Bulk Scheduling with the DIANA Scheduler

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 200

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

CRAY mini manual. Revision D

This document briefly describes the use of the CRAY supercomputers that are an integral part of the Supercomputing Network Subsystem of the Central Scientific Computing Complex at LaRC. Features of the CRAY supercomputers are covered, including: FORTRAN, C, PASCAL, architectures of the CRAY-2 and CRAY Y-MP, the CRAY UNICOS environment, batch job submittal, debugging, performance analysis, parallel processing, utilities unique to CRAY, and documentation. The document is intended for all CRAY users as a ready reference to frequently asked questions and to more detailed information contained in the vendor manuals. It is appropriate for both the novice and the experienced user

Analysis of the Norwegian-Swedish Market for Green Certificates Using the EMPS Model

Over the last few years it has been important for the European countries to support green energy as a countermeasure against climate changes. Norway and Sweden have chosen to implement a common green certificate market, which is a support scheme for renewable energy technology. The scheme will be a crucial contribution for Norway to abide by EUs' Renewables Directive. So far the market is doing really well and is on track to complete the goal of 26,4 TWh new electricity production from renewables before the end of 2020. The joint certificate market is very closely connected to the power market. Therefore, the EMPS model, which is a good model of the Nordic power market, was chosen as the model to use for testing and forecasting in this thesis. The main objective has been to analyze the Norwegian-Swedish certificate market in the EMPS model with particular focus on factors that affect the green certificate price levels and price fluctuations. This has been done by adjusting and updating a realistic dataset for the Nordic power market. The historic values from the real certificate market were used as inputs and as a basis for the simulations. 10 different cases have been presented and simulated, where the main differences between them were the expansion rate of new production and the expansion of different energy sources. The results obtained from the simulations corresponded to theoretical findings about the green certificate market. Currently the certificate storage holds 13 million certificates, but it appears like it might increase towards a value of 15 million during 2015, before it will start to decrease considerably from 2016. In order to achieve different price scenarios in the simulations, the initial certificate storage needed to be lowered a great deal from the real value, so that the EMPS model could see a possibility of deficit in the future. The most ideal cases with the most even price levels were achieved when the amount of production and consumption of green certificates were as close together as possible. It was demonstrated by the simulations that the expansion rate of new production greatly influences the certificate prices. If the expansion rate is fast, a smaller probability of deficit exists, and as a result the certificate prices will be lower. The opposite is true for a slow expansion rate. The different types of production sources (hydropower, wind power and biofuels) also affected the certificate price levels in different ways, even though the storage developments were the same. When there was more hydropower expansion the average price was the lowest, but the price curve was the most extreme. Expansion in wind power lead to a higher average price, while expansion in biofuels had the most even price curve. A reason for these different price scenarios could be that both hydropower and biofuels can be adjusted in response to power prices. However, different calibrations for the EMPS model had to be performed for the different cases. It is therefore likely that this also influenced the price levels for the different types of production. All things considered, it looks as if 2015 is going to be a very important year regarding the future of the common certificate market. A lot of decisions needs to be made regarding both changes in regulation and potential new investments, which will affect whether the common goal will be met or not. The future is uncertain, but with more work and testing the EMPS model could hopefully predict some of it