1. GPU support in Spark allows for accelerating Spark applications by offloading compute-intensive tasks to GPUs. However, production deployments face challenges like low resource utilization and overload when scheduling mixed GPU and CPU workloads. 2. The presentation proposes solutions like recognizing GPU tasks to optimize the DAG and inserting new GPU stages. It also discusses policies for prioritizing and allocating GPU and CPU resources independently through multi-dimensional scheduling. 3. Evaluation shows the ALS Spark example achieving speedups on GPUs. IBM Spectrum Conductor provides a Spark-centric shared service with fine-grained resource scheduling, reducing wait times and improving utilization across shared GPU and CPU resources.