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Accelerating the cryo-EM structure determination in RELION on GPU cluster

Frontiers of Computer Science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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A review of auxiliary hardware architectures supporting dynamic taint analysis

A review of auxiliary hardware architectures supporting dyna...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Yang, Anfeng Guang, Yan Zhao, Yuntian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510656673

Dynamic taint analysis is a widely used information flow analysis method. The software structure supporting dynamic taint analysis needs running program to monitor the spread of taint, which will bring a large running time overhead. The architecture based on hardware implementation can shorten the running time overhead of taint analysis through hardware modification, which has great research significance. Firstly, we introduced the research background and significance of hardware. Then, we describe the problems of providing support structure for dynamic taint analysis based on software and how to solve or alleviate these problems in hardware architecture on the basis of the introduction of the specific classification and structure of hardware architecture. Finally, on the basis of comparing the characteristics of hardware structure from different dimensions, the future research direction of hardware structure is prospected. © 2022 SPIE.

关键词： Memory architecture

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PFCA: An Intelligent Method of Multidimensional User Profile Construction and Application 7

PFCA: An Intelligent Method of Multidimensional User Profile...

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7th International Conference on Big Data Analytics, ICBDA 2022

作者： Xia, Yi Zhou, Gang Luo, Junyong Lan, Mingjing Chen, Xiaohui Wang, Shiyu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479387

User profile plays an important role in analyzing high influencers in the era of big data. For example, the profile can help the political strategists to scrutinize the performance of a candidate or party and overcome their weakness long before election day. In this work, we propose a novel profile construction and application model (PFCA) that aims to discover the overall character of high influencers, and apply PFCA to Donald Trump, one of the most influential people recently, to identify Trump's comprehensive personalities. We first collect and preprocess profile data from static and dynamic aspects of multi-source data, and then categorize multi-source data topics and emotions. Next, we attempt to construct the profile from four aspects: fundamental characteristic, behavior characteristic, value characteristic and situation characteristic, to explore the correlation among these characteristics. Finally, PFCA is verified by the outcomes of the 2016 and 2020 U.S. presidential elections, and is evaluated by relevant experts, which proves that PFCA is relatively effective and efficient to profile high influencers. Moreover, PFCA can be further applied to related analysis and prediction in the 2024 U.S. presidential election. © 2022 IEEE.

关键词： Sentiment analysis

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A Malware Classification Method based on Attentive Bidirectional Model 7

A Malware Classification Method based on Attentive Bidirecti...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Gui, Hairen Liu, Fudong Zhang, Chunyan Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665478571

Deep learning techniques have achieved significant improvements in malware analysis application, such as malware detection, malware classification and vulnerability mining. Convolutional neural network (CNN) is applied to extract local features of samples, while recurrent neural network (RNN) mainly extracts global features from sequence context. However, the pooling layer in CNN is a nonlinear dimensionality reduction operation, which can cause a loss of correlation feature. The raw RNN or LSTM model cannot perform parallel computing when calculating the feature vectors, limiting the effect of model application. Hence, we propose a novel malware classification model named BGSA (Bidirectional GRU with Self- Attention), which applies bi-direction gated recurrent unit (BiGRU) and self-attention mechanism. To demonstrate that the model can improve the feature extraction capability, a series of comparison experiments have been conducted on the VxHeavon dataset. The results show that the classification of BGSA reaches 92.54% in term of precision, which surpass the performance of method based on LSTM+CNN and BiGRU. © 2022 IEEE.

关键词： Long short-term memory

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A PV-DM-based feature fusion method for binary malware clustering 4

A PV-DM-based feature fusion method for binary malware clust...

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4th International Conference on Communications, Information System and Computer engineering, CISCE 2022

作者： Gui, Hairen Zhang, Chunyan Huang, Yizhao Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Utilizing NLP methods in malware classification or clustering applications is a hotspot in malware analysis. Binary malware embedding has become an important direction and basis for malware analysis. Existing binary malware vectorizing methods cannot extract semantic and functional information simultaneously, which may affect the performance of family clustering. In this paper, a novel feature fusion representation method is proposed based on PV-DM (Paragraph Vector-Distributed Memory) model, and the K-Means algorithm is adopted for family clustering. The method combines the features of program-level API call sequence and function-level instruction sequences by means of PV-DM. The functional and semantic representation capabilities of binary malwares can be improved. Malware public dataset VXHeavon is taken as the dataset. A series of experiments are conducted, using the existing family labels as the clustering accuracy evaluation standard. The results demonstrate that our feature fusion representation method outperforms the baseline method by 4.96% in terms of accuracy. © 2022 IEEE.

关键词： Malware

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A Review of Few-Shot Network Traffic Classification Based on Deep Learning

A Review of Few-Shot Network Traffic Classification Based on...

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2023 International Conference on Mechatronics and Intelligent Control, ICMIC 2023

作者： Yang, Kai Wang, Ruo Nan Meng, Qi Meng, Long Qi, Zan Li, Xue Fei, Jin Long State Key Laboratory of Mathematical Engineering And Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781510668393

At present, deep learning has achieved excellent performance in the field of network traffic classification. However, deep learning relies on massive data-driven classification models. When the data set is small, it is usually hindered. In order to solve this problem, few-shot network traffic classification technology based on deep learning has been gradually studied. In this paper, we conducted a comprehensive survey to fully understand the few-shot traffic classification techniques. We first define the few-shot traffic classification problem. Based on how to solve the contradiction between the few-shot data set and the large number of parameters to be trained in the model, we classify the current few-shot traffic classification method based on deep learning from two perspectives: (1) Data augmentation method, which uses the method of expanding the data set to enhance the supervision experience. In this paper, the methods based on data enhancement are divided into two categories based on GAN and feature transformation.(2) Model-based methods. Model-based methods are divided into three categories: transfer learning, metric learning and meta-learning. And discuss the advantages and disadvantages of each classification method. Finally, the results are summarized and the future development direction of few-shot network traffic classification technology based on deep learning is prospected. © 2023 SPIE.

关键词： Deep learning

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A Tree-based Structure Mutation Method for Network Protocol Fuzzing 6

A Tree-based Structure Mutation Method for Network Protocol ...

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2021 6th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2021

作者： Yang, Rui Jing, Jing Qi, Xuyan Zheng, Hongbing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783800757282

It is difficult to fuzz the protocol because the format of network protocol is complex and often involves syntax and semantic constraints. Traditional fuzzing often makes a trade-off between the validity of protocol message and mutation space when applied to network protocol. In this paper, we present a tree-based structure mutation method for network protocols. Firstly, the test template is parsed into a tree structure, then the tree nodes are mutated specifically, and finally the test message is reassembled for fuzzing. To demonstrate the effectiveness of our method, we evaluated it on three protocol programs, and the results show compared with the traditional mutation-based and grammar-based method, the tree-based structure mutation method can further expand the mutation space while ensuring the efficiency of the message, and has a higher probability of discovering program paths and triggering vulnerabilities. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Semantics

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Multi-protocol comprehensive topology probe technology under non-cooperative conditions

Multi-protocol comprehensive topology probe technology under...

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2022 International Symposium on Computer Applications and Information Systems, ISCAIS 2022

作者： Lu, Xuanting Yang, Qichao Liu, Shengli Yang, Pengfei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510655010

With the rapid development of the Internet and the increasingly complex and diverse network environment, it is of great significance to quickly and accurately obtain the topology environment of the target network for optimizing network management, locating network resources and ensuring network security. Most of the existing network topology detection methods are implemented based on the SNMP protocol, or use the Traceroute-like method for active detection. However, in a non-cooperative environment, that is, the router disables SNMP permissions or prohibits responding to probe packets to shield Traceroute probes, network probes will face problems such as missing information and blocked probes. Based on the original SNMP protocol detection, this paper adopts RIP, OSPF and other routing protocols to supplement the detection method, which greatly reduces the number of undetectable nodes. In addition, for a small number of router devices that are still undetectable, the Traceroute directional breakthrough method using the routing table as a clue is used to bypass these undetectable nodes, complete the missing information in the network topology, and further optimize and improve the detection results. © 2022 SPIE

关键词： Network topology

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An Abnormal Domain Name Generation Method Based on a Character-Level Model 4

An Abnormal Domain Name Generation Method Based on a Charact...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Wu, Chunbing Fei, Jinlong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450398343

Internet technology develops by leaps and bounds, new types of botnet attacks are springing up. Currently, most malware are stealthy when implementing attacks since they can bypass detection strategies such as blacklists and sinkholes. These malware mainly use DGA to generate many random domain names to prevent C&C server IP addresses from being traced to the source. To resist botnet attacks, machine-learning methods have been proposed for detecting abnormal domain names. However, there are still some inadequacies, e.g., detection methods based on manual feature extraction couldn't identify novel variant DGA domains, and existing neural network detection methods have weak generalization ability and lack training data for new variant DGA domain names. To address the above problems, this paper proposes an abnormal domain name data generation technology based on a character-level model. Firstly, combining specific text generation-related ideas improves the character-level language model. Secondly, the Bi-GRU network model and attention mechanism are used to thoroughly learn the internal correlation features, and simulate the generation of abnormal domain names. The experimental results show that this method's abnormal domain name data is highly similar to the actual domain name in relative entropy and character composition. Furthermore, it verifies the feasibility of the generated data as training data set for a new variant DGA domain name, which to a certain extent, helps to improve the generalization ability of the abnormal domain name detection model. © 2022 ACM.

关键词： Botnet

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SWPy: Python Numerical computing Library Optimization for Domestic Many-core Processors 10

SWPy: Python Numerical Computing Library Optimization for Do...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Wang, Qihan Pang, Jianming Yue, Feng Yang, Shudan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The NumPy computational library, the most typical scientific computing library in Python, is generally limited to a single-node or multi-threaded CPU-only execution model. With the increasing size of datasets, the increasing complexity of programs, and their growing computational overhead, these resources are far beyond what a single CPU node can provide, and there is a growing need to solve these problems by utilizing more massive computational resources. In this paper, we port the NumPy numerical computation library for domestic heterogeneous multicore processors and make full use of the huge computational resources of sunway multicore processors to optimize the parallel acceleration of common key functions in the NumPy library, forming the heterogeneous multicore Python computation library - SWPy. © 2022 IEEE.

关键词： Python

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