Browsing by Author "Scogings, C.J."

Now showing 1 - 3 of 3
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    64-bit architechtures and compute clusters for high performance simulations
    (Massey University, 2006) Hawick, K.A.; James, H.A.; Scogings, C.J.
    Simulation of large complex systems remains one of the most demanding of high performance computer systems both in terms of raw compute performance and efficient memory management. Recent availability of 64-bit architectures has opened up the possibilities of commodity computers accessing more than the 4 Gigabyte memory limit previously enforced by 32-bit addressing. We report on some performance measurements we have made on two 64-bit architectures and their consequences for some high performance simulations. We discuss performance of our codes for simulations of artificial life models; computational physics models of point particles on lattices; and with interacting clusters of particles. We have summarised pertinent features of these codes into benchmark kernels which we discuss in the context of wellknown benchmark kernels of the 32-bit era. We report on how these these findings were useful in the context of designing 64-bit compute clusters for high-performance simulations.
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    Simulating large random Boolean networks
    (Massey University, 2007) Hawick, K.A.; James, H.A.; Scogings, C.J.
    The Kauffman N-K, or random boolean network, model is an important tool for exploring the properties of large scale complex systems. There are computational challenges in simulating large networks with high connectivities. We describe some high-performance data structures and algorithms for implementing large-scale simulations of the random boolean network model using various storage types provided by the D programming language. We discuss the memory complexity of an optimised simulation code and present some measured properties of large networks.
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    Simulation modelling and visualisation: toolkits for building artificial worlds
    (Massey University, 2008) Playne, D.P.; Gerdelan, A.P.; Leist, A.; Scogings, C.J.; Hawick, K.A.
    Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm.