Yuan CMSC 838 Presentation Parallelisation of IBD computation for determining genetic disease map

CMSC 838T – Presentation Introduction u Parallel Genehunter package  In high level  Provide dynamic allocation mechanism  Reduce Space and CPU Time consumption

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

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

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 !

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

CMSC 838T – Presentation Algorithm P0 P1 P2 P3 P4 Master Slaves task1 task2 task3 task4

CMSC 838T – Presentation Algorithm P0 P1 P2 P3 P4 Master Slaves WORK_REQUEST New tasks

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

CMSC 838T – Presentation Evaluation

CMSC 838T – Presentation Evaluation

CMSC 838T – Presentation Evaluation

CMSC 838T – Presentation Related Work u Low-level approach  Parallel Genehunter: Implementation of a linkage analysis package for distributed memory architectures

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

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  …