Teaching Parallel and Distributed Computing to Undergraduate Computer Science Students EduPar 2013

Transcription

1 Teaching Parallel and Distributed Computing to Undergraduate Computer Science Students EduPar 2013 Dpto. de Computación - FCEFQyN - Universidad Nacional de Río Cuarto May, 2013

2 Curriculum overview Degree on Computer Science Duration: Five years (common in Argentina) Focus: Computer science concepts and programming Areas (in core): Mathematics Programming Software engineering and databases Programming languages and paradigms Theory of computation Architecture and computer systems: (computer organization, simulation, operating systems, networking and distributed systems) Set of elective courses (formal development and verification, numerical methods, parallel programming,... )

3 PDC topics Areas and PDC topics covered Area Topics Computer arch. Flynn taxonomy, cache, pipeline,... Programming Functional programming, implicit paralellism,... DSyA (I, II) DyQ, dynamic programming, OO,... Prog. lang. Shared mem. and message passing paradigms models, data races, deadlock Software eng. Patterns (for concurrency) Networking Message passing model, and DS Distributed algorithms,... Compilers Optimizations, directive based par. Theory Comp. models, complexity

4 PDC topics (cont.) PDC topics in some elective courses Concurrency and Parallel Programming: it is an advanced (5th year) course with specific PDC contents: Parallel computers Programming models and tools (threads, OpenMP, MPI,... ) Performance measures Decomposition techniques Parallel patterns and skeletons Software validation and verification: study of formal methods, techniques and tools Reasoning on concurrency Modeling of concurrent systems Race conditions and deadlocks detection These courses are in core in the new proposed curriculum

5 Teaching PDC Motivation The course Concurrency and Parallelism is taken by (advanced) CS students and other areas (mathematics, biologists, engineers,... ). We have an heterogeneus classroom

6 Teaching PDC Motivation The course Concurrency and Parallelism is taken by (advanced) CS students and other areas (mathematics, biologists, engineers,... ). We have an heterogeneus classroom Teaching Approach Top down approach Using parallel skeletons (C++ templates) Some data parallel skeletons map A(f, [x 1,..., x n]) = [f(x 1),..., f(x n)] reduce T (f,,, x 1(x 2, x 3)) = f(x 1) (f(x 2) f(x 3)) Some architectural skeletons pipeline(is, os, se 1, se 2,..., se n) workers(is, os, se, N)

7 Using skeletons Advantage Hide a lot of programming details Focus on problem solving Algorithms implemented in a compositional approach Students can write and run complex parallel programs from the very begining Skeletons provide the basis for performance analysis Programs are written in a functional style

8 Using skeletons Advantage Hide a lot of programming details Focus on problem solving Algorithms implemented in a compositional approach Students can write and run complex parallel programs from the very begining Skeletons provide the basis for performance analysis Programs are written in a functional style Projects CS students are encouraged to analyze/extend skeletons and develop new abstractions/targets and deal with details Non CS students are able to develop programs quickly and put emphasis on achieving performance

9 Thanks Questions?