TY - GEN
T1 - Language-Agnostic Optimization and Parallelization for Interpreted Languages
AU - Strout, Michelle Mills
AU - Debray, Saumya
AU - Isaacs, Kate
AU - Kreaseck, Barbara
AU - Cárdenas-Rodríguez, Julio
AU - Hurwitz, Bonnie
AU - Volk, Kat
AU - Badger, Sam
AU - Bartels, Jesse
AU - Bertolacci, Ian
AU - Devkota, Sabin
AU - Encinas, Anthony
AU - Gaska, Ben
AU - Neth, Brandon
AU - Sackos, Theo
AU - Stephens, Jon
AU - Willer, Sarah
AU - Yadegari, Babak
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Scientists are increasingly turning to interpreted languages, such as Python, Java, R, Matlab, and Perl, to implement their data analysis algorithms. While such languages permit rapid software development, their implementations often run into performance issues that slow down the scientific process. Source-level approaches for parallelization are problematic for two reasons: first, many of the language features common to these languages can be challenging for the kinds of analyses needed for parallelization; and second, even where such analysis is possible, a language-specific approach implies that each language would need its own parallelizing compiler and/or constructs, resulting in significant duplication of effort. The Science Up To Par project is investigating a radically different approach to this problem: automatic parallelization at the machine code level using trace information. The key to accomplishing this will be the static and dynamic analysis of executables and the reconstitution of such executables into parallel executables. The key insight is that with trace information it should be possible optimize out the interpreter and other dynamic features in a language-agnostic manner and create parallelized executables for multicore architectures. If successful, this can enable scientists to continue to develop in programming environments that most conveniently support their scientific exploration without paying the performance overheads currently associated with many such environments.
AB - Scientists are increasingly turning to interpreted languages, such as Python, Java, R, Matlab, and Perl, to implement their data analysis algorithms. While such languages permit rapid software development, their implementations often run into performance issues that slow down the scientific process. Source-level approaches for parallelization are problematic for two reasons: first, many of the language features common to these languages can be challenging for the kinds of analyses needed for parallelization; and second, even where such analysis is possible, a language-specific approach implies that each language would need its own parallelizing compiler and/or constructs, resulting in significant duplication of effort. The Science Up To Par project is investigating a radically different approach to this problem: automatic parallelization at the machine code level using trace information. The key to accomplishing this will be the static and dynamic analysis of executables and the reconstitution of such executables into parallel executables. The key insight is that with trace information it should be possible optimize out the interpreter and other dynamic features in a language-agnostic manner and create parallelized executables for multicore architectures. If successful, this can enable scientists to continue to develop in programming environments that most conveniently support their scientific exploration without paying the performance overheads currently associated with many such environments.
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U2 - 10.1007/978-3-030-35225-7_4
DO - 10.1007/978-3-030-35225-7_4
M3 - Conference contribution
AN - SCOPUS:85132571759
SN - 9783030352240
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 46
BT - Languages and Compilers for Parallel Computing - 30th International Workshop, LCPC 2017, Revised Selected Papers
A2 - Rauchwerger, Lawrence
PB - Springer Science and Business Media Deutschland GmbH
T2 - 30th Workshop on Languages and Compilers for Parallel Computing, LCPC 2017
Y2 - 11 October 2017 through 13 October 2017
ER -