A comparison between a Computational Grid and a High-end Multicore Server in an academic environment David Risinamhodzi – North-west University- South Africa- David – David.Risinamhodzi@nwu.ac.za

Scientific problem - Definition Increasing demand for more computational resources in fields such as mathematical computation, scientific simulation, data mining and climate forecasting. One challenge in meeting this high computing demand has been the high cost of acquiring high-performance processors The underutilization of computing resources in academic environments remains a challenge many institutions and the NWU(VTC) is no exception. Reference to Prof Roberto’s presentation regarding distributed computing platform to provide the much needed computational capacity FANLAB by Isaac:- need for high performance computing in academia Scalability of application by Joseline 2

Proposed Scientific solution Using computational grids to alleviate the pressure on multicore servers as a solution to curb underutilization as well as provide the much needed computational capacity. Figure showing a generic architecture of the SA National Grid 3

Scientific Method: Compare computational grid and multicore server Generic comparisons in scientific computing

Scientific Method: Compare computational grid and multicore server Research methodology used in the study

Results Experiment 1: SVM Multicore server performed up to four times better than the grid with bigger data sets regarding simple turnaround time. Both systems performed relatively similar when using smaller data sets. Variation in turnaround time on the grid was higher that the local server:- unpredictability of the turnaround time. (data staging time was a factor) Results Experiment 1: SVM Overall performance of the systems Variation in overall system performance

Results Experiment 1: SVM with minimum data staging Overall performance of the systems Multicore server performs up to two times better than the grid in simple turnaround time. Variation in turnaround time stabilizes as compared to the previous real life scenario case. (with min data staging the predictability of turnaround time increases) Variation in overall system performance

Results Experiment 1: Text preparing corpus Variation in overall system performance Experiment 1: Text preparing corpus Multicore server performed better than the computational grid used in this case

Findings Computational grid advantages Allows abstraction of workflows so execution can be done at any available site Allows abstraction of the data location and this metadata service was used to achieve efficient data distribution e.g (OAR). Ease of collaboration in research. Services on the SA national grid are free of charge. Grid provided redundancy and better fault handling during experiments. Backend services such as Logical File Catalogue, gLibrary metadata service and CREAM were accepting and wrapping jobs for submission. Availability of computing resources. Enables easy building of scientific communities of practice. Computational grid disadvantages Inconsistencies in data staging time Access to the grid was difficult because researcher did not belong to a virtual organization that is part of the South African national grid Overhead and unavailability of site administrators at times made it difficult to use the grid Policies of use such as proxy renewal settings hindered the study as tasks were sometimes terminated before completion. Reliance on the experts to use the grid as there is little or no documentation. Network problems at times university ICT’s and(SANREN):- indicates the need to keep growing the network and increasing capacity.

Findings Multicore server disadvantages Multicore server advantages Local server lacks redundancy Access local server is affected negatively when it is overloaded (lacks load balancing capabilities) Local server does not promote easy sharing of data, scripts and other resources because of local access control and restrictions on who is allowed to use the system No abstraction of workflows so as to execute them on any other stand-alone machine if the need arises. Outdated software versions due to lack of a dedicated server administrator Multicore server advantages Allows quick and easy data distribution and experiment setup, as well as easy software installation Allows researcher full control of the local server in terms of access and configuration. Local server’s performance was easy to predict in terms of simple turnaround time researcher had access to the hardware of the local server in cases where there were network problems and power failures. Service level agreements on how to share the local server’s resources and how to use the local server effectively were easy to attain since administrators are local

Discussion Researchers can use both systems as they complement each other and cannot be separated since the multicore server is part of a distributed computing system such as a grid computer. Academic Institutions regarding Grid Join federations such as the SA National Grid. Hire a site administrator to port applications to the computational grid. Promote open science in education. Researcher regarding Grid Ease of collaboration with other researchers. Mobility and redundancy Long term retention of applications(port to CODE-RADE and have it forever) Could help mitigate in the enormous need for high-end computing capacity.

Much Appreciation to Sci-GaIA for the support, resources and exposure as well as Dr. Becker(SA National Grid Coordinator) Thank you! sci-gaia.eu info@sci-gaia.eu 12