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（特别是高性能计算（HPC）系统）中的关键挑战。传统基于软件的调度器由于调度开销、对随机工作负载缺乏适应性以及资源利用率欠佳，难以有效平衡工作负载。在共享HPC集群中，任务到达和处理时间本质上是随机的，这使得调度问题进一步复杂化。对这些要素进行预测虽有可能，但会引入额外开销。为执行这种复杂调度，我们开发了两种FPGA辅助硬件加速器微架构：Hercules和Stannic。Hercules采用以任务为中心的随机调度抽象，而Stannic则继承以调度为中心的抽象。这些硬件辅助解决方案借助并行性、预计算和空间内存访问来显著加速调度过程。我们加速了一种非抢占式随机在线调度算法，以近乎实时的方式生成异构感知的调度方案。通过Hercules，我们实现了相对于基础C/C++实现高达1060倍的加速比，证明了硬件辅助加速对于异构感知随机调度的有效性。借助Stannic，我们进一步提升了效率，实现了每次计算迭代延迟降低7.5倍，目标异构系统规模扩大14倍。实验结果表明，在随机场景下，所生成的调度方案展现出高效的机器利用率和较低的平均任务延迟。