=Paper=
{{Paper
|id=Vol-1482/536
|storemode=property
|title=Automated parallelization of sequential C-programs on the example of two applications from the field of laser material processing
|pdfUrl=https://ceur-ws.org/Vol-1482/536.pdf
|volume=Vol-1482
}}
==Automated parallelization of sequential C-programs on the example of two applications from the field of laser material processing==
<pdf width="1500px">https://ceur-ws.org/Vol-1482/536.pdf</pdf>
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    Суперкомпьютерные дни в России 2015 // Russian Supercomputing Days 2015 // RussianSCDays.org



     Automated parallelization of sequential C-programs on the
     example of two applications from the field of laser material
                             processing
                  M.S. Baranov1,2, D.I. Ivanov1,2, N.A. Kataev1, A.A. Smirnov
    Keldysh Institute of Applied Mathematics Russian Academy of Sciences1, Lomonosov
                                  Moscow State University2

     Optimization and parallelization of programs suppose an execution of a sequence analysis and
transform passes. The choice of the optimal sequence depends on the application, on the purpose of
optimization, on the architecture of the target computer system and technologies used for parallel
programming. A huge size of space of possible optimization sequences complicates the automatic
search for the best sequence for a certain program. Although manual parallelization takes into
consideration these peculiarities, it is still a complicated and time-consuming process.
     The approach proposed in this article involves an automatic execution of individual passes in the
order specified by the user. Semi-automatic tools for transformation and analysis were developed.
     Several types of static analysis can be performed: data dependence analysis with the detection of
the dependence vector, privatizable variables analysis, induction and reduction variables recognition.
     Each transform pass consists of a set of basic transformations selected by the user: variable
propagation, loop-invariant code motion, loop unrolling, loop distribution, loop swapping, loop
merging, loop permutation, iteration space shifting. The basic transformations are specified in the
source code of the program as directives which are specified by using #pragma mechanism provided
by the C standard. Transformations are performed over nests of perfectly nested loops. In some cases a
transformation of arbitrary code blocks is allowed. There are two types of transformations: safe and
unsafe ones. Before executing safe transformations their permissibility is checked by the mentioned
transformation tool, in the case of unsafe transformations the responsibility for them lies on the user.
     The proposed approach was applied for semi-automatic parallelization of two applications for
laser material processing. After semi-automatic analysis and transformation these two programs were
successfully manually parallelized using parallel programming technologies OpenMP and OpenACC.
Table 1 shows the time of execution of three-dimensional problem in seconds on 4-cores processor
Intel Core i7-3770 CPU 3.40GHz with active Hyper Threading and graphics accelerator NVIDIA
GTX Titan. All versions of the program were compiled with option -O3. The original and transformed
programs were also parallelized in automatic way using Intel compiler with option -parallel.
       Table 1: Execution time (in seconds) of the programs Powder 3D (100x100x100, 100 iterations)
               Original     Transformed         Auto             Auto          OpenMP        OpenACC
                                              (original)    (transformed)
 1 thread       50.59           19.77                                             22.81         92.53
 8 threads                                      62.38            11.61            11.30
 1 GPU                                                                                          19.46
     We plan to introduce the developed tools into the system for automated parallelization
SAPFOR [1] in order to achieve a serial implementation of the parallelized program which may be
efficiently mapped on modern clusters by the automatic parallelization compiler included in the
system.

References
1. Bahtin V.A., Borodich I.G., Kataev N.A., Klinov M.S., Kovaleva N.V., Krukov V.A.,
   Podderugina N.V. Dialog s programmistom v sisteme avtomatizacii rasparallelivanija SAPFOR
   [Dialogue with a programmer in the automatic parallelization environment SAPFOR]. Vestnik
   Nizhegorodskogo universiteta im. N.I. Lobachevskogo [Vestnik of Lobachevsky State University
   of Nizhni Novgorod]. 2012 No. 5 (2). P. 242–245.


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