Efficient GPU Multitasking with Morphable Kernels

GPU multitasking offers a promising approach to improving hardware utilization by co-locating concurrent workloads on the same device. However, achieving high resource utilization with minimized interference requires fine-grained, adaptive scheduling. Existing scheduling solutions are fundamentally constrained by a rigid assumption: once a kernel is launched, its resource footprint remains fixed throughout its execution. Consequently, they either rely on static resource pre-partitioning, which lacks the flexibility to adapt to rapid workload changes, or adopt kernel-slicing techniques, which achieve fine-grained control at the cost of prohibitive kernel launch overheads.