. Moreover, limited amount of communications among the workstations is required by the proposed method. The utilization of the available GPUs allows an increase in the number of subdomains along with finer-grained parallelism, leading to improved performance. A load-balancing algorithm that ensures the concurrency of the computations on multicore processors and GPUs is proposed. Flexible parallel preconditioned Krylov subspace iterative methods enhanced with multi-projection type methods have been designed appropriately in order to have improved performance, compared to CPU-only or GPU-only executions, by exploiting the available CPUs and GPUs of the distributed memory system concurrently. The unsymmetric local linear systems are solved by the preconditioned Bi-Conjugate Gradient STABilized (BiCGSTAB) method enhanced with the modified generic factored approximate sparse inverse preconditioner, whereas the preconditioned conjugate gradient (CG) method along with the symmetric factored approximate sparse inverse preconditioner is used for the symmetric positive definite local coefficient matrices. Numerical results regarding the convergence behavior, the performance, and the scalability of the proposed method for several problems are given.

关键词： Semi-aggregation algebraic domain decomposition high performance computing hybrid solver graphics processing units load balancing

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