title: Optimisation and parallelisation strategies for Monte Carlo simulation of HIV infection.
abstract: In recent years, the study of immune response behaviour through mathematical and computational models has attracted considerable efforts. The dynamics of key cell types, and their interactions, has been a primary focus in terms of building a picture of how the immune system responds to a threat. Discrete methods, based on lattice Monte-Carlo (MC) models, with their flexibility and relative simplicity have previously been used to model the immune system behaviour. However, due to speed and memory constraints, large-scale simulations cannot be done on a single computer. Key issues in the reduction of simulation time are code optimisation and code parallelisation. In this paper, optimisation and parallelisation solutions are discussed, with reference to existing MC simulation code for dynamics of HIV infection.
authors.affiliation: Department of Computing, INSA de Lyon, Villeurbanne, France.
authors.forename: D
authors.initials: D
authors.lastname: Hecquet
authors.forename: H J
authors.initials: HJ
authors.lastname: Ruskin
authors.forename: M
authors.initials: M
authors.lastname: Crane
mesh_terms: D000914:Antibodies, Viral / Q000276:immunology; D000956:Antigens, Viral / Q000276:immunology; D001402:B-Lymphocytes / Q000276:immunology; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology; D003198:Computer Simulation; D015658:HIV Infections / Q000276:immunology*; D006801:Humans; D007111:Immunity, Cellular / Q000276:immunology; D007156:Immunologic Memory / Q000276:immunology; D008264:Macrophages / Q000276:immunology; D018448:Models, Immunological; D009010:Monte Carlo Method; D011336:Probability; D011381:Programming Languages; D012984:Software; D013269:Stochastic Processes; D013602:T-Lymphocytes, Cytotoxic; D018417:Th1 Cells / Q000276:immunology; D018418:Th2 Cells / Q000276:immunology
chemical_list: D000914:Antibodies, Viral; D000956:Antigens, Viral