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Towards realistic results for instrumentation-based profilers for JIT-compiled systems

Burchell, Humphrey, Larose, Octave, Marr, Stefan (2024) Towards realistic results for instrumentation-based profilers for JIT-compiled systems. In: Proceedings of the 21st ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes. Proceedings of the 21st ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes (MPLR '24). . pp. 82-89. ISBN 979-8-4007-1118-3. (doi:10.1145/3679007.3685058) (KAR id:106797)

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Abstract

Profilers are crucial tools for identifying and improving application performance. However, for language implementations with just-in-time (JIT) compilation, e.g., for Java and JavaScript, instrumentation-based profilers can have significant overheads and report unrealistic results caused by the instrumentation.

In this paper, we examine state-of-the-art instrumentation-based profilers for Java to determine the realism of their results. We assess their overhead, the effect on compilation time, and the generated bytecode. We found that the profiler with the lowest overhead increased run time by 82x. Additionally, we investigate the realism of results by testing a profiler’s ability to detect whether inlining is enabled, which is an important compiler optimization. Our results document that instrumentation can alter program behavior so that performance observations are unrealistic, i.e., they do not reflect the performance of the uninstrumented program.

As a solution, we sketch late-compiler-phase-based instrumentation for just-in-time compilers, which gives us the precision of instrumentation-based profiling with an overhead that is multiple magnitudes lower than that of standard instrumentation-based profilers, with a median overhead of 23.3% (min. 1.4%, max. 464%). By inserting probes late in the compilation process, we avoid interfering with compiler optimizations, which yields more realistic results.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/3679007.3685058
Uncontrolled keywords: profiling, instrumentation, sampling, accuracy
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: Engineering and Physical Sciences Research Council (https://ror.org/0439y7842)
Royal Society (https://ror.org/03wnrjx87)
Depositing User: Stefan Marr
Date Deposited: 05 Aug 2024 11:19 UTC
Last Modified: 15 Nov 2024 11:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/106797 (The current URI for this page, for reference purposes)

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