- 作 者: ( "jingling xue" OR "jing-ling xue" )
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IFDS-based Context Debloating for Object-Sensitive Pointer AnalysisACM Transactions on Software Engineering and Methodology (IF 3.685) Pub Date : 2023-05-27 ,DOI:https://dl.acm.org/doi/10.1145/3579641Dongjie He, Jingbo Lu, Jingling XueObject-sensitive pointer analysis, which separates the calling contexts of a method by its receiver objects, is known to achieve highly useful precision for object-oriented languages such as Java. Despite recent advances, all object-sensitive pointer analysis algorithms still suffer from the scalability problem due to the combinatorial explosion of contexts in large programs. In this article, we introduce ...
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Hybrid Inlining: A Compositional and Context Sensitive Static Analysis FrameworkarXiv - CS - Logic in Computer Science Pub Date : 2022-10-26 ,DOI:arxiv-2210.14436Jiangchao Liu, Jierui Liu, Peng Di, Diyu Wu, Hengjie Zheng, Alex Liu, Jingling XueContext sensitivity is essential for achieving the precision in inter-procedural static analysis. To be (fully) context sensitive, top-down analysis needs to fully inline all statements of the callees at each callsite, leading to statement explosion. Compositional analysis, which inlines summaries of the callees, scales up but often loses precision, as it is not strictly context sensitive. We propose ...
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ReaDy: A ReRAM-Based Processing-in-Memory Accelerator for Dynamic Graph Convolutional NetworksIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IF 2.565) Pub Date : 2022-10-20 ,DOI:10.1109/tcad.2022.3199152Yu Huang, Long Zheng, Pengcheng Yao, Qinggang Wang, Haifeng Liu, Xiaofei Liao, Hai Jin, Jingling XueDynamic graph convolutional networks (DGCNs) have emerged as an effective approach to analyzing graph data that is constantly changing. The typical DGCNs incorporate not only graph convolutional networks (GCNs) to extract the structural information but also with recurrent neural networks (RNNs) to capture the temporal information from evolving graph data. These two alternative execution kernels of ...
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A Flexible Yet Efficient DNN Pruning Approach for Crossbar-Based Processing-in-Memory ArchitecturesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IF 2.565) Pub Date : 2022-10-20 ,DOI:10.1109/tcad.2022.3197510Long Zheng, Haifeng Liu, Yu Huang, Dan Chen, Chaoqiang Liu, Haiheng He, Xiaofei Liao, Hai Jin, Jingling XuePruning deep neural networks (DNNs) can reduce the model size and thus save hardware resources of a resistive-random-access-memory (ReRAM)-based DNN accelerator. For the tightly coupled crossbar structure, existing ReRAM-based pruning techniques prune the weights of a DNN in a structured manner, thereby attaining low pruning ratios. This article presents a novel pruning technique, SegPrune, for pruning ...
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The driving factors and buffering mechanism regulating cropland soil acidification across the Sichuan Basin of ChinaCatena (IF 6.367) Pub Date : 2022-10-19 ,DOI:10.1016/j.catena.2022.106688Aiwen Li, Chengji Li, Yuanyuan Zhang, Qian Deng, Hongyan Fang, Bin Zhao, Min Ran, Liangying Song, Jingling Xue, Qi Tao, Rong Huang, Yiding Li, Wei Zhou, Jingting Wang, John P. Wilson, Qiquan Li
Cropland soil has a higher acidification risk, with the driving factors and the soil buffering capacity controlling the degree of soil acidification. However, the relative importance of the main driving factors at broad geographic scales remains unclear, and the evidence on the acid-buffering process is rarely examined by large-scale observations. Using 4101 soil samples obtained in 1980s and 4348
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Practical Software-Based Shadow Stacks on x86-64ACM Transactions on Architecture and Code Optimization (IF 1.444) Pub Date : 2022-10-07 ,DOI:https://dl.acm.org/doi/10.1145/3556977Changwei Zou, Yaoqing Gao, Jingling XueControl-Flow Integrity (CFI) techniques focus often on protecting forward edges and assume that backward edges are protected by shadow stacks. However, software-based shadow stacks that can provide performance, security, and compatibility are still hard to obtain, leaving an important security gap on x86-64. In this article, we introduce a simple, efficient, and effective parallel shadow stack design ...
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A Source-Level Instrumentation Framework for the Dynamic Analysis of Memory SafetyIEEE Transactions on Software Engineering (IF 9.322) Pub Date : 2022-09-29 ,DOI:10.1109/tse.2022.3210580Zhe Chen, Qi Zhang, Jun Wu, Junqi Yan, Jingling XueLow-level control makes C unsafe, resulting in memory errors that can lead to data corruption, security vulnerabilities or program crashes. Dynamic analysis tools, which have been widely used for detecting memory errors at runtime, usually perform instrumentation at the IR or binary level. However, these non-source-level instrumentation frameworks and tools suffer from two inherent drawbacks: optimization ...
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Selecting Context-Sensitivity Modularly for Accelerating Object-Sensitive Pointer AnalysisIEEE Transactions on Software Engineering (IF 9.322) Pub Date : 2022-03-24 ,DOI:10.1109/tse.2022.3162236Dongjie He, Jingbo Lu, Yaoqing Gao, Jingling XueObject-sensitive pointer analysis (denoted k obj under $k$ -limiting) for an object-oriented program can be accelerated if context-sensitivity can be selectively applied to only some precision-critical variables/objects in a program. Existing pre-analyses for making such selections, which are performed as whole-program analyses to a program, are developed based on two broad approaches. One approach ...
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Buddy Stacks: Protecting Return Addresses with Efficient Thread-Local Storage and Runtime Re-RandomizationACM Transactions on Software Engineering and Methodology (IF 3.685) Pub Date : 2022-03-04 ,DOI:10.1145/3494516Changwei Zou, Xudong Wang, Yaoqing Gao, Jingling XueShadow stacks play an important role in protecting return addresses to mitigate ROP attacks. Parallel shadow stacks, which shadow the call stack of each thread at the same constant offset for all threads, are known not to support multi-threading well. On the other hand, compact shadow stacks must maintain a separate shadow stack pointer in thread-local storage (TLS), which can be implemented in terms ...
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Optimizing deep neural networks on intelligent edge accelerators via flexible-rate filter pruningJournal of Systems Architecture (IF 5.836) Pub Date : 2022-02-12 ,DOI:10.1016/j.sysarc.2022.102431Guangli Li, Xiu Ma, Xueying Wang, Hengshan Yue, Jiansong Li, Lei Liu, Xiaobing Feng, Jingling XueWhile deep learning has shown superior performance in various intelligent tasks, it is still a challenging problem to deploy sophisticated models on resource-limited edge devices. Filter pruning performs a system-independent optimization, which shrinks a neural network model into a thinner one, providing an attractive solution for efficient on-device inference. Prevailing approaches usually utilize ...
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EagleACM Transactions on Software Engineering and Methodology (IF 3.685) Pub Date : 2021-07-23 ,DOI:10.1145/3450492Jingbo Lu, Dongjie He, Jingling XueObject sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k -object-sensitive pointer analysis (under k -limiting), denoted k -obj , is often inefficient even when it is scalable for small values of k ...
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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IF 2.565) Pub Date : 2020-11-01 ,DOI:10.1109/tcad.2020.3013050Guangli Li, Xiu Ma, Xueying Wang, Lei Liu, Jingling Xue, Xiaobing FengThe increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this article, we present a novel ...
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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesarXiv - CS - Performance Pub Date : 2020-10-30 ,DOI:arxiv-2010.16165Guangli Li, Xiu Ma, Xueying Wang, Lei Liu, Jingling Xue and Xiaobing FengThe increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel ...
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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesarXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-10-30 ,DOI:arxiv-2010.16165Guangli Li, Xiu Ma, Xueying Wang, Lei Liu, Jingling Xue and Xiaobing FengThe increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel ...
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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesarXiv - CS - Machine Learning Pub Date : 2020-10-30 ,DOI:arxiv-2010.16165Guangli Li, Xiu Ma, Xueying Wang, Lei Liu, Jingling Xue and Xiaobing FengThe increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel ...
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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesarXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2020-10-30 ,DOI:arxiv-2010.16165Guangli Li, Xiu Ma, Xueying Wang, Lei Liu, Jingling Xue and Xiaobing FengThe increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel ...
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Value-Flow-Based Demand-Driven Pointer Analysis for C and C++IEEE Transactions on Software Engineering (IF 9.322) Pub Date : 2020-08-01 ,DOI:10.1109/tse.2018.2869336Yulei Sui, Jingling XueWe present Supa, a value-flow-based demand-driven flow- and context-sensitive pointer analysis with strong updates for C and C++ programs. Supa enables computing points-to information via value-flow refinement, in environments with small time and memory budgets. We formulate Supa by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains ...
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CloudRaid: Detecting Distributed Concurrency Bugs via Log Mining and EnhancementIEEE Transactions on Software Engineering (IF 9.322) Pub Date : 2020-06-02 ,DOI:10.1109/tse.2020.2999364Jie Lu, Feng Li, Chen Liu, Lian Li, Xiaobing Feng, Jingling XueCloud systems suffer from distributed concurrency bugs, which often lead to data loss and service outage. This paper presents CloudRaid , a new automatical tool for finding distributed concurrency bugs efficiently and effectively. Distributed concurrency bugs are notoriously difficult to find as they are triggered by untimely interaction among nodes, i.e., unexpected message orderings. To detect concurrency ...
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A Conflict-free Scheduler for High-performance Graph Processing on Multi-pipeline FPGAsACM Transactions on Architecture and Code Optimization (IF 1.444) Pub Date : 2020-05-30 ,DOI:10.1145/3390523Qinggang Wang, Long Zheng, Jieshan Zhao, Xiaofei Liao, Hai Jin, Jingling Xue
FPGA-based graph processing accelerators are nowadays equipped with multiple pipelines for hardware acceleration of graph computations. However, their multi-pipeline efficiency can suffer greatly from the considerable overheads caused by the read/write conflicts in their on-chip BRAM from different pipelines, leading to significant performance degradation and poor scalability. In this article, we investigate
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[Effects of Long Non-coding RNA Plasmacytoma Variant Translocation 1 Gene on Inflammatory Response and Cell Migration in Helicobacter Pylori Infected Gastric Epithelial Cell Line].Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae Pub Date : 2020-04-28 ,DOI:10.3881/j.issn.1000-503x.11395Xiao-Hui Jing,Ling-Xue Li,Tao-Tao Han,Juan ShiObjective To investigate the mechanism of long non-coding RNA plasmacytoma variant translocation 1 (PVT1) in gastric cancer caused by helicobacter pylori (HP) infection. Methods The expression of PVT1 was detected by quantitative real-time polymerase chain reaction in HP-infected normal gastric epithelial cells GES-1. Gastric cancer cell line SGC-7901 was transfected with PVT1 small interfering RNA ...
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WCET-aware data selection and allocation for scratchpad memoryACM SIGPLAN Notices Pub Date : 2020-04-03 ,DOI:10.1145/2345141.2248425Qing Wan, Hui Wu, Jingling XueIn embedded systems, SPM (scratchpad memory) is an attractive alternative to cache memory due to its lower energy consumption and higher predictability of program execution. This paper studies the problem of placing variables of a program into an SPM such that its WCET (worst-case execution time) is minimized. We propose an efficient dynamic approach that comprises two novel heuristics. The first heuristic ...
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DNNTuneACM Transactions on Architecture and Code Optimization (IF 1.444) Pub Date : 2019-12-26 ,DOI:10.1145/3368305Chunwei Xia, Jiacheng Zhao, Huimin Cui, Xiaobing Feng, Jingling Xue
Deep Neural Networks (DNNs) are now increasingly adopted in a variety of Artificial Intelligence (AI) applications. Meantime, more and more DNNs are moving from cloud to the mobile devices, as emerging AI chips are integrated into mobiles. Therefore, the DNN models can be deployed in the cloud, on the mobile devices, or even mobile-cloud coordinate processing, making it a big challenge to select an
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A Feature-Oriented Corpus for Understanding, Evaluating and Improving Fuzz TestingarXiv - CS - Software Engineering Pub Date : 2019-05-04 ,DOI:arxiv-1905.01405Xiaogang Zhu, Xiaotao Feng, Tengyun Jiao, Sheng Wen, Yang Xiang, Seyit Camtepe, Jingling XueFuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs/binaries. However,existing corpora usually do not capture the features that prevent fuzzers from finding bugs, leading to ambiguous conclusions on the pros and cons of the fuzzers evaluated. A typical example is that Driller ...
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LCCFS: a lightweight distributed file system for cloud computing without journaling and metadata servicesScience China Information Sciences (IF 7.275) Pub Date : 2019-04-04 ,DOI:10.1007/s11432-017-9295-4Li Wang,Jingling Xue,Xiangke Liao,Yunchuan Wen,Min Chen
The major usage of a file system integrated with a cloud computing platform is to provide the storage for VM (virtual machine) instances. Distributed file systems, especially those implemented on top of object storage have many potential advantages over traditional local file systems for VM instance storage. In this paper, we make an investigation in the requirements imposed on a file system in cloud
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Understanding and Analyzing Java ReflectionACM Transactions on Software Engineering and Methodology (IF 3.685) Pub Date : 2019-02-26 ,DOI:10.1145/3295739Yue Li, Tian Tan, Jingling Xue
Java reflection has been widely used in a variety of applications and frameworks. It allows a software system to inspect and change the behaviour of its classes, interfaces, methods, and fields at runtime, enabling the software to adapt to dynamically changing runtime environments. However, this dynamic language feature imposes significant challenges to static analysis, because the behaviour of reflection-rich
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Parallel Construction of Interprocedural Memory SSA FormJournal of Systems and Software (IF 3.514) Pub Date : 2018-12-01 ,DOI:10.1016/j.jss.2018.09.038Yulei Sui, Hua Yan, Zheng Zheng, Yunpeng Zhang, Jingling XueAbstract Interprocedural memory SSA form, which provides a sparse data-flow representation for indirect memory operations, paves the way for many advanced program analyses. Any performance improvement for memory SSA construction benefits for a wide range of clients (e.g., bug detection and compiler optimisations). However, its construction is much more expensive than that for scalar-based SSA form ...
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P okerACM Transactions on Architecture and Code Optimization (IF 1.444) Pub Date : 2018-11-16 ,DOI:10.1145/3280850Feng Zhang, Jingling Xue
We introduce P oker , a permutation-based approach for vectorizing multiple queries over B + -trees. Our key insight is to combine vector loads and path-encoding-based permutations to alleviate memory latency while keeping the number of key comparisons needed for a query to a minimum. Implemented as a C++ template library, P oker represents a general-purpose solution for vectorizing the queries over
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SCPACM Transactions on Architecture and Code Optimization (IF 1.444) Pub Date : 2018-10-10 ,DOI:10.1145/3274654Xing Su, Xiangke Liao, Hao Jiang, Canqun Yang, Jingling Xue
GEneral Matrix Multiply (GEMM) is the most fundamental computational kernel routine in the BLAS library. To achieve high performance, in-memory data must be prefetched into fast on-chip caches before they are used. Two techniques, software prefetching and data packing, have been used to effectively exploit the capability of on-chip least recent used (LRU) caches, which are popular in traditional high-performance
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Ripple : Reflection analysis for Android apps in incomplete information environmentsSoftware: Practice and Experience (IF 3.2) Pub Date : 2018-04-19 ,DOI:10.1002/spe.2577Yifei Zhang,Yue Li,Tian Tan,Jingling Xue
Reflection poses grave problems for static security analysis, despite its widespread use in Android apps. In general, string inference has been mainly used to handle reflection, resulting in significantly missed security vulnerabilities. In this work, we bring forward the ubiquity of incomplete information environments (IIEs) for Android apps, where some critical dataflows are missing during static
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