Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum

We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum

Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI

The computational difficulty of econometric problems has increased dramatically in recent years as econometricians examine more complicated models and utilize more sophisticated estimation techniques. Many problems in econometrics are `embarrassingly parallel' and can take advantage of parallel computing to reduce the wall clock time it takes to solve a problem. In this paper I demonstrate a method that can be used to solve a maximum likelihood problem using the MPI message passing library. The econometric problem is a simple multinomial logit model that does not require parallel computing but illustrates many of the problems one would confront when estimating more complicated models

Parallel software applications in high-energy physics

Parallel programming allows the speed of computations to be increased by using multiple processors or computers working jointly on the same task. In parallel programming dif culties that are not present in sequential programming can be encountered, for instance communication between processors. The way of writing a parallel program depends strictly on the architecture of a parallel system. An ef cient program of this kind not only performs its computations faster than its sequential version, but also effectively uses the CPU time. Parallel programming has been present in high-energy physics for years. The lecture is an introduction to parallel computing in general. It discusses the motivation for parallel computations, hardware architectures of parallel systems and the key concepts of a parallel programming. It also relates parallel computing to high-energy physics and presents a parallel programming application in the eld, namely PROOF

OpenCUDA+MPI

The introduction and rise of General Purpose Graphics Computing has significantly impacted parallel and high-performance computing. It has introduced challenges when it comes to distributed computing with GPUs. Current solutions target specifics: specific hardware, specific network topology, a specific level of processing. Those restrictions on GPU computing limit scientists and researchers in various ways. The goal of OpenCUDA+MPI project is to develop a framework that allows researchers and scientists to write a general algorithm without the overhead of worrying about the specifics of the hardware and the cluster it will run against while taking full advantage of parallel and distributed computing on GPUs. As work toward the solution continues to progress, we have proven the need for the framework and expect to find the framework enables scientists to focus on their research