471,217 research outputs found
A Multimedia Interactive Environment Using Program Archetypes: Divide-and-Conquer
As networks and distributed systems that can exploit parallel computing become more widespread, the need for ways to teach parallel programming effectively grows as well. Even though many colleges and universities provide courses on parallel programming [1], most of those courses are reserved for graduate students and advanced undergraduates. There is a demand for ways to teach fundamental parallel programming concepts to people with just a working knowledge of programming. By using the idea of a software archetype, and providing a learning environment that teaches both concept and coding, we hope to satisfy this need. This paper presents an overview of the multimedia approach we took in teaching parallel programming and offers Divide-and-Conquer as an example of its use
Teaching Parallel Programming Using Java
This paper presents an overview of the "Applied Parallel Computing" course
taught to final year Software Engineering undergraduate students in Spring 2014
at NUST, Pakistan. The main objective of the course was to introduce practical
parallel programming tools and techniques for shared and distributed memory
concurrent systems. A unique aspect of the course was that Java was used as the
principle programming language. The course was divided into three sections. The
first section covered parallel programming techniques for shared memory systems
that include multicore and Symmetric Multi-Processor (SMP) systems. In this
section, Java threads was taught as a viable programming API for such systems.
The second section was dedicated to parallel programming tools meant for
distributed memory systems including clusters and network of computers. We used
MPJ Express-a Java MPI library-for conducting programming assignments and lab
work for this section. The third and the final section covered advanced topics
including the MapReduce programming model using Hadoop and the General Purpose
Computing on Graphics Processing Units (GPGPU).Comment: 8 Pages, 6 figures, MPJ Express, MPI Java, Teaching Parallel
Programmin
Exploiting parallelism in coalgebraic logic programming
We present a parallel implementation of Coalgebraic Logic Programming (CoALP)
in the programming language Go. CoALP was initially introduced to reflect
coalgebraic semantics of logic programming, with coalgebraic derivation
algorithm featuring both corecursion and parallelism. Here, we discuss how the
coalgebraic semantics influenced our parallel implementation of logic
programming
The JStar language philosophy
This paper introduces the JStar parallel programming language, which is a Java-based declarative language aimed at discouraging sequential programming, en-couraging massively parallel programming, and giving the compiler and runtime maximum freedom to try alternative parallelisation strategies. We describe the execution semantics and runtime support of the language, several optimisations and parallelism strategies, with some benchmark results
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
While modern parallel computing systems provide high performance resources,
utilizing them to the highest extent requires advanced programming expertise.
Programming for parallel computing systems is much more difficult than
programming for sequential systems. OpenMP is an extension of C++ programming
language that enables to express parallelism using compiler directives. While
OpenMP alleviates parallel programming by reducing the lines of code that the
programmer needs to write, deciding how and when to use these compiler
directives is up to the programmer. Novice programmers may make mistakes that
may lead to performance degradation or unexpected program behavior. Cognitive
computing has shown impressive results in various domains, such as health or
marketing. In this paper, we describe the use of IBM Watson cognitive system
for education of novice parallel programmers. Using the dialogue service of the
IBM Watson we have developed a solution that assists the programmer in avoiding
common OpenMP mistakes. To evaluate our approach we have conducted a survey
with a number of novice parallel programmers at the Linnaeus University, and
obtained encouraging results with respect to usefulness of our approach
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