HuntKTm: Hybrid Scheduling and Automatic Management for Efficient Kernel Execution on Modern GPUs
Wenxuan Pan, Zejia Lin, Jiangsu Du, Xianwei Zhang · ACM Transactions on Architecture and Code Optimization · 2025
Nowadays, Graphics Processing Units (GPUs) dominate in a wide spectrum of computing realms with massive parallel processing capabilities. However, as resources are continuously integrated into GPUs, traditional serial execution often leads to underutilization. Prior studies have shown that allowing multiple kernels to run concurrently and share GPU resources can effectively improve both resource utilization and system throughput. Nonetheless, existing methods for solely automating concurrent kernel execution either schedule kernels within individual applications (i.e., kernel-level) or enable task concurrency across multiple applications (i.e., task-level), thus leaving substantial GPU capacity underexploited. Moreover, they inevitably introduce new programming frameworks, which incur cumbersome manual efforts and further impose substantial programming burdens on developers. To address these limitations, we propose HuntKTm , a hybrid scheduling and automatic management method that cooperates kernel-level and task-level concurrency to enhance system throughput with minimal code modification. Specifically, HuntKTm comprises a stream scheduler to assign kernels, a task scheduler to dispatch tasks onto GPUs, and a memory manager to reduce memory footprint. The stream scheduler applies a strategy to dispatch kernels to hardware queues and adopts a novel algorithm to remove redundant synchronizations in computational flow. Then, the task scheduler automatically issues tasks based on resource requirements and availability to support GPU sharing among uncooperative applications. Finally, the memory manager reduces memory footprint for tasks by limiting the lifetimes of memory objects, which thereby enables more tasks to execute simultaneously. Experimental results demonstrate that HuntKTm improves system throughput by 33.2% over the existing state-of-the-art CASE framework on a single machine equipped with four NVIDIA A100 GPUs and reaches 13.8% higher application-level performance over Taskflow, even with lessened programming efforts.