Evaluating inlining techniques

Kaser, Owen; Ramakrishnan, C. R.

doi:10.1016/s0096-0551(98)00003-4

Cited by 20 publications

(8 citation statements)

References 13 publications

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“…If the result of inlining is that the size of the working set exceeds the size of the instruction cache during program execution, performance can suffer. Therefore most research into inlining focuses on maximizing the inlining benefits under limited code growth constraints [15]. For profile-guided techniques [18], that only inline procedures at frequently executed call-sites, limiting the code growth is equivalent to limiting the growth of the hot code.…”

Section: Relation With Inliningmentioning

confidence: 99%

On the side-effects of code abstraction

et al. 2003

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More and more devices contain computers with limited amounts of memory. As a result, code compaction techniques are gaining popularity, especially when they also improve performance and power consumption, or at least not degrade it. This paper quantifies the side-effects of code abstraction on performance using extensive measurements and simulations on the SPECint2000 benchmark suite and some additional C++ programs. We show how to use profile information in order to obtain almost all the code size reduction benefits of code abstraction, yet experience almost none of its disadvantages.

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Section: Relation With Inliningmentioning

confidence: 99%

On the side-effects of code abstraction

et al. 2003

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“…The constant ratio assumption is used to avoid the cost of a multilevel history. Kaser and Ramakrishman [1998] propose a probabilistic model to estimate the effect of using profile data, with a one-level history based on the constant ratio assumption. When evaluating inlinable calls remaining after optimization, they report good results with their technique compared to other compilers.…”

Section: Profile-directed Methods Inliningmentioning

confidence: 99%

Design and evaluation of dynamic optimizations for a Java just-in-time compiler

Suganuma

Yasue

Kawahito

et al. 2005

ACM Trans. Program. Lang. Syst.

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The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward employing a dynamic compilation system on the basis of online runtime profile information. The trade-off between the compilation overhead and performance benefit is a crucial issue for such a system. This article describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler, together with two techniques for profile-directed optimizations: method inlining and code specialization. Our approach is to employ a mixed mode interpreter and a three-level optimizing compiler, supporting level-1 to level-3 optimizations, each of which has a different set of trade-offs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire period while applications are running to monitor the programs' hot spots. Detailed information on runtime behavior can be collected by dynamically generating instrumentation code that is installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic profile-directed method inlining and code specialization to be performed on the basis of call site information or specific parameter values at the higher optimization levels. The experimental results show that our approach offers high performance and low compilation overhead in both program startup and steady state measurements in comparison to the previous systems. The two profile-directed optimization techniques contribute significant portions of the improvements.

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“…We adopt the so-called original version inlining approach [15], which always considers the original version of the callee and the current version of the caller when performing inlinings. This can be obtained by duplicating the original code of the inlined method.…”

Section: Methods Inliningmentioning

confidence: 99%

“…More formally, if we define: TP = in, out, trans, call, catch then TP |= T P = (G), and TP is minimal w.r.t. the partial order (15), that is:…”

Section: A3 Completeness Of the Tp Analysismentioning

confidence: 99%