cs240a-sources123

# Cs240a-sources123 - Sources of Parallelism in Physical Simulation Based on slides from David Culler Jim Demmel Kathy Yelick et al UCB CS267

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Sources of Parallelism in Physical Simulation Based on slides from David Culler, Jim Demmel, Kathy Yelick, et al., UCB CS267

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2 Parallelism and Locality in Simulation Real world problems have parallelism and locality: Some objects may operate independently of others. Objects may depend more on nearby than distant objects. Dependence on distant objects can often be simplified. Scientific models may introduce more parallelism: When a continuous problem is discretized, time-domain dependencies are generally limited to adjacent time steps. Far-field effects can sometimes be ignored or approximated. Many problems exhibit parallelism at multiple levels Example: circuits can be simulated at many levels, and within each there may be parallelism within and between subcircuits.
3 Multilevel Modeling: Circuit Simulation Circuits are simulated at many different levels Level Primitives Examples Instruction level Instructions SimOS, SPIM Cycle level Functional units VIRAM-p Register Transfer Level (RTL) Register, counter, MUX VHDL Gate Level Gate, flip-flop, memory cell Thor Switch level Ideal transistor Cosmos Circuit level Resistors, capacitors, etc. Spice Device level Electrons, silicon

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4 Basic kinds of simulation Discrete event systems Time and space are discrete Particle systems Important special case of lumped systems Ordinary Differential Equations (ODEs) Lumped systems Location/entities are discrete, time is continuous Partial Different Equations (PDEs) Time and space are continuous discrete continuous
5 Basic Kinds of Simulation Discrete event systems: Examples: “Game of Life,” logic level circuit simulation. Particle systems: Examples: billiard balls, semiconductor device simulation, galaxies. Lumped variables depending on continuous parameters: ODEs, e.g., circuit simulation (Spice), structural mechanics, chemical kinetics. Continuous variables depending on continuous parameters: PDEs, e.g., heat, elasticity, electrostatics. A given phenomenon can be modeled at multiple levels. Many simulations combine more than one of these techniques.

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6 A Model Problem: Sharks and Fish Illustration of parallel programming Original version: WATOR, proposed by Geoffrey Fox Sharks and fish living in a 2D toroidal ocean Several variations to show different physical phenomena Basic idea: sharks and fish living in an ocean rules for movement breeding, eating, and death forces in the ocean forces between sea creatures See link on course home page for details
7 Discrete Event Systems

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8 Discrete Event Systems Systems are represented as: finite set of variables. the set of all variable values at a given time is called the
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## This note was uploaded on 12/27/2011 for the course CMPSC 240A taught by Professor Gilbert during the Fall '09 term at UCSB.

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Cs240a-sources123 - Sources of Parallelism in Physical Simulation Based on slides from David Culler Jim Demmel Kathy Yelick et al UCB CS267

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