Performance Research and Optimization on Cpython's Interpreter

Proceedings of the Federated Conference on DOI: 10.15439/2015F139 Computer Science and Information Systems pp. 435–441 ACSIS, Vol. 5 Performance Research and Optimization on CPython’s Interpreter Huaxiong Cao, Naijie Gu1, Kaixin Ren, and Yi Li 1) Department of Computer Science and Technology, University of Science and Technology of China 2) Anhui Province Key Laboratory of Computing and Communication Software 3) Institute of Advanced Technology, University of Science and Technology of China Hefei, China, 230027 Email: [email protected], [email protected] Abstract—In this paper, the performance research on monitoring programs’ behavior and using this information to CPython’s latest interpreter is presented, concluding that drive optimization decisions [1]. The dominant concepts that bytecode dispatching takes about 25 percent of total execution have influenced effective optimization technologies in today’s time on average. Based on this observation, a novel bytecode virtual machines include JIT compilers, interpreters, and their dispatching mechanism is proposed to reduce the time spent on this phase to a minimum. With this mechanism, the blocks integrations. associated with each kind of bytecodes are rewritten in JIT (just-in-time) techniques exploit the well-known fact that hand-tuned assembly, their opcodes are renumbered, and their large scale programs usually spend the majority of time on a memory spaces are rescheduled. With these preparations, this small fraction of the code [2]. During the execution of new bytecode dispatching mechanism replaces the interpreters, they record the bytecode blocks which have been time-consuming memory reading operations with rapid executed more than a specified number of times, and cache the operations on registers. This mechanism is implemented in CPython-3.3.0. Experiments binary code associated to these blocks. The next time these on lots of benchmarks demonstrate its correctness and efficiency. bytecode blocks are executed, it has no need to interpreter these The comparison between original CPython and optimized bytecodes once again, just jumps to the cached binary code, and CPython shows that this new mechanism achieves about 8.5 continues the execution. By this way, much interpreting work is percent performance improvement on average. For some omitted, and performance improvement can be achieved. particular benchmarks, the maximum improvement is up to 18 However, since the threshold is quite large in general, the percentages. interpreting stage still accounts for a large proportion of total execution time. JIT strategies work well for for/while blocks I. INTRODUCTION which likely exist in science compute field. For the programs, he past decade has witnessed the widespread use of whose purposes are remote configuration, warning, tracing, Python, which is a typical dynamic language designed statistics, communication, etc, it is very hard to find hot blocks. T to execute on virtual machines. Several software Though not large, these programs are usually executed quite engineering advantages over statically compiled binaries, frequently. including portable program representations, thread manage Interpreters have a series of advantages which make them -ment, some safety guarantees, built-in automatic memory, and attractive [3]. Firstly, optimizing interpreters reduces the dynamic program composition through dynamic class loading, uptime of all Python programs with or without hot blocks. are provided by Python. These advanced features enhance the Secondly, they are quite simpler to construct than JIT compilers, user programming model and drive the success of Python making them quicker, more reliable to construct and easier to language. However, these features also require the dynamic maintain. Thirdly, interpreters require less memory than JIT compilers to do quite a lot of extra operations, such as type compilers, for both the interpreted virtual machine code and the checking, wrapping/unwrapping of boxed values, virtual interpreter itself. Interpreters for dynamic language have two method dispatching, bytecode dispatching, etc. These extra main structures [1]: switch-based structure and threaded-based operations usually make Python programs several or dozens of structure, whose details are given in section 2 part B. The latter times slower than static programs achieving the same is the latest and most efficient mechanism. Recently, many functionality. Moreover, traditional static program optimiza researchers have applied dynamic techniques to improve the -tion technologies are frustrated, introducing new challenges performance of threaded-based structure. Piumarta and for achieving high performance. Riccardi [4] describe their techniques to dynamically generate In response, more and more researchers have paid their threaded codes for purpose of eliminating a central dispatch site attention to optimize dynamic language compilers. The and inlining common bytecode sequences. Ertl and Gregg [5] technologies proposed aim to improve performance by extend Piumarta and Riccardi’s work by duplicating bytecode sequences, researching various interpreter generation heuristics, 1 Corresponding author. Email: [email protected] 978-83-60810-66-8/$25.00 c 2015, IEEE 435 436 PROCEEDINGS OF THE FEDCSIS. ŁOD´ Z,´ 2015 and concentrating on improving branch prediction accuracy. these compilers, CPython is the official and standard compiler Gagnon and Hendren [6] adapt Piumarta and Riccardi’s for Python language, it can support all the grammars and research to work in the context of multithreading and dynamic extensions. Others are developed for special applications. They class loading. Sullivan et al. [7] describe a combination focus on particular scenarios and take in-depth optimization between an interpreter implementation and a dynamic binary passes. In this context, these compilers show their excellent optimizer, which enhances the efﬁcacy of the underlying performances for some benchmarks, but for other benchmarks, dynamic binary optimizer during the execution of interpreter. they may be several times slower than CPython. As shown on Despite the enhancements above, interpreters are still worse Table I, taking fid.py for example, it takes CPython 3.696 than static compilers from the runtime perspective. The purpose seconds to execute this script, while IronPython, Pyston and of our research is to explore new ways to achieve further PyPy spend less than 2 seconds on the same script. However, it performance improvement. In this paper, the performance takes CPython 1.082 seconds to execute emwomding.py, while research on CPython’s latest interpreter is presented, finding JPython, IronPython, Pyston and PyPy spend more than 3.700 that bytecode dispatching takes about 25 percent of total seconds on the execution of emwomding.py. execution time on average. Then, a novel bytecode dispatching What’s more, some benchmarks, like pydigits.py, empth_lo mechanism, which aims at reducing memory reading -op.py, vecf_add.py, nq.py, raytrace.py, cannot be executed operations during bytecode dispatching and reducing the time successfully by some of these compilers. Based on these spent on this phase to a minimum, is proposed. This mechanism comparisons, our research focuses on CPython, and has not is implemented inside CPython’s interpreter. Experiments on only practical application value, but also instruction meaning lots of benchmarks demonstrate the correctness and efficiency for the improvement of other interpreters. of our new mechanism. The comparisons between original B. Switch-based Mechanism VS Thread-based Mechanism CPython and optimized CPython show that our new mechanism can achieve about 8.5 percent performance The performance of interpreters depends heavily on their improvement on average. For some particular benchmarks, the bytecode dispatching mechanisms. CPython’s interpreter maximum improvement is up to 18 percentages. provides two main bytecode dispatching mechanisms: The remainder of this paper is structured as follows: In the switch-based mechanism and thread-based mechanism. next section, we make comparisons among different compilers The inner loop of switch-based interpreters is quite simple: of Python and present both switch-based and threaded-based jump to the dispatch point, fetch the next bytecode and dispatch mechanisms. Section 3 reports our performance research on to its implementation through a switch statement. Its typical CPython’s interpreter. Section 4 discusses the main techniques framework is shown in Fig. 1. we adopted to construct a new bytecode dispatching As shown in Fig. 1, the interpreter is an infinite loop with a mechanism. Benchmarks and Experiments are given in section big switch block to dispatch bytecodes successively. Each 5. Section 6 discusses the related work. We conclude this paper bytecode are implemented by a particular case in the body of in the last section. this switch block. At the end of each case, control are passed back to the beginning of the infinite loop by breaking out of this II. BACKGROUND switch block. Traditional C compilers like GCC translate this switch block into a series of comparison statements. A. CPython VS Other Python compilers Considering a particular bytecode whose opcode is A series of dynamic compilers are designed to run Python BINARY_SUBTRACT, five indispensable comparisons programs, such as CPython, Jython [8], IronPython [9], Pyston should be executed before reaching its associated block. [10], PyPy [11], etc. The comparisons

Load more