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UID:submissions.supercomputing.org_SC22_sess226_spostg115@linklings.com
SUMMARY:Optimizing Traceback in the Smith-Waterman Algorithm for GPUs
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nOptimizing Traceba
 ck in the Smith-Waterman Algorithm for GPUs\n\nLindsey\n\nThe traceback ph
 ase of the Smith-Waterman (SW) algorithm requires significant memory and i
 ntroduces an irregular memory access pattern which makes it challenging to
  implement for GPU architectures. In this work, we introduce a novel strat
 egy for implementing the traceback kernel for the SW algorithm on GPUs by 
 restructuring the global memory access patterns and introducing a memory-e
 fficient data structure for storing large dynamic programming matrices in 
 GPU’s limited memory. To demonstrate this kernel’s performance we integrat
 ed this into the existing ADEPT library and Metahipmer2, a de novo metagen
 omic short read assembler. Our implementation is 3.6x faster than tracebac
 k in GASAL2, and 51x faster than traceback in Striped Smith-Waterman, the 
 current state of the art SW libraries on GPU and CPU respectively. It sped
  up the final alignment step in Metahipmer2 by an average of 44% and impro
 ved the overall execution time of Metahipmer2 by an average of 13%.\n\nReg
 istration Category: Tech Program Reg Pass
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