%0 Conference Paper %1 daSilva:2019:PSV %A da Silva, Anderson B. N. %A Cunha, Daniel A. M. %A Silva, Vitor R. G. %A de A. Furtunato, Alex F. %A Xavier-de Souza, Samuel %B Programming and Performance Visualization Tools %C Cham %D 2019 %E Bhatele, Abhinav %E Boehme, David %E Levine, Joshua A. %E Malony, Allen D. %E Schulz, Martin %I Springer International Publishing %K Loops Parallelization Visualization %P 250--264 %T PaScal Viewer: A Tool for the Visualization of Parallel Scalability Trends %X Taking advantage of the growing number of cores in supercomputers to increase the scalability of parallel programs is an increasing challenge. Many advanced profiling tools have been developed to assist programmers in the process of analyzing data related to the execution of their program. Programmers can act upon the information generated by these data and make their programs reach higher performance levels. However, the information provided by profiling tools is generally designed to optimize the program for a specific execution environment, with a target number of cores and a target problem size. A code optimization driven towards scalability rather than specific performance requires the analysis of many distinct execution environments instead of details about a single environment. With the goal of providing more useful information for the analysis and optimization of code for parallel scalability, this work introduces the PaScal Viewer tool. It presents an novel and productive way to visualize scalability trends of parallel programs. It consists of four diagrams that offers visual support to identify parallel efficiency trends of the whole program, or parts of it, when running on scaling parallel environments with scaling problem sizes. %@ 978-3-030-17872-7

@inproceedings{daSilva:2019:PSV, abstract = {Taking advantage of the growing number of cores in supercomputers to increase the scalability of parallel programs is an increasing challenge. Many advanced profiling tools have been developed to assist programmers in the process of analyzing data related to the execution of their program. Programmers can act upon the information generated by these data and make their programs reach higher performance levels. However, the information provided by profiling tools is generally designed to optimize the program for a specific execution environment, with a target number of cores and a target problem size. A code optimization driven towards scalability rather than specific performance requires the analysis of many distinct execution environments instead of details about a single environment. With the goal of providing more useful information for the analysis and optimization of code for parallel scalability, this work introduces the PaScal Viewer tool. It presents an novel and productive way to visualize scalability trends of parallel programs. It consists of four diagrams that offers visual support to identify parallel efficiency trends of the whole program, or parts of it, when running on scaling parallel environments with scaling problem sizes.}, added-at = {2019-08-16T21:59:15.000+0200}, address = {Cham}, author = {da Silva, Anderson B. N. and Cunha, Daniel A. M. and Silva, Vitor R. G. and de A. Furtunato, Alex F. and Xavier-de Souza, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/2c7b05a207728e7a72d532a0105ac789c/gron}, booktitle = {Programming and Performance Visualization Tools}, description = {PaScal Viewer: A Tool for the Visualization of Parallel Scalability Trends | SpringerLink}, editor = {Bhatele, Abhinav and Boehme, David and Levine, Joshua A. and Malony, Allen D. and Schulz, Martin}, interhash = {fa507d6d9ff9f972a0b5689bd83c523e}, intrahash = {c7b05a207728e7a72d532a0105ac789c}, isbn = {978-3-030-17872-7}, keywords = {Loops Parallelization Visualization}, pages = {250--264}, publisher = {Springer International Publishing}, timestamp = {2019-08-16T21:59:15.000+0200}, title = {{PaScal Viewer: A Tool for the Visualization of Parallel Scalability Trends}}, year = 2019 }