  Deep learning (DL) has demonstrated significant success across diverse
fields, leading to the construction of dedicated GPU accelerators within GPU
clusters for high-quality training services. Efficient scheduler designs for
such clusters are vital to reduce operational costs and enhance resource
utilization. While recent schedulers have shown impressive performance in
optimizing DL job performance and cluster utilization through periodic
reallocation or selection of GPU resources, they also encounter challenges such
as preemption and migration overhead, along with potential DL accuracy
degradation. Nonetheless, few explore the potential benefits of GPU sharing to
improve resource utilization and reduce job queuing times. Motivated by these
insights, we present a job scheduling model allowing multiple jobs to share the
same set of GPUs without altering job training settings. We introduce SJF-BSBF
(shortest job first with best sharing benefit first), a straightforward yet
effective heuristic scheduling algorithm. SJF-BSBF intelligently selects job
pairs for GPU resource sharing and runtime settings (sub-batch size and
scheduling time point) to optimize overall performance while ensuring DL
convergence accuracy through gradient accumulation. In experiments with both
physical DL workloads and trace-driven simulations, even as a preemption-free
policy, SJF-BSBF reduces the average job completion time by 27-33\% relative to
the state-of-the-art preemptive DL schedulers. Moreover, SJF-BSBF can wisely
determine the optimal resource sharing settings, such as the sharing time point
and sub-batch size for gradient accumulation, outperforming the aggressive GPU
sharing approach (baseline SJF-FFS policy) by up to 17\% in large-scale traces.
