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Yuan CMSC 838 Presentation Parallelisation of IBD computation for determining genetic disease map
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CMSC 838T – Presentation Introduction u Parallel Genehunter package In high level Provide dynamic allocation mechanism Reduce Space and CPU Time consumption
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CMSC 838T – Presentation Reminder u Genehunter Software package Construction of human genetic maps Generate inherited diseases maps Gene therapy u IBD (Identity by Descent) One function in Genehunter Identify the identical locus transmitted by a common ancestor
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CMSC 838T – Presentation u Markers A number to quantify the genetic resemblance of two relatives affected by the same genetic disease u Non-founder Individual whose parents are in the family u Genetic disease maps Multipoint linkage analysis with many different markers Account for all the family information Reminder
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CMSC 838T – Presentation Problems with Genehunter u Space Requirement: O(2 2n ) n – number of non-founder u Time Requirement: O(m2 2n ) m-number of markers Genehunter is inefficient in terms of Space and Time usage !
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CMSC 838T – Presentation Parallel Genehunter u Goals Not alter the mathematical detail u Solution Genehunter running for each family can be considered as an independent task Master-slave model Message Passing Interface
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CMSC 838T – Presentation Algorithm P0 P1 P2 P3 P4 Master Slaves task1 task2 task3 task4
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CMSC 838T – Presentation Algorithm P0 P1 P2 P3 P4 Master Slaves WORK_REQUEST New tasks
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CMSC 838T – Presentation Evaluation u Experiment environment Network of workstation(NOW): 10 processors (distributed memory) HPC3500: 8 processors (shared memory) SunFire6800: 24 processors( shared memory) u Parameter for test cases
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CMSC 838T – Presentation Evaluation
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CMSC 838T – Presentation Evaluation
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CMSC 838T – Presentation Evaluation
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CMSC 838T – Presentation Related Work u Low-level approach Parallel Genehunter: Implementation of a linkage analysis package for distributed memory architectures
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CMSC 838T – Presentation General approaches u Three model of parallelism High level based on family Low level based on different markers Combine low-level and high-level approach u Select suitable model based on parameters C1: number of family C2: number of markers C3: structure of family
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CMSC 838T – Presentation Observations u Pros Useful: Genehunter is a popular Simple strategy u Cons: Too simply to be efficient Heavy communication between processors Cannot scale to larger number of processors Workload unbalance with large family No concrete example how about to select different models List Implementing combine model as future work …
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