An ARM Backend for PyPyls Tracing JIT - STUPS Group

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2 CONTENTS6.4.3 Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.4.4 Memory Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.4.5 Calls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.4.6 Forcing the Frame Object . . . . . . . . . . . . . . . . . . . . . . . . 336.5 Guards and Leaving the Loop . . . . . . . . . . . . . . . . . . . . . . . . . . 346.6 Bridges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Cross Translation 368 Evaluation 388.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388.2 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388.2.1 Python Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 398.2.2 Prolog Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Related Work 4910 Conclusion and Future Work 5011 Annex 51References 53List of Figures 57List of Tables 58
31 IntroductionARM cores are present in most of the devices we use day to day, be it a phone, a tabletor a music player. Additionally ARM based CPUs are beginning to be used in netbooksand energy efficient servers. The large scale deployment of ARM cores and the varietyof devices they power makes ARM an increasingly interesting platform to be targeted bydevelopers.At the same time there is a growing interest in dynamic programming languages and anincrease in their usage due to improvements in the implementations of the languages andthe increased computational power of computers. These developments make it more andmore feasible to develop software from small scripts to large scale server systems usingdynamic languages.ARM based devices are usually small, energy efficient and portable devices where executionspeed and power consumption are critical concerns when creating software.To bridge the gap between the high level expressiveness and ease of development of dynamiclanguages with the restricted resources found on small systems we want to createa backend for PyPy’s tracing JIT [PyP11b]. Based on the powerful toolchain provided byPyPy, we are able provide a way to create interpreters for different dynamic languages.These interpreters are able to, besides the platforms already targeted by PyPy, also run onARM based systems. At the same time these interpreters may benefit from the optimizationsprovided by just-in-time compilation as done by PyPy. This will make it possibleto write more and faster software for mobile devices using high-level languages such asPython.PyPy is the tool of choice to bring dynamic languages and their dynamic compilationto ARM, because it is a general toolchain for writing dynamic languages that alreadyhas a very compliant Python 2.7 implementation as well as a Prolog implementation andseveral experimental language implementations. Due to its modular architecture PyPyallows all language implementations to potentially benefit from and use the new featureof an ARM backend for the JIT. PyPy has a powerful JIT compiler generator providingvery good results on x86. We hope to be able to bring these good results to ARM.In this thesis we present an ARM backend for PyPy’s tracing JIT compiler. After givingan introduction into the ARM architecture and into just-int-time compilation, we willdescribe how this works on PyPy. Based on this foundation we will describe how wetranslate the operations PyPy’s JIT to machine code in a way that it can be efficientlyexecuted on ARM based devices. Additionally we present an new translation platformfor PyPy to cross-translate programs written in RPython to target ARM.The rest of this thesis is structured as follows: in Section 2 we present the ARM architectureand the company behind it, describing the different features that ARM provides,focussing especially on those that are central to our goal of creating a backend for PyPy’sJIT. In Section 3 we present a general introduction to just-in-time compilation and differenttechniques which have been developed to improve the execution speed of high levellanguages. In Section 4 we are going to focus on one particular approach in developingdynamic languages presenting the PyPy project and some of its core ideas. In Section 5we will describe PyPy’s approach to just-in-time compilation. Based on these concepts
Page 1: INSTITUT FÜR INFORMATIKSoftwaretec
Page 5: AbstractA large part of the computi
Page 11 and 12: 52 ARMARM is at the same time the n
Page 13 and 14: 2.2 The ARM Architecture 7The curre
Page 15 and 16: 2.2 The ARM Architecture 9and from
Page 17 and 18: 113 Just-in-Time CompilationJust-in
Page 19 and 20: 3.2 Trace Based Just-In-Time Compil
Page 21 and 22: 154 PyPyThe PyPy project was starte
Page 23 and 24: 175 PyPy’s Approach to Tracing JI
Page 25 and 26: 5.1 The Shape of a Trace 19program
Page 27 and 28: 5.2 Optimizations 21loop_start(a0,
Page 29 and 30: 236 A Backend for PyPy’s JITIn th
Page 31 and 32: 6.1 Low level Code Generation Inter
Page 33 and 34: 6.2 Register Allocation 27def gen_l
Page 35 and 36: 6.3 Setup to Execute a Compiled Loo
Page 37 and 38: 6.4 Compiling Trace Operations 31Ac
Page 39 and 40: 6.4 Compiling Trace Operations 33of
Page 41 and 42: 6.5 Guards and Leaving the Loop 35o
Page 43 and 44: 37ARM using a cross compiler, such
Page 45 and 46: 8.2 Benchmarks 398.2.1 Python Bench
Page 47 and 48: 8.2 Benchmarks 41Benchmark cpython
Page 49 and 50: 8.2 Benchmarks 43Ratio6x5x4x3x2x1x0
Page 51 and 52: 8.2 Benchmarks 458.2.2 Prolog Bench
Page 53 and 54: 8.2 Benchmarks 47SWInojit, boehmjit
Page 55 and 56: 499 Related WorkThere are many virt
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5111 AnnexBenchmark cpython [ms] no
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REFERENCES 53References[AACM07] ANC
Page 61 and 62:
REFERENCES 55annual ACM SIGPLAN con
Page 63 and 64:
LIST OF FIGURES 57List of Figures1
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An ARM Backend for PyPyls Tracing JIT - STUPS Group

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