Estimating the overhead and coupling of scientific computing clusters

Since simulation became the third pillar of scientific research, several forms of computers have become available to drive computer aided simulations, and nowadays, clusters are the most popular type of computers supporting these tasks. For instance, cluster settings, such as the so-called supercomputers, cluster of workstations (COW), cluster of desktops (COD), and cluster of virtual machines (COV) have been considered in literature to embrace a variety of scientific applications. However, those scientific applications categorized as high-performance computing (HPC) are conceptually restricted to be addressed only by supercomputers. In this aspect, we introduce the notions of cluster overhead and cluster coupling to assess the capacity of non-HPC systems to handle HPC applications. We also compare the cluster overhead with an existing measure of overhead in computing systems, the total parallel overhead, to explain the correctness of our methodology. The evaluation of capacity considers the seven dwarfs of scientific computing, which are well-known, scientific computing building blocks considered in the development of HPC applications. The evaluation of these building blocks provides insights regarding the strengths and weaknesses of non-HPC systems to deal with future HPC applications developed with one or a combination of these algorithmic building blocks.

Cluster Of Workstations for cloud foundry based functions and applications

In this publication we describe on OpenRAN based cluster of workstations, implementing cloud foundry Kubernetes, architecture for AWS inspired Lambdas and threads on cloud foundry, implemented on dedicated units as a cluster and on robotic segway RMP 440 platforms as HTCondor based thread schedulers, using PyMACS integrated with a resident kernel inspired by the shadow process of HT Condor. This allows for thread scheduling on idle robotic CPUsKubernetes, Cloud Foundry, AWS, VPN, HTCondor.

Exhaustive Community Enumeration in Parallel

An algorithm to evaluate/count all the possible communities of a graph is presented. An associated unrank function is described. An implementation of an existing algorithm to evaluate all the possible partitions of a graph, based on an unrank function, is presented as well. Performance results of the parallelizations of these algorithms obtained on a shared memory machine, a cluster of workstations and a Graphical Processing Unit (GPU) are included.

The parallelization of the Keller box method on heterogeneous cluster of workstations

High performance computing is the branch of parallel computing dealing with very large problems and large parallel computers that can solve those problems in a reasonable amount of time. This paper will describe the parallelization of the Keller-box method using the high performance computing on heterogeneous cluster of workstations. The problem statement is based on the equation of boundary-layer flow due to a moving flat plate. The objective is to develop the parallel algorithm of the Keller-box method in purpose to solve a large size of matrix. The parallelization is based on the domain decomposition, where the upper and lower matrices will be splitting into a number of blocks, which then will be compute concurrently on the parallel computers. The experiment was run using 200, 2000, and 20000 size of matrices and using 10 number of processors. The comparison was made from the results obtained from that various size of matrices by doing the analysis based on the performance measurement in terms of time execution, speedup, and effectiveness.