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DTSTART;TZID=America/Denver:20231116T163000
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UID:submissions.supercomputing.org_SC23_sess170_pap259@linklings.com
SUMMARY:TrivialSpy: Identifying Software Triviality via Fine-Grained and D
 ataflow-Based Value Profiling
DESCRIPTION:Xin You, Hailong Yang, Kelun Lei, Zhongzhi Luan, and Depei Qia
 n (Beihang University)\n\nTrivial operations cause software inefficiencies
  that waste functional units and memory bandwidth for executing useless in
 structions. Although previous works have identified a significant amount o
 f trivial operations in widely used programs, the proposed solutions only 
 provide useful observations, other than actionable guidance to eliminate t
 rivial operations for better performance. In this paper, we propose Trivia
 lSpy - a fine-grained and dataflow-based value profiler to effectively ide
 ntify software triviality with optimization potential estimation. With the
  help of dataflow analysis, TrivialSpy can detect software trivialities of
  heavy operation, trivial chain, and redundant backward slice. In addition
 , TrivialSpy can identify trivial breakpoints that combine multiple trivia
 l conditions for more optimization opportunities. The evaluation results d
 emonstrate TrivialSpy is capable of identifying software triviality in hig
 hly optimized programs. Based on the optimization guidance provided by Tri
 vialSpy, we can achieve 52.09% performance speedup at maximum after elimin
 ating trivial operations.\n\nTag: Compilers, Performance Measurement, Mode
 ling, and Tools, Performance Optimization, Programming Frameworks and Syst
 em Software\n\nRegistration Category: Tech Program Reg Pass\n\nReproducibi
 lity Badges: Artifact Available, Artifact Functional, Results Reproduced\n
 \nSession Chair: Martin Kong (Ohio State University)\n\n
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