An ARM Backend for PyPyls Tracing JIT - STUPS Group

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44 8 EVALUATIONRatio6x5x4x3x2x1x0xaibm_makochaoscrypto_pyaesdjangofannkuchfloatgohtml5libmeteor−contestcpythonnojit, boehmjit, boehmnojit, gcjit, gcnbody_modifiedraytrace−simplerichardsslowspitfirespectral−normspitfire_cstringiotelcotwisted_iterationtwisted_namestwisted_pbtwisted_tcpwafBenchmarkFigure 25: PyPy benchmarks run on x86, normalized to CPython
8.2 Benchmarks 458.2.2 Prolog BenchmarksTo see how the ARM backend performs when the JIT is applied to an interpreter that isnot based on a bytecode dispatch loop we used the Pyrolog interpreter and the benchmarksdescribed in [BLS10]. Pyrolog is a Prolog interpreter written in RPython that canbe translated using the PyPy toolchain. On ARM we created two versions and comparedthem, as for Python, to the same interpreter translated in different versions for x86. ForARM we created one version with and one version without the JIT, both using the BoehmGC. On x86 we created the same set of versions as we did for Python. On both platformswe compared the generated versions of Pyrolog to SWI-Prolog 12 which is available forUbuntu 10.10 on ARM and x86 in version 5.8.2. For comparability the benchmarks wererun the same way as described in [BLS10], running each benchmark twice in the sameprocess to give the JIT time to warm up and generate code and measuring the secondrun.Benchmark no jit, boehm jit, boehm SWIiterate_assert 1270 ms 10 ms 110 msiterate_call 3060 ms 10 ms 3190 msiterate_cut 2690 ms 360 ms 70 msiterate_exception 3140 ms 350 ms 4990 msiterate_failure 2780 ms 130 ms 430 msiterate_findall 4190 ms – 740 msiterate_if 7410 ms 10 ms 140 msiterate 1190 ms 10 ms 50 msTable 4: Absolute runtimes for Pyrolog iterate benchmarks run on ARMFrom the set of benchmarks we had to remove the arithmetic and reducer benchmarks,because they did not terminate in a reasonable amount of time on both platforms. Thisas well as the slower results compared to [BLS10] might be due to recent changes inthe optimizer of PyPy’s JIT that affect the Pyrolog interpreter negatively. Also we hadto adapt some of the benchmarks so they could run on ARM, reducing the number ofiterations they performed. The adapted benchmarks can be found in the arm branchof the pyrolog-benchmark repository 13 . All graphs of the results of running the Prologbenchmarks are presented in a logarithmic scale.Additionally on ARM we performed the iterate benchmarks which are rather atypicalProlog programs but show how a loop in form of recursive calls is optimized by the JIT,compared to SWI-Prolog. From this set of benchmarks the iterate_findall benchmarkcrashed when executed with the JIT, thus producing no result. Also worth noting,the bad performance of the iterate_cut which is due to the bad handling of the cutoperation by JIT. An in depth evaluation of these benchmarks has been done in [BLS10].The results for the iterate set of benchmarks, confirm the findings of [BLS10] that theJIT optimizes recursive calls and can optimize dynamic invocation particularly well alsogiving a speedup on ARM for the cases it does on x86. The results are shown in Table 412 http://www.swi-prolog.org/13 https://bitbucket.org/cfbolz/pyrolog-benchmark/changeset/arm
Page 1: INSTITUT FÜR INFORMATIKSoftwaretec
Page 5: AbstractA large part of the computi
Page 9 and 10: 31 IntroductionARM cores are presen
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: 8.2 Benchmarks 43Ratio6x5x4x3x2x1x0
Page 53 and 54: 8.2 Benchmarks 47SWInojit, boehmjit
Page 55 and 56: 499 Related WorkThere are many virt
Page 57 and 58: 5111 AnnexBenchmark cpython [ms] no
Page 59 and 60: 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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