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"abstract": "We propose the concept of Speculative Execution for Visual Analytics and discuss its effectiveness for model exploration and optimization. Speculative Execution enables the automatic generation of alternative, competing model configurations that do not alter the current model state unless explicitly confirmed by the user. These alternatives are computed based on either user interactions or model quality measures and can be explored using delta-visualizations. By automatically proposing modeling alternatives, systems employing Speculative Execution can shorten the gap between users and models, reduce the confirmation bias and speed up optimization processes. In this paper, we have assembled five application scenarios showcasing the potential of Speculative Execution, as well as a potential for further research.",
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"authors": [
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"affiliation": "National University of Defense Technology,National Lab for Parallel and Distributed Processing,Changsha,China",
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"affiliation": "National University of Defense Technology,National Lab for Parallel and Distributed Processing,Changsha,China",
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"affiliation": "National University of Defense Technology,National Lab for Parallel and Distributed Processing,Changsha,China",
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"affiliation": "National University of Defense Technology,National Lab for Parallel and Distributed Processing,Changsha,China",
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"affiliation": "National University of Defense Technology,National Lab for Parallel and Distributed Processing,Changsha,China",
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"abstract": "Since April 2008, a \"Digital Inclusion\" research cluster gathering researchers and professional practitioners from British universities and industry has focused on discussing and developing methodologies to increase the accessibility of digital systems to \"digitally disenfranchised groups\", including special needs and elderly users.We here report on the Ontologies and Interactive Systems international workshop (Ontoract'08), which was held as a related activity to this initiative. The workshop gathered experts and researchers who discussed their respective expertise and exchanged their experiences of using knowledge-based methodologies and techniques to solve complex HCI and Interaction Design issues. Of particular interest were solutions designed to address the challenges of inclusion for all in the digital society.",
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"abstract": "Internet and database technology provide the conditions for us to learn human behaviors empirically. Exploring these behaviors is important for us to understand internet users’ surfing habits, and to set the stage for better research on social dynamics closer to reality. In this paper, we explore the Internet users’ post and reply behaviors et al. in an internet forum, Tianya. Statistical analysis shows that, internet users’ post, reply, replied, page view, in-degree and out-degree distribution all follow power-law form, which is qualitatively agree with achievements of other researchers.",
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"content": "Internet and database technology provide the conditions for us to learn human behaviors empirically. Exploring these behaviors is important for us to understand internet users’ surfing habits, and to set the stage for better research on social dynamics closer to reality. In this paper, we explore the Internet users’ post and reply behaviors et al. in an internet forum, Tianya. Statistical analysis shows that, internet users’ post, reply, replied, page view, in-degree and out-degree distribution all follow power-law form, which is qualitatively agree with achievements of other researchers.",
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"normalizedAbstract": "Internet and database technology provide the conditions for us to learn human behaviors empirically. Exploring these behaviors is important for us to understand internet users’ surfing habits, and to set the stage for better research on social dynamics closer to reality. In this paper, we explore the Internet users’ post and reply behaviors et al. in an internet forum, Tianya. Statistical analysis shows that, internet users’ post, reply, replied, page view, in-degree and out-degree distribution all follow power-law form, which is qualitatively agree with achievements of other researchers.",
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"abstract": "There is an increasingly concern with the use of retrospective power. Some researchers suggested calculating the retrospective power after conducting hypothesis testing, particularly in meta-analysis. They considered that retro-spective power can be used to determine whether the sample size is sufficient in an experiment and that the combination of retrospective power and prior power can enhance the power of current and future studies. Furthermore, the commonly used statistical software packages, such as SPSS and SAS, provide retrospective power analysis in some modules. However, some pointed out the abuse of retrospective power. In this study, we used Monte Carlo technique to estimate the retrospective power in t-test and ANOVA to address this issue. We found that four styles of retrospective powers, including observed power, plug-in power, unbiased power and median power, are highly positive correlated. They dispersed distribute in the range of (0.05, 1). The width of 25th-75th percentages achieved 0.38-0.54, the standard deviations 0.25-0.30 for 0.7 of the statistical power. However, they all monotonously decreased in respect to P value. The retrospective powers were negatively correlated with P values with Pearson coefficients ranged from -0.753 to -0.831 (p",
"abstracts": [
{
"abstractType": "Regular",
"content": "There is an increasingly concern with the use of retrospective power. Some researchers suggested calculating the retrospective power after conducting hypothesis testing, particularly in meta-analysis. They considered that retro-spective power can be used to determine whether the sample size is sufficient in an experiment and that the combination of retrospective power and prior power can enhance the power of current and future studies. Furthermore, the commonly used statistical software packages, such as SPSS and SAS, provide retrospective power analysis in some modules. However, some pointed out the abuse of retrospective power. In this study, we used Monte Carlo technique to estimate the retrospective power in t-test and ANOVA to address this issue. We found that four styles of retrospective powers, including observed power, plug-in power, unbiased power and median power, are highly positive correlated. They dispersed distribute in the range of (0.05, 1). The width of 25th-75th percentages achieved 0.38-0.54, the standard deviations 0.25-0.30 for 0.7 of the statistical power. However, they all monotonously decreased in respect to P value. The retrospective powers were negatively correlated with P values with Pearson coefficients ranged from -0.753 to -0.831 (p",
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"abstract": "Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.",
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"abstract": "We describe a method for spatio-temporal data mining based on expected distribution domain generalization (ExGen) graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranking possible summaries according to current expectations, and then allow the user to adjust these expectations.",
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"content": "We describe a method for spatio-temporal data mining based on expected distribution domain generalization (ExGen) graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranking possible summaries according to current expectations, and then allow the user to adjust these expectations.",
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"fno": "19120181",
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"authors": [
{
"affiliation": "University of Regina",
"fullName": "Howard J. Hamilton",
"givenName": "Howard J.",
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{
"affiliation": "University of Regina",
"fullName": "Liqiang Geng",
"givenName": "Liqiang",
"surname": "Geng",
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{
"affiliation": "University of Regina",
"fullName": "Leah Findlater",
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{
"affiliation": "University of Regina",
"fullName": "Dee Jay Randall",
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"doi": "10.1109/ICDE48307.2020.00102",
"title": "An Interval-centric Model for Distributed Computing over Temporal Graphs",
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"abstract": "Algorithms for temporal property graphs may be time-dependent (TD), navigating the structure and time concurrently, or time-independent (TI), operating separately on different snapshots. Currently, there is no unified and scalable programming abstraction to design TI and TD algorithms over large temporal graphs. We propose an interval-centric computing model (ICM) for distributed and iterative processing of temporal graphs, where a vertex's time-interval is a unit of data-parallel computation. It introduces a unique time-warp operator for temporal partitioning and grouping of messages that hides the complexity of designing temporal algorithms, while avoiding redundancy in user logic calls and messages sent. GRAPHITE is our implementation of ICM over Apache Giraph, and we use it to design 12 TI and TD algorithms from literature. We rigorously evaluate its performance for diverse real-world temporal graphs - as large as 131M vertices and 5.5B edges, and as long as 219 snapshots. Our comparison with 4 baseline platforms on a 10-node commodity cluster shows that ICM shares compute and messaging across intervals to out-perform them by up to 25×, and matches them even in worst-case scenarios. GRAPHITE also exhibits weak-scaling with near-perfect efficiency.",
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"content": "Algorithms for temporal property graphs may be time-dependent (TD), navigating the structure and time concurrently, or time-independent (TI), operating separately on different snapshots. Currently, there is no unified and scalable programming abstraction to design TI and TD algorithms over large temporal graphs. We propose an interval-centric computing model (ICM) for distributed and iterative processing of temporal graphs, where a vertex's time-interval is a unit of data-parallel computation. It introduces a unique time-warp operator for temporal partitioning and grouping of messages that hides the complexity of designing temporal algorithms, while avoiding redundancy in user logic calls and messages sent. GRAPHITE is our implementation of ICM over Apache Giraph, and we use it to design 12 TI and TD algorithms from literature. We rigorously evaluate its performance for diverse real-world temporal graphs - as large as 131M vertices and 5.5B edges, and as long as 219 snapshots. Our comparison with 4 baseline platforms on a 10-node commodity cluster shows that ICM shares compute and messaging across intervals to out-perform them by up to 25×, and matches them even in worst-case scenarios. GRAPHITE also exhibits weak-scaling with near-perfect efficiency.",
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"affiliation": "School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Korea, Republic of",
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"abstract": "The purpose of this paper is to present an efficient distributed cycle/knot detection, algorithm for general graphs which will determine whether a given node is a member of a knot or a cycle. This is relevant to an application such as parallel simulation in which (1) cycles and knots can arise frequently, (2) the size of the graph is very large and (3) it is necessary to know if a given node is in a cycle or a knot. The algorithm is based on a diffusing computation. It requires less communication cost than preceding algorithms and is the first algorithm capable of detecting both cycles and knots. The algorithm differs from the classical diffusing computation methods through its use of incomplete search messages to speed up the computation. The algorithm requires a total of at most 2m messages, where m is the number of links. This is compared to Chandy-Misra's algorithm (1982) which requires at least (3m+n), where n is a number of nodes and m is the number of links. The algorithm. Requires O(log(n)) bits of memory. Various applications for the cycle/knot detection algorithm are presented. In particular, we demonstrate its importance to deadlock detection to algorithms for parallel simulation which employ a blocking paradigm and a deadlock breaking technique known as TNE/DLTNE.",
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"title": "Untangling Mathematical Knots with Simulated Annealing and Opposition-Based Learning",
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"abstract": "The fundamental problem in knot theory is to determine whether a given knot is an unknot. Solving such problem with existing computational algorithms or heuristic methods still remains challenging. In this paper we describe a hybrid algorithm that employs simulated annealing (SA) algorithm and opposition-based learning strategy to simplify and accelerate the simulation of mathematical knot relaxation. In our work, mathematical knots are modeled as closed node-link diagrams with energies charged at each node. A mathematical force model with adaptive step sizes is incorporated into SA for new individual generation. One elementary move with a descending number of nodes is included to prune the conformation and reduce the redundant information following the isotopy consistency. However, the force-directed model may be trapped to a local energy minimum. To overcome such intrinsic limitation, we adopt an opposition force based learning strategy to tackle this problem. With a flexible neighborhood and the instructive disturbance information, the knot geometry is able to untangle quickly and can reach the global minimum state without extra intervention. Illustrative test cases are presented, including three unknots and two classical knots with complex isotopy.",
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"abstract": "Geometric problems of interest to mathematical visualization applications involve changing structures, such as the moves that transform one knot into an equivalent knot. In this paper, we describe mathematical entities (curves and surfaces) as link-node graphs, and make use of energy-driven relaxation algorithms to optimize their geometric shapes by moving knots and surfaces to their simplified equivalence. Furthermore, we design and conFigure parallel functional units in the relaxation algorithms to accelerate the computation these mathematical deformations require. Results show that we can achieve significant performance optimization via the proposed threading model and level of parallelization.",
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"abstract": "Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We herein address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on real super-resolution microscopy data from a time-lapse sequence of living cells.",
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"content": "Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We herein address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on real super-resolution microscopy data from a time-lapse sequence of living cells.",
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"abstract": "The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision farming), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision farming (e.g., optimizing irrigation practices and other land management decisions).",
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"abstract": "While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edits whose changes do not depend on each other exist. We empirically investigated the impact of systematic edits on history slicing. In this study, commits in which systematic edits were detected are split between each file so that unnecessary dependencies between commits are eliminated. In several histories of open source systems, the size of history slices was reduced by 13.3-57.2% on average after splitting the commits for systematic edits.",
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"abstract": "Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication, from the commit history of projects. Most of the studied networks are based on the co-authorship of software artefacts defined at the level of files, modules, or packages. While this approach has led to insights into the social aspects of software development, it neglects detailed information on code changes and code ownership, e.g. which exact lines of code have been authored by which developers, that is contained in the commit log of software projects. Addressing this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. This information allows us to construct directed, weighted, and time-stamped networks, where a link signifies that one developer has edited a block of source code originally written by another developer. Our tool is applied in case studies of an Open Source and a commercial software project. We argue that it opens up a massive new source of high-resolution data on human collaboration patterns.",
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"abstract": "Detecting similar functions in binary executables serves as a foundation for many binary code analysis and reuse tasks. By far, recognizing similar components in binary code remains a challenge. Existing research employs either static or dynamic approaches to capture program syntax or semantics-level features for comparison. However, there exist multiple design limitations in previous work, which result in relatively high cost, low accuracy and scalability, and thus severely impede their practical use. In this paper, we present a novel method that leverages in-memory fuzzing for binary code similarity analysis. Our prototype tool IMF-SIM applies in-memory fuzzing to launch analysis towards every function and collect traces of different kinds of program behaviors. The similarity score of two behavior traces is computed according to their longest common subsequence. To compare two functions, a feature vector is generated, whose elements are the similarity scores of the behavior trace-level comparisons. We train a machine learning model through labeled feature vectors; later, for a given feature vector by comparing two functions, the trained model gives a final score, representing the similarity score of the two functions. We evaluate IMF-SIM against binaries compiled by different compilers, optimizations, and commonly-used obfuscation methods, in total over one thousand binary executables. Our evaluation shows that IMF-SIM notably outperforms existing tools with higher accuracy and broader application scopes.",
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"content": "Binary modification allows users to alter existing code or inject new code into programs without requiring source code, symbols, or debugging information. It is critically important that such modification not accidentally create a structurally invalid binary that has illegal control flow or executes invalid instructions. Unfortunately, current modification tools do not make this guarantee, instead relying on the user to manually ensure the modified binary is valid. In addition, they fail to provide high-level abstractions of the binary (e.g., functions), instead requiring the user to have a deep understanding of the idiosyncrasies of the instruction set and the behavior of the program. We present structured binary editing, which allows users to modify a program binary by modifying its control flow graph (CFG). We define an algebra of CFG transformations that is closed under a CFG validity constraint, thus ensuring that users can arbitrarily compose these transformations while preserving structural validity. We have implemented structured binary editing in the Dyninst binary analysis and instrumentation framework, creating a high-level binary modification toolkit. We demonstrate the usefulness of our approach by creating a hot patching tool that closes three vulnerabilities in a running, unmodified Apache HTTPD server without interrupting the server's execution.",
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"normalizedAbstract": "Binary modification allows users to alter existing code or inject new code into programs without requiring source code, symbols, or debugging information. It is critically important that such modification not accidentally create a structurally invalid binary that has illegal control flow or executes invalid instructions. Unfortunately, current modification tools do not make this guarantee, instead relying on the user to manually ensure the modified binary is valid. In addition, they fail to provide high-level abstractions of the binary (e.g., functions), instead requiring the user to have a deep understanding of the idiosyncrasies of the instruction set and the behavior of the program. We present structured binary editing, which allows users to modify a program binary by modifying its control flow graph (CFG). We define an algebra of CFG transformations that is closed under a CFG validity constraint, thus ensuring that users can arbitrarily compose these transformations while preserving structural validity. We have implemented structured binary editing in the Dyninst binary analysis and instrumentation framework, creating a high-level binary modification toolkit. We demonstrate the usefulness of our approach by creating a hot patching tool that closes three vulnerabilities in a running, unmodified Apache HTTPD server without interrupting the server's execution.",
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"abstract": "Source code instrumentation is a widely used method to generate fast software simulation models by annotating timing information into application source code. Source-level simulation models can be easily integrated into SystemC based simulation environment for fast simulation of complex multiprocessor systems. The accurate back-annotation of the timing information relies on the mapping between source code and binary code. The compiler optimizations might make it hard to get accurate mapping information. This paper addresses the mapping problems caused by complex compiler optimizations, which are the main source of simulation errors. To obtain accurate mapping information, we propose a method called fine-grained flow mapping that establishes a mapping between sequences of control flow of source code and binary code. In case that the code structure of a program is heavily altered by compiler optimizations, we propose to replace the altered part of the source code with functionally-equivalent IR-level code which has an optimized structure, leading to Partly Optimized Source Code (POSC). Then the flow mapping can be established between the POSC and the binary code and the timing information is back-annotated to the POSC. Our experiments demonstrate the accuracy and speed of simulation models generated by our approach.",
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"content": "Source code instrumentation is a widely used method to generate fast software simulation models by annotating timing information into application source code. Source-level simulation models can be easily integrated into SystemC based simulation environment for fast simulation of complex multiprocessor systems. The accurate back-annotation of the timing information relies on the mapping between source code and binary code. The compiler optimizations might make it hard to get accurate mapping information. This paper addresses the mapping problems caused by complex compiler optimizations, which are the main source of simulation errors. To obtain accurate mapping information, we propose a method called fine-grained flow mapping that establishes a mapping between sequences of control flow of source code and binary code. In case that the code structure of a program is heavily altered by compiler optimizations, we propose to replace the altered part of the source code with functionally-equivalent IR-level code which has an optimized structure, leading to Partly Optimized Source Code (POSC). Then the flow mapping can be established between the POSC and the binary code and the timing information is back-annotated to the POSC. Our experiments demonstrate the accuracy and speed of simulation models generated by our approach.",
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"content": "Function recognition in program binaries serves as the foundation for many binary instrumentation and analysis tasks. However, as binaries are usually stripped before distribution, function information is indeed absent in most binaries. By far, identifying functions in stripped binaries remains a challenge. Recent research work proposes to recognize functions in binary code through machine learning techniques. The recognition model, including typical function entry point patterns, is automatically constructed through learning. However, we observed that as previous work only leverages syntax-level features to train the model, binary obfuscation techniques can undermine the pre-learned models in real-world usage scenarios. In this paper, we propose FID, a semantics-based method to recognize functions in stripped binaries. We leverage symbolic execution to generate semantic information and learn the function recognition model through well-performing machine learning techniques.FID extracts semantic information from binary code and, therefore, is effectively adapted to different compilers and optimizations. Moreover, we also demonstrate that FID has high recognition accuracy on binaries transformed by widely-used obfuscation techniques. We evaluate FID with over four thousand test cases. Our evaluation shows that FID is comparable with previous work on normal binaries and it notably outperforms existing tools on obfuscated code.",
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"title": "Exploring GNN Based Program Embedding Technologies for Binary Related Tasks",
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"abstract": "With the rapid growth of program scale, program analysis, mainte-nance and optimization become increasingly diverse and complex. Applying learning-assisted methodologies onto program analysis has attracted ever-increasing attention. However, a large number of program factors including syntax structures, semantics, running platforms and compilation configurations block the effective re-alization of these methods. To overcome these obstacles, existing works prefer to be on a basis of source code or abstract syntax tree, but unfortunately are sub-optimal for binary-oriented analysis tasks closely related to the compilation process. To this end, we propose a new program analysis approach that aims at solving program-level and procedure-level tasks with one model, by taking advantage of the great power of graph neural networks from the level of binary code. By fusing the semantics of control flow graphs, data flow graphs and call graphs into one model, and embedding instructions and values simultaneously, our method can effectively work around emerging compilation-related problems. By testing the proposed method on two tasks, binary similarity detection and dead store prediction, the results show that our method is able to achieve as high accuracy as 83.25%, and 82.77%.",
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{
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"content": "With the rapid growth of program scale, program analysis, mainte-nance and optimization become increasingly diverse and complex. Applying learning-assisted methodologies onto program analysis has attracted ever-increasing attention. However, a large number of program factors including syntax structures, semantics, running platforms and compilation configurations block the effective re-alization of these methods. To overcome these obstacles, existing works prefer to be on a basis of source code or abstract syntax tree, but unfortunately are sub-optimal for binary-oriented analysis tasks closely related to the compilation process. To this end, we propose a new program analysis approach that aims at solving program-level and procedure-level tasks with one model, by taking advantage of the great power of graph neural networks from the level of binary code. By fusing the semantics of control flow graphs, data flow graphs and call graphs into one model, and embedding instructions and values simultaneously, our method can effectively work around emerging compilation-related problems. By testing the proposed method on two tasks, binary similarity detection and dead store prediction, the results show that our method is able to achieve as high accuracy as 83.25%, and 82.77%.",
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"affiliation": "Michigan Tech,Houghton,USA",
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"abstract": "Static Worst-Case Execution Time (WCET) estimation techniques operate upon the binary code of a program in order to provide the necessary input for schedulability analysis techniques. Compilers used to generate this binary code include tens of optimizations, that can radically change the flow information of the program. Such information is hard to be maintained across optimization passes and may render automatic extraction of important flow information, such as loop bounds, impossible. Thus, compiler optimizations, especially the sophisticated optimizations of mainstream compilers, are typically avoided. In this work, we explore for the first time iterative-compilation techniques that reconcile compiler optimizations and static WCET estimation. We propose a novel learning technique that selects sequences of optimizations that minimize the WCET estimate of a given program. We experimentally evaluate the proposed technique using an industrial WCET estimation tool (AbsInt aiT) over a set of 46 benchmarks from four different benchmarks suites, including reference WCET benchmark applications, image processing kernels and telecommunication applications. Experimental results show that WCET estimates are reduced on average by 20.3% using the proposed technique, as compared to the best compiler optimization level applicable.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Static Worst-Case Execution Time (WCET) estimation techniques operate upon the binary code of a program in order to provide the necessary input for schedulability analysis techniques. Compilers used to generate this binary code include tens of optimizations, that can radically change the flow information of the program. Such information is hard to be maintained across optimization passes and may render automatic extraction of important flow information, such as loop bounds, impossible. Thus, compiler optimizations, especially the sophisticated optimizations of mainstream compilers, are typically avoided. In this work, we explore for the first time iterative-compilation techniques that reconcile compiler optimizations and static WCET estimation. We propose a novel learning technique that selects sequences of optimizations that minimize the WCET estimate of a given program. We experimentally evaluate the proposed technique using an industrial WCET estimation tool (AbsInt aiT) over a set of 46 benchmarks from four different benchmarks suites, including reference WCET benchmark applications, image processing kernels and telecommunication applications. Experimental results show that WCET estimates are reduced on average by 20.3% using the proposed technique, as compared to the best compiler optimization level applicable.",
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"normalizedAbstract": "Static Worst-Case Execution Time (WCET) estimation techniques operate upon the binary code of a program in order to provide the necessary input for schedulability analysis techniques. Compilers used to generate this binary code include tens of optimizations, that can radically change the flow information of the program. Such information is hard to be maintained across optimization passes and may render automatic extraction of important flow information, such as loop bounds, impossible. Thus, compiler optimizations, especially the sophisticated optimizations of mainstream compilers, are typically avoided. In this work, we explore for the first time iterative-compilation techniques that reconcile compiler optimizations and static WCET estimation. We propose a novel learning technique that selects sequences of optimizations that minimize the WCET estimate of a given program. We experimentally evaluate the proposed technique using an industrial WCET estimation tool (AbsInt aiT) over a set of 46 benchmarks from four different benchmarks suites, including reference WCET benchmark applications, image processing kernels and telecommunication applications. Experimental results show that WCET estimates are reduced on average by 20.3% using the proposed technique, as compared to the best compiler optimization level applicable.",
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"Learning Artificial Intelligence",
"Optimising Compilers",
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"Binary Code",
"Static WCET Estimation",
"Learning Technique",
"Industrial WCET Estimation Tool",
"Compiler Optimization",
"Schedulability Analysis",
"Static Worst Case Execution Time Estimation Techniques",
"Worst Case Execution Time Estimation Compiler Optimizations Iterative Compilation"
],
"authors": [
{
"affiliation": "INSA, IETR, UMR 6164, France",
"fullName": "Mickaël Dardaillon",
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{
"affiliation": "Univ Rennes, Inria, CNRS, IRISA, France",
"fullName": "Stefanos Skalistis",
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{
"affiliation": "Univ Rennes, Inria, CNRS, IRISA, France",
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{
"affiliation": "Univ Rennes, Inria, CNRS, IRISA, France",
"fullName": "Steven Derrien",
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