Papers › GPU Scheduler for De Novo Genome Assembly with Multiple MPI Processes
GPU Scheduler for De Novo Genome Assembly with Multiple MPI Processes
Minhao Li, Siyu Wang, Guanghao Wei
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De Novo Genome assembly is one of the most important tasks in computational biology. ELBA is the state-of-the-art distributed-memory parallel algorithm for overlap detection and layout simplification steps of De Novo genome assembly but exists a performance bottleneck in pairwise alignment. In this work, we proposed 3 GPU schedulers for ELBA to accommodate multiple MPI processes and multiple GPUs. The GPU schedulers enable multiple MPI processes to perform computation on GPUs in a round-robin fashion. Both strong and weak scaling experiments show that 3 schedulers are able to significantly improve the performance of baseline while there is a trade-off between parallelism and GPU scheduler overhead. For the best performance implementation, the one-to-one scheduler achieves ∼7-8× speed-up using 25 MPI processes compared with the baseline vanilla ELBA GPU scheduler.
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