Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HPC) systems. Traditional software-based schedulers struggle to efficiently balance workloads due to scheduling overhead, lack of adaptability to stochastic workloads, and suboptimal resource utilization. The scheduling problem further compounds in the context of shared HPC clusters, where job arrivals and processing times are inherently stochastic. Prediction of these elements is possible, but it introduces additional overhead. To perform this complex scheduling, we developed two FPGA-assisted hardware accelerator microarchitectures, Hercules and Stannic. Hercules adopts a task-centric abstraction of stochastic scheduling, whereas Stannic inherits a schedule-centric abstraction. These hardware-assisted solutions leverage parallelism, pre-calculation, and spatial memory access to significantly accelerate scheduling. We accelerate a non-preemptive stochastic online scheduling algorithm to produce heterogeneity-aware schedules in near real time. With Hercules, we achieved a speedup of up to 1060x over a baseline C/C++ implementation, demonstrating the efficacy of a hardware-assisted acceleration for heterogeneity-aware stochastic scheduling. With Stannic, we further improved efficiency, achieving a 7.5x reduction in latency per computation iteration and a 14x increase in the target heterogeneous system size. Experimental results show that the resulting schedules demonstrate efficient machine utilization and low average job latency in stochastic contexts.

翻译：高效的工作负载调度是现代异构计算环境（尤其是高性能计算（HPC）系统）中的关键挑战。传统基于软件的调度器因调度开销、对随机工作负载适应性不足以及资源利用率欠佳而难以有效平衡负载。在共享HPC集群环境中，任务到达与处理时间本质具有随机性，调度问题进一步加剧。尽管可对这些要素进行预测，但会引入额外开销。为完成复杂调度任务，我们开发了两种基于FPGA的硬件加速器微架构——Hercules与Stannic。Hercules采用面向任务的随机调度抽象，而Stannic则继承面向调度流程的抽象。这些硬件辅助方案通过利用并行计算、预计算与空间内存访问能力显著加速调度过程。我们实现了一种非抢占式随机在线调度算法的加速，使其能够近实时生成异构感知调度方案。基于Hercules，我们相较于基线C/C++实现获得了最高1060倍的加速比，验证了硬件辅助加速在异构感知随机调度中的有效性。通过Stannic，我们进一步提升了效率，使单次计算迭代延迟降低7.5倍，目标异构系统规模提升14倍。实验结果表明，在随机场景下，生成的调度方案实现了高效机器利用率与低平均作业延迟。