Affine Parallelization Using Dependence and Cache Analysis in a Binary Rewriter

Kotha, Aparna; Anand, Kapil; Creech, Timothy; ElWazeer, Khaled; Smithson, Matthew; Yellareddy, Greeshma; Barua, Rajeev

doi:10.1109/tpds.2014.2349501

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Pradelle et al transformed the binary into the C source code while extracting high-level information and applied the existing parallelizer to the C source code for parallelization of binary codes [34]. Kotha et al adapted the existing parallelization methods for source codes into binary codes for automatic parallelization of binary codes [35]. Sato et al dynamically generated improved binaries by transforming the input binary into the IR code and by optimizing the IR code [36].…”

Section: E Binary Transformation and Optimizationmentioning

confidence: 99%

Redundancy Analysis and Elimination on Access Patterns of the Windows Applications Based on I/O Log Data

Lee

Kwon

2020

IEEE Access

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In this paper, we analyze I/O log data monitored in the Windows operating system for improving the system performance. Especially, we focus on the I/O operations to the Windows registry. As a result, we identify redundant access patterns of the Windows applications. To find all the possible redundant patterns from the large-scale log data, we propose the redundancy detection algorithm. Then, we propose the two-level redundancy elimination method to remove unnecessary redundant operations. We also present an event-driven method that guarantees that the result of redundancy elimination is equivalent to that of the original program. Through experiments, we show that the proposed redundancy elimination method improves the performance of the original program having redundant access patterns by up to 90.25% for individual access patterns; by 8.93% ∼ 26.21% when the multiple programs having combined access patterns are running concurrently. INDEX TERMS I/O log data, access pattern analysis, redundancy elimination, Windows registry.

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Section: E Binary Transformation and Optimizationmentioning

confidence: 99%

Redundancy Analysis and Elimination on Access Patterns of the Windows Applications Based on I/O Log Data

Lee

Kwon

2020

IEEE Access

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“…Furthermore, static analysis is generally applicable at the source code level. Since dynamic analysis relies on detecting dependence violations rather than proving their impossibility, it could be applied at binary-level [4].…”

Section: Introductionmentioning

confidence: 99%

Binary-level data dependence analysis of hot execution regions using abstract interpretation at runtime

et al. 2020

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With the widespread of multicore systems, automatic parallelization becomes more pronounced, particularly for legacy programs, where the source code is not generally available. An essential operation in any parallelization system is detecting data dependence among parallelization candidate instructions. Conducting dependence analysis at the binary-level is more challenging than that at the source-level due to the much lower semantics of the binary code. In this paper, we consider using the elaborate 'static' analysis of abstract interpretation, for the first time, at runtime for data dependence detection. Specifically, our system interprets instructions at a hot region, while at the same time, collect programs semantics for seen program points, thereby conducting abstract interpretation analysis dynamically. The analysis is guaranteed to be correct as long as execution does not exit the region prematurely. Moreover, successive hot region re-entries will resume previous analysis, albeit much faster in case no major change in the program semantics. Such approach provides for more rigorous analysis than other simple dynamic analysis which would typically miss parallelization opportunities. The proposed approach also does not require any hardware support, availability of the source code, as well as any code re-compilation. To study the performance and accuracy of our approach, we have extended the Padrone dynamic code modification framework, and conduct an initial study on a set of PolyBench kernels and selected programs from SPEC CPU. Experimental results show accurate dependence detection with low overhead.

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A Stack Memory Abstraction and Symbolic Analysis Framework for Executables

Anand

ElWazeer

Kotha

et al. 2016

ACM Trans. Softw. Eng. Methodol.

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This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct stack-memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise stack-memory model, and scalability. Furthermore, they ignore memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing stack-memory models by 25%, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

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Affine Parallelization Using Dependence and Cache Analysis in a Binary Rewriter

Cited by 4 publications

References 18 publications

Redundancy Analysis and Elimination on Access Patterns of the Windows Applications Based on I/O Log Data

Redundancy Analysis and Elimination on Access Patterns of the Windows Applications Based on I/O Log Data

Binary-level data dependence analysis of hot execution regions using abstract interpretation at runtime

A Stack Memory Abstraction and Symbolic Analysis Framework for Executables

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