  $\textit{De Novo}$ 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 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 $\sim$7-8$\times$ speed-up
using 25 MPI processes compared with the baseline vanilla ELBA GPU scheduler.
