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Supporting On-Stack Replacement in Unstructured Languages by Loop Reconstruction and Extraction∗

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Supporting On-Stack Replacement in Unstructured Languages by Loop Reconstruction and Extraction∗ Supporting On-Stack Replacement in Unstructured Languages by Loop Reconstruction and Extraction∗ Raphael Mosaner David Leopoldseder Manuel Rigger Johannes Kepler University Linz Johannes Kepler University Linz ETH Zürich Austria Austria Switzerland [email protected] [email protected] [email protected] Roland Schatz Hanspeter Mössenböck Oracle Labs Johannes Kepler University Linz Austria Austria [email protected] [email protected] Abstract Replacement in Unstructured Languages by Loop Reconstruction On-stack replacement (OSR) is a common technique em- and Extraction. In Proceedings of the 16th ACM SIGPLAN Interna- ployed by dynamic compilers to reduce program warm-up tional Conference on Managed Programming Languages and Run- times (MPLR ’19), October 21–22, 2019, Athens, Greece. ACM, New time. OSR allows switching from interpreted to compiled York, NY, USA, 14 pages. https://doi.org/10.1145/3357390.3361030 code during the execution of this code. The main targets are long running loops, which need to be represented explicitly, with dedicated information about condition and body, to be optimized at run time. Bytecode interpreters, however, repre- 1 Introduction sent control flow implicitly via unstructured jumps and thus On-stack replacement [9] is a technique employed by dy- do not exhibit the required high-level loop representation. To namic compilers for reducing program warm-up time [1]. enable OSR also for jump-based—often called unstructured— Based on Barrett’s work [1], we define program warm-up languages, we propose the partial reconstruction of loops in as the time it takes a dynamic compiler to identify and com- order to explicitly represent them in a bytecode interpreter. pile the hot parts of a program to reach a steady state of Besides an outline of the general idea, we implemented our peak performance. OSR usually works by detecting hot but approach in Sulong, a bytecode interpreter for LLVM bit- not yet compiled code and performing a switch from the code, which allows the execution of C/C++. We conducted interpreted to the compiled version of this code while it is an evaluation with a set of C benchmarks, which showed being executed. This is most useful for long-running loops, speed-ups in warm-up of up to 9x for certain benchmarks. which can take up most of the execution time and should This facilitates execution of programs with long-running be compiled as soon as possible. However, method-based loops in rarely called functions, which would yield signifi- compilation systems do not directly support OSR, because cant slowdown without OSR. While shown with a prototype methods are the most fine-grained compilation units. For implementation, the overall idea of our approach is general- instance, a computation intensive loop in a main-function izable for all bytecode interpreters. would never be compiled, as the main-function is only called CCS Concepts • Software and its engineering → Just- once and thus never considered hot. To make OSR work in-time compilers; Dynamic compilers; Virtual machines. in such systems, it would be possible to extract loops into arXiv:1909.08815v1 [cs.PL] 19 Sep 2019 separate functions which can be independently compiled Keywords On-stack Replacement, Truffle, Sulong by a method-based compiler even when it lacks support ACM Reference Format: for OSR. While for structured languages, loop bodies could Raphael Mosaner, David Leopoldseder, Manuel Rigger, Roland be extracted to separate functions to allow for their quick Schatz, and Hanspeter Mössenböck. 2019. Supporting On-Stack compilation, unstructured languages lack high-level loop information. To tackle this issue and support OSR for un- ∗This research project is partially funded by Oracle Labs. structured languages in method-based compilation systems MPLR ’19, October 21–22, 2019, Athens, Greece we propose an approach in which we (1) reconstruct loop © 2019 Association for Computing Machinery. information from unstructured, basic block-based program This is the author’s version of the work. It is posted here for your personal representations and (2) explicitly represent loops as separate use. Not for redistribution. The definitive Version of Record was published in functions to enable OSR. Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes (MPLR ’19), October 21–22, 2019, We implemented our approach in Sulong [21, 22], an in- Athens, Greece, https://doi.org/10.1145/3357390.3361030. terpreter for LLVM IR, which suffers from long warm-up time [22]. LLVM IR is an unstructured language that does MPLR ’19, October 21–22, 2019, Athens, Greece R. Mosaner, D. Leopoldseder, M. Rigger, R. Schatz, H. Mössenböck not explicitly represent loops. Sulong is used in the multi- to then return this value to its parent. Truffle uses the dy- lingual GraalVM [7, 18, 25] to implement native function namic Graal compiler [32] to efficiently compile frequently- interfaces of dynamic languages such as TruffleRuby [2]. All executed functions to machine code. When invoked, it recur- these language implementations are based on a common lan- sively inlines all node execution methods of the AST (which guage implementation framework, called Truffle [31]. It uses is a form of partial evaluation [10]), to then further optimize dynamic-compilation based on profiling information gath- it. ered during interpretation of abstract syntax trees (ASTs) To achieve optimal performance, language implementers for efficient execution of the implemented language. Truffle have to use various constructs of the Truffle framework. does not directly support OSR. However, it provides language Graal, when used as a standard Java compiler, is capable of implementers with a framework to enable loop-level OSR performing OSR on loop-level. Truffle, however, provides which requires extracting loops to form separate compilation an interface that needs to be implemented by guest lan- units as outlined above. We demonstrate that reconstructing guage nodes to support OSR for structured languages. This loops and extracting them to separate units gives significant RepeatingNode interface assumes that the interpreted lan- speed-ups. Specifically, our evaluation with a set of well- guage has a concept of high-level loops in order to enable known benchmarks shows significant reductions by up to OSR. 9x in program warm-up given that OSR is applicable. Note Listing 1 shows a typical implementation of a that our approach can be used by other Truffle bytecode- RepeatingNode. The executeRepeating() method based implementations lacking high-level loop information, executes only a single loop iteration and returns either including GraalSqueak [19], Truffle Java [11] and Truffle true if the loop is to be executed again or false if it was CIL. Furthermore, it is applicable in any compiler with a the loop’s last iteration. The Truffle framework wraps the background system which provides means for establishing RepeatingNode with a LoopNode that executes the loop. Its mappings between extracted control flow. implementation is transparent to the language implementer. In summary, this paper contributes the following: Since each loop iteration is performed individually, the LoopNode can trigger compilation after any loop iteration • a novel multi-tier approach for employing OSR for and switch from interpreted to compiled execution. Cur- unstructured languages rently, Truffle uses a constant iteration threshold before – detection of loops from unstructured control flow OSR-compiling a loop. Note that this technique is generally – reconstruction of high-level loops applicable to compilation systems for adding OSR support. – extraction of loops into separate functions to model loop-level OSR using method-based compilation class LLVMRepeatingNode implements RepeatingNode { public boolean executeRepeating(Frame frame) { • a prototype implementation in Sulong, if (( boolean )conditionNode.execute(frame)) { • extensive experiments suggesting significant improve- try { bodyNode. execute(frame) ; ments in warm-up performance. } catch (BreakException e) { return false ; } catch (ContinueException e) { return true ; 2 Background } return true ; This section provides the necessary background informa- } return false ; tion to understand our approach for supporting OSR in } Truffle-based implementations of unstructured languages. } We first give an overview of the Truffle language implemen- Listing 1. A typical implementation of a RepeatingNode tation framework and its OSR mechanism for structured lan- for structured languages. Both conditionNode(loop guages. We then discuss bytecode-interpreter-based Truffle condition) and bodyNode(loopBody) are child nodes of the languages and the problems they cause for OSR. We imple- RepeatingNode. Execution of the body can be interrupted by mented a prototype of our approach in Sulong, an interpreter break- or continue-statements, which throw an exception. for LLVM IR. Thus, we also give background information about Sulong and LLVM. 2.2 Sulong and LLVM IR 2.1 Truffle and Graal Sulong [21, 22] is a bytecode interpreter for LLVM IR built The Truffle language implementation framework [32] pro- on top of Truffle. LLVM IR is the intermediate representation vides language implementers with means for creating effi- of the LLVM compilation framework [16], which provides cient Abstract Syntax Tree (AST) interpreters. In an AST front ends for various languages such as C/C++ and Fortran, interpreter, each
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