标题： HERCULES：异构系统中随机调度的硬件加速器 显示英文标题

标题： HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems

摘要： 高效的负载调度是现代异构计算环境中的一项关键挑战，尤其是在高性能计算（HPC）系统中。传统的基于软件的调度器由于高调度开销、缺乏对动态负载的适应性以及资源利用不优，难以有效平衡负载分配。这些缺陷在异构系统中尤为突出，其中不同的计算元件可能具有截然不同的性能特征。为了解决这些障碍，我们提出了一种基于FPGA的随机在线调度（SOS）加速器。我们通过将现有的成本方程进行适应性调整，在将其实施到硬件加速器设计之前，使其与离散时间相结合，从而修改了一种贪心成本选择分配策略。我们的设计利用了硬件并行性、预计算和精度量化来减少作业调度延迟。通过引入一种硬件加速的实时调度方法，本文确立了异构计算系统中自适应调度机制的新范式。所提出的设计实现了高吞吐量、低延迟和节能运行，为传统软件调度方法提供了一种可扩展的替代方案。实验结果表明，负载分配一致，机器利用率公平，并且相对于单线程软件调度策略实现，速度提高了最多1060倍。这使得SOS加速器成为部署在高性能计算系统、深度学习流水线和其他性能关键应用中的有力候选。 显示英文摘要

摘要： 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 workload distribution due to high scheduling overhead, lack of adaptability to dynamic workloads, and suboptimal resource utilization. These pitfalls are compounded in heterogeneous systems, where differing computational elements can have vastly different performance profiles. To resolve these hindrances, we present a novel FPGA-based accelerator for stochastic online scheduling (SOS). We modify a greedy cost selection assignment policy by adapting existing cost equations to engage with discretized time before implementing them into a hardware accelerator design. Our design leverages hardware parallelism, precalculation, and precision quantization to reduce job scheduling latency. By introducing a hardware-accelerated approach to real-time scheduling, this paper establishes a new paradigm for adaptive scheduling mechanisms in heterogeneous computing systems. The proposed design achieves high throughput, low latency, and energy-efficient operation, offering a scalable alternative to traditional software scheduling methods. Experimental results demonstrate consistent workload distribution, fair machine utilization, and up to 1060x speedup over single-threaded software scheduling policy implementations. This makes the SOS accelerator a strong candidate for deployment in high-performance computing system, deeplearning pipelines, and other performance-critical applications.