@Article{MiranaStep:2021:CoHPAp,
               author = "Mirana, Eduardo Furlan and Stephany, Stephan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Common HPC Approaches in Python Evaluated for aScientific 
                         Computing Test Case",
              journal = "Revista Cereus",
                 year = "2021",
               volume = "13",
               number = "2",
                pages = "84--98",
             keywords = "High performance computing, Python language, Finite-difference 
                         method, Scientific computing, Performance comparison.",
             abstract = "A number of the most common high-performance computing approaches 
                         available in the Python programming environment of the LNCC Santos 
                         Dumont supercomputer, are compared using a specific test case. 
                         Python includes specific libraries, development tools, 
                         implementations, documentation and optimizing/parallelizing 
                         resources. It provides a straightforward way to program in a high 
                         level of abstraction, but parallelization resources to exploit 
                         multiple cores, processors or accelerators like GPUs are diverse 
                         and may be not easily selectable by the programmer. This work 
                         makes a comparison of common approaches in Python to boost 
                         computing performance. The test case is a well-known 2D heat 
                         transmission problem modeled by the Poisson partialdifferential 
                         equation, which is solved by a finite difference method that 
                         requires the calculation of a 5-point stencil over the domain 
                         grid. Their serial and parallel implementations in Fortran 90 were 
                         taken as references in order to compare their performance to some 
                         serial and parallel Python implementations of the same algorithm. 
                         Besides performance results, a discussion about the trade-off 
                         between easiness of programming versus processing performance is 
                         included. This work is a primer for the use of HPC resources in 
                         Python.",
                  doi = "10.18605/2175-7275/cereus.v13n2p84-98",
                  url = "http://dx.doi.org/10.18605/2175-7275/cereus.v13n2p84-98",
                 issn = "2175-7275",
             language = "en",
           targetfile = "miranda_common.pdf",
        urlaccessdate = "11 jun. 2024"
}