$\textit{De Novo}$ 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 $\textit{De Novo}$ genome assembly but exists a performance bottleneck in pairwise alignment. In this work, we introduce 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 $\sim$7-8$\times$ speed-up using 25 MPI processes compared with the baseline vanilla ELBA GPU scheduler.

翻译：$\textit{De Novo}$ 基因组组装是计算生物学中最重要的任务之一。ELBA是当前最先进的分布式内存并行算法，用于$\textit{De Novo}$基因组组装的重叠检测和布局简化步骤，但在成对比对中存在性能瓶颈。在本文中，我们为ELBA引入了3种GPU调度器，以支持多个MPI进程和多个GPU。这些GPU调度器使得多个MPI进程能够以轮询方式在GPU上执行计算。强扩展和弱扩展实验均表明，三种调度器能够显著提升基线的性能，但在并行度和GPU调度器开销之间存在权衡。对于最佳性能实现，一对一调度器在使用25个MPI进程时，相比基线原始ELBA GPU调度器实现了约7-8倍的加速。