Different high performance techniques, such as profiling, tracing, and instrumentation, have been used to tune and enhance the performance of parallel applications. However, these techniques do not show how to explore the potential of parallelism in a given application. Animating and visualizing the execution process of a sequential algorithm provide a thorough understanding of its usage and functionality. In this work, an interactive web-based educational animation tool was developed to assist users in analyzing sequential algorithms to detect parallel regions regardless of the used parallel programming model. The tool simplifies algorithms’ learning, and helps students to analyze programs efficiently. Our statistical -test study on a sample of students showed a significant improvement in their perception of the mechanism and parallelism of applications and an increase in their willingness to learn algorithms and parallel programming.
AreiasC.M.MendesA.J. and GomesA.J., Learning to program with ProGuide, in: International Conference on Engineering Education, Coimbra, Portugal, 2007, pp. 1–6.
2.
RößlingG., A family of tools for supporting the learning of programming, Algorithms3 (2010), 168–182.
3.
NapsT.L.RößlingG.AlmstrumV.DannW.FleischerR.HundhausenC.KorhonenA.MalmiL.McNallyM. and RodgerS., Exploring the role of visualization and engagement in computer science education, in: Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education, Aarhus, Denmark, 2002, pp. 131–152.
4.
HundhausenC.D.DouglasS.A. and StaskoJ.T., A meta-study of algorithm visualization effectiveness, Journal of Visual Languages & Computing13 (2002), 259–290.
5.
ShendeS.S. and MalonyA.D., The TAU parallel performance system, The International Journal of High Performance Computing Applications20 (2006), 287–311.
6.
NethercoteN. and SewardJ., Valgrind: A framework for heavyweight dynamic binary instrumentation, ACM Sigplan notices42 (2007), 89–100.
7.
NapsT.L. and ChanE.E., Using visualization to teach parallel algorithms, in: The Proceedings of the Thirtieth SIGCSE Technical Symposium on Computer Science Education, New Orleans Louisiana, USA, 1999, pp. 232–236.
8.
VitaliyM.MohamadZ.J.NikolayK.VladimirL. and VladislavaM., Modeloo – the tool for teaching parallel computations, Advanced Science Letters21 (2015), 2243–2246.
9.
RantakokkoJ., Algorithm visualization through animation and role plays, in: Program Visualization Workshop, Coventry, UK, 2004, pp. 76–81.
10.
DesaiS.KulkarniS.VasantV.VaralakshmiM. and MohamedP., VPMM: Visualization of parallel matrix multiplication algorithms, Journal of Computing Sciences in Colleges33 (2017), 24–31.
11.
GrossmanM.AzizM.ChiH.TibrewalA.ImamS. and SarkarV., Pedagogy and tools for teaching parallel computing at the sophomore undergraduate level, Journal of Parallel and Distributed Computing105 (2017), 18–30.
University of San Francisco, Sorting visualization, Comparison sorting algorithm, Accessed: 13-11-2016.
14.
Steven ZeilJ., AlgAE (Algorithm Animation Engine), (2011), Accessed: 13-11-2016.
15.
MarcelinoM.MihaylovT. and MendesA., H-SICAS, a handheld algorithm animation and simulation tool to support initial programming learning, in: 38th Annual Frontiers in Education Conference, Saratoga Springs, NY, USA, 2008, T4A-7.
16.
KrushkovH.KrushkovaM.AtanasovV. and KrushkovaM., A computer-based tutoring system for programming, in: Proceedings of the Thirty Ninth Spring Conference of the Union of Bulgarian Mathematicians, Albena, Bulgaria, 2010, pp. 354–358.
TuparovG.TuparovaD. and JordanovV., Teaching sorting and searching algorithms through simulation-based learning objects in an introductory programming course, Procedia-Social and Behavioral Sciences116 (2014), 2962–2966.
20.
TuparovG.TuparovaD. and TsarnakovaA., Using interactive simulation-based learning objects in introductory course of programming, Procedia-Social and Behavioral Sciences46 (2012), 2276–2280.
21.
MalmiL.KaravirtaV.KorhonenA.NikanderJ.SeppäläO. and SilvastiP., Visual algorithm simulation exercise system with automatic assessment: TRAKLA2, Informatics in Education3 (2004), 267–267.
22.
QawasmehA.MalikA.ChapmanB.HuckK. and MalonyA., Open source task profiling by extending the openmp runtime api, in: 9th International Workshop on OpenMP, Canberra, Australia, 2013, pp. 186–199.
23.
QawasmehA.MalikA.M. and ChapmanB.M., OpenMP task scheduling analysis via OpenMP runtime API and tool visualization, in: IPDPSW, Phoenix, AZ, USA, 2014, pp. 1049–1058.
24.
KimT.K., T test as a parametric statistic, Korean Journal of Anesthesiology68 (2015), 540–540.
25.
DannerA.NewhallT. and WebbK.C., Paravis: A library for visualizing and debugging parallel applications, in: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, 2019, pp. 326–333.
