Educational and Research Systems for Evaluating the Efficiency of Parallel Computations

In this paper we consider the educational and research systems that can be used to estimate the efficiency of parallel computing. ParaLab allows parallel computation methods to be studies. With the ParaLib library, we can compare the parallel programming languages and technologies. The Globalizer Lab system is capable of estimating the efficiency of algorithms for solving computationally intensive global optimization problems. These systems can build models of various high-performance systems, formulate the problems to be solved, perform computational experiments in the simulation mode and analyze the results. The crucial matter is that the described systems support a visual representation of the parallel computation process. If combined, these systems can be useful for developing high-performance parallel programs which take the specific features of modern supercomputing systems into account.

[1]  Eileen Kraemer,et al.  The Visualization of Parallel Systems: An Overview , 1993, J. Parallel Distributed Comput..

[2]  Jaime Urquiza-Fuentes,et al.  Toward the effective use of educational program animations: The roles of student's engagement and topic complexity , 2013, Comput. Educ..

[3]  Y. D. Sergeyev,et al.  Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications) , 2000 .

[4]  Victor P. Gergel,et al.  Challenges of a Systematic Approach to Parallel Computing and Supercomputing Education , 2015, Euro-Par Workshops.

[5]  Victor P. Gergel,et al.  NSF/IEEE-TCPP Curriculum Implementation at the State University of Nizhni Novgorod , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

[6]  V. P. Gergel,et al.  A method of using derivatives in the minimization of multiextremum functions , 1996 .

[7]  Anthony Skjellum,et al.  Using MPI - portable parallel programming with the message-parsing interface , 1994 .

[8]  Zbigniew J. Czech,et al.  Introduction to Parallel Computing , 2017 .

[9]  John T. Stasko,et al.  Please address correspondence to , 2000 .

[10]  E. Kozinov,et al.  Learning Parallel Computations with ParaLab , 2015 .

[11]  Victor Gergel,et al.  Heterogeneous Parallel Computations for Solving Global Optimization Problems1 , 2015 .

[12]  Vladimir A. Grishagin,et al.  Local Tuning in Nested Scheme of Global Optimization , 2015, ICCS.

[13]  Victor P. Gergel,et al.  The ParaLab System for Investigating the Parallel Algorithms , 2010, MTPP.

[14]  Lauri Malmi,et al.  A Review of Generic Program Visualization Systems for Introductory Programming Education , 2013, TOCE.

[15]  Yury Kochetov,et al.  Special Issue: XVI Baikal International School-Seminar <> , 2016, J. Glob. Optim..

[16]  Rohit Chandra,et al.  Parallel programming in openMP , 2000 .

[17]  Erkki Sutinen,et al.  A decade of research and development on program animation: The Jeliot experience , 2011, J. Vis. Lang. Comput..

[18]  Victor P. Gergel,et al.  Parallel global optimization on GPU , 2016, Journal of Global Optimization.

[19]  Angelo Sifaleras,et al.  An empirical study on factors influencing the effectiveness of algorithm visualization , 2013, Comput. Appl. Eng. Educ..

[20]  Leonie Kohl,et al.  Parallel Programming In C With Mpi And Open Mp , 2016 .