ECOOP 2018
Sun 15 - Sat 21 July 2018 Amsterdam, Netherlands
co-located with ECOOP and ISSTA 2018
Fri 20 Jul 2018 13:50 - 14:15 at Zurich II - Runtime Systems Chair(s): Christian Hammer

Scientific applications are ideal candidates for the “heterogeneous computing” paradigm, in which parts of a computation are “offloaded” to available accelerator hardware such as GPUs. However, when such applications are written in dynamic languages such as Python or R, as they increasingly are, things become less straightforward. The same flexibility that makes these lan- guages so appealing to programmers also significantly complicates the problem of automatically and transparently partitioning a program’s execution between a CPU and available accelerator hardware without having to rely on programmer annotations. A common way of handling the features of dynamic languages is by introducing speculation in conjunction with guards to ascertain the validity of assumptions made in the speculative computation. Unfortunately a single guard violation during the execution of “offloaded” code may result in a huge performance penalty and necessitate the complete re-execution of the offloaded computation. In the case of dynamic languages, this problem is compounded by the fact that a full compiler analysis is not always possible ahead of time.

This paper presents MegaGuards, a new approach for speculatively executing dynamic languages on heterogeneous platforms in a fully automatic and transparent manner. Our method translates each target loop into a single static region devoid of any dynamic features. The dynamic parts are instead handled by a construct that we call a mega guard which checks all of the speculative assumptions ahead of its corresponding static region. Notably, the advantage of MegaGuards is not limited to heterogeneous computing; because it removes guards from compute-intensive loops, the approach also improves sequential performance.

We have implemented MegaGuards along with an automatic loop parallelization backend in ZipPy, a Python Virtual Machine. The results of a careful and detailed evaluation reveal very significant speedups of more than 34x on average with a maximum speedup of up to 175x when compared to the original ZipPy performance as a baseline. These results demonstrate the potential for applying heterogeneous computing to dynamic languages.

Fri 20 Jul
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13:50 - 15:30: ECOOP Research Papers - Runtime Systems at Zurich II
Chair(s): Christian HammerUniversity of Potsdam
ecoop-2018-papers13:50 - 14:15
Research paper
Mohaned QunaibitUniversity of California, Irvine, Stefan BrunthalerBundeswehr University Munich, Yeoul Na, Stijn VolckaertUniversity of California, Irvine, Michael FranzUniversity of California, Irvine
ecoop-2018-papers14:15 - 14:40
Research paper
Jonathan BellGeorge Mason University, Luís PinaGeorge Mason University
DOI Pre-print Media Attached
ecoop-2018-papers14:40 - 15:05
Research paper
Julien Gascon-Samson, Kumseok JungUniversity of British Columbia, Shivanshu GoyalUniversity of British Columbia, Armin Rezaiean-AselUniversity of British Columbia, Karthik PattabiramanUniversity of British Columbia
ecoop-2018-papers15:05 - 15:30
Research paper
Tianxiao Gu, Xiaoxing MaNanjing University, Chang XuNanjing University, Yanyan JiangNanjing University, Chun CaoNanjing University, Jian LuNanjing University