3 research outputs found
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
An Embedded System for applying High Performance Computing in Educational Learning Activity
HPC (High Performance Computing) has become more popular in the last few years. With the benefits on high computational power, HPC has impact on industry, scientific research and educational activities. Implementing HPC as a curriculum in universities could be consuming a lot of resources because well-known HPC system are using Personal Computer or Server. By using PC as the practical moduls it is need great resources and spaces.脗聽 This paper presents an innovative high performance computing cluster system to support education learning activities in HPC course with small size, low cost, and yet powerful enough. In recent years, High Performance computing usually implanted in cluster computing and require high specification computer and expensive cost. It is not efficient applying High Performance Computing in Educational research activiry such as learning in Class. Therefore, our proposed system is created with inexpensive component by using Embedded System to make High Performance Computing applicable for leaning in the class. Students involved in the construction of embedded system, built clusters from basic embedded and network components, do benchmark performance, and implement simple parallel case using the cluster. 脗聽In this research we performed evaluation of embedded systems comparing with i5 PC, the results of our embedded system performance of NAS benchmark are similar with i5 PCs. We also conducted surveys about student learning satisfaction that with embedded system students are able to learn about HPC from building the system until making an application that use HPC system
An谩lisis y evaluaci贸n del uso de la supercomputaci贸n en la mejora del desempe帽o formativo = Analysis and evaluation of supercomputing for training performance improvement
205 p.Los recursos de supercomputaci贸n son en la actualidad el pilar fundamental para el desarrollo de la investigaci贸n en diversos campos. Su impacto se basa en la capacidad de c谩lculo, que permite realizar simulaciones computacionales que permiten mejorar la precisi贸n de los experimentos. La presente Tesis Doctoral pretende, en primer lugar, realizar un estudio de la evoluci贸n de la supercomputaci贸n y su aplicaci贸n a diversos campos para, posteriormente, estudiar los factores determinantes que permitan analizar los aspectos m谩s relevantes a la hora de estudiar la relaci贸n existente entre los estudios de supercomputaci贸n con los aspectos pedag贸gicos, de conocimiento y de contenido, bas谩ndose en el modelo TPACK. El estudio se realiz贸 con informaci贸n procedente de la base de datos de estudiantes del Centro de Supercomputaci贸n de Castilla y Le贸n (SCAYLE), de la que se obtuvieron 97 participantes. En el estudio se realiz贸 un an谩lisis factorial para comprobar que la estructura de datos obtenida era coherente con el modelo TPACK usado como referencia. Los resultados obtenidos del an谩lisis relacionan las dimensiones tecnol贸gicas con las de conocimiento, pedag贸gicas y de contenido