scholarly journals Automatic Parallelization and Optimization of Programs by Proof Rewriting

2009 ◽  
pp. 52-68 ◽  
Author(s):  
Clément Hurlin
Keyword(s):  
Automatic Parallelization

Evaluation of automatic parallelization algorithms to minimize speculative parallelism overheads: An experiment

2021 ◽  
Vol 24 (5) ◽  
pp. 1517-1528
Author(s):  
Sudhakar Kumar ◽  
Sunil Kr. Singh ◽  
Kriti Aggarwal ◽  
Naveen Aggarwal
Keyword(s):  
Automatic Parallelization ◽  
Speculative Parallelism

Performance Evaluation of an OpenMP Parallelization by Using Automatic Parallelization Information

2014 ◽  
pp. 119-126
Author(s):  
Kazuhiko Komatsu ◽  
Ryusuke Egawa ◽  
Hiroyuki Takizawa ◽  
Hiroaki Kobayashi
Keyword(s):  
Performance Evaluation ◽  
Automatic Parallelization

scholarly journals Investigation of Data Dependencies by Dynamic Analysis of Sapfor

2020 ◽  
Vol 23 (3) ◽  
pp. 473-493
Author(s):  
Nikita Andreevich Kataev ◽  
Alexander Andreevich Smirnov ◽  
Andrey Dmitrievich Zhukov
Keyword(s):  
Dynamic Analysis ◽  
Programming Languages ◽  
Static Analysis ◽  
Automatic Parallelization ◽  
Parallel Execution ◽  
Control Flow ◽  
Analysis Tool ◽  
Data Dependencies ◽  
Program Parallelization ◽  
Memory Accesses

The use of pointers and indirect memory accesses in the program, as well as the complex control flow are some of the main weaknesses of the static analysis of programs. The program properties investigated by this analysis are too conservative to accurately describe program behavior and hence they prevent parallel execution of the program. The application of dynamic analysis allows us to expand the capabilities of semi-automatic parallelization. In the SAPFOR system (System FOR Automated Parallelization), a dynamic analysis tool has been implemented, based on on the instrumentation of the LLVM representation of an analyzed program, which allows the system to explore programs in both C and Fortran programming languages. The capabilities of the static analysis implemented in SAPFOR are used to reduce the overhead program execution, while maintaining the completeness of the analysis. The use of static analysis allows to reduce the number of analyzed memory accesses and to ignore scalar variables, which can be explored in a static way. The developed tool was tested on performance tests from the NAS Parallel Benchmarks package for C and Fortran languages. The implementation of dynamic analysis, in addition to traditional types of data dependencies (flow, anit, output), allows us to determine privitizable variables and a possibility of pipeline execution of loops. Together with the capabilities of DVM and OpenMP these greatly facilitates program parallelization and simplify insertion of the appropriate compiler directives.

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