Mapping applications with collectives over sub-communicators on torus networks

pre-printThe placement of tasks in a parallel application on specific nodes of a supercomputer can significantly impact performance. Traditionally, this task mapping has focused on reducing the distance between communicating tasks on the physical network. This minimizes the number of hops that point-to-point messages travel and thus reduces link sharing between messages and contention. However, for applications that use collectives over sub-communicators, this heuristic may not be optimal. Many collectives can benefit from an increase in bandwidth even at the cost of an increase in hop count, especially when sending large messages. For example, placing communicating tasks in a cube configuration rather than a plane or a line on a torus network increases the number of possible paths messages might take. This increases the available bandwidth which can lead to significant performance gains. We have developed Rubik, a tool that provides a simple and intuitive interface to create a wide variety of mappings for structured communication patterns. Rubik supports a number of elementary operations such as splits, tilts, or shifts, that can be combined into a large number of unique patterns. Each operation can be applied to disjoint groups of processes involved in collectives to increase the effective bandwidth. We demonstrate the use of Rubik for improving performance of two parallel codes, pF3D and Qbox, which use collectives over sub-communicators

Forecasting Volume of Patients in the Queue Using Monte Carlo Simulation Model

Healthcare is essential to the general welfare of society. It provides for the prevention, treatment, and management of illness and the preservation of mental and physical well-being through the services offered by medical and allied health professions. Hospitals crowding causes a series of negative effects, e.g. medical errors, poor patient treatment and general patient dissatisfaction. In light of these challenges, a need for review and reform of our healthcare practices has become apparent. One road to improve the typical clinical system is to describe the patient flow in a model of the system and how the system is constrained by available equipment, beds and personnel. Various predictive control models have been developed to try and ease overcrowding in hospitals. Such model is the Model Predictive Control to control the queuing systems developed by Yang Wang and Stephen Boyd. The problem with this model is that it is very slow, and thus not very effective. Others are queuing systems, e.g. Lagrange approach of adaptive control based on Markov Chain model. In this study the research has compared the existing prediction models and come up with Monte Carlo Simulation model to forecasting the volume of patients in the queue. The model uses Poisson distribution on arrival and exponential distribution on service time. The R program was used to run the data where after running, it generate random numbers. After several experiments the model has proved to be very accurate and efficient. This will assist the hospital to utilize the resources and reduces cost of operations