HyTiS: Hybrid Tile Scheduling for GPU GEMM with Enhanced Wave Utilization and Cache Locality

General matrix-matrix multiplication (GEMM) is a fundamental operation in both deep learning and scientific computing. To accelerate these workloads, GPUs with a large number of streaming multiprocessors (SMs) are widely used. However, as modern GPUs scale in core count and adopt larger tile sizes, the wave quantization problem induced by partially filled waves results in growing hardware underutilization and substantially degraded performance. Existing solutions to this problem often suffer from low execution efficiency or introduce additional synchronization overhead.