BOOSTEDSOSA：低方差随机在线调度的加速推理
BOOSTEDSOSA: Accelerated Inferencing for Low Variance Stochastic Online Scheduling
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中文总结 AI 辅助
针对随机在线调度中用户运行时估计方差大的问题，提出双FPGA ML辅助调度架构BOOSTEDSOSA，经真实HPC数据验证，MAE显著降低，推理速度大幅提升。
中文摘要 AI 辅助
在高性能计算（HPC）等随机在线环境中的异构调度是一项重大挑战。随机在线调度加速器（SOSAs）提供了一种有前景的解决方案，但它们的有效性因依赖用户提供的运行时估计而受到损害。这些估计在调度过程中引入了显著的方差（平均平均绝对误差（MAE）达数百核心日），从而削弱了随机在线调度算法的竞争力，因为其竞争比边界随运行时变异性而增加。为解决此限制，我们提出了BOOSTEDSOSA，一种双FPGA的ML辅助调度架构，集成了用于预期处理时间的机器学习预测器和一种新颖的时间感知训练策略。该预测器仅使用提交时可用的调度器参数来估计作业运行时，使其可在现有HPC系统中使用。使用来自阿贡领导力计算设施、MIT Supercloud和UIUC Blue Waters工作负载数据集的历史真实世界HPC作业数据，我们表明该预测器与用户运行时估计相比将MAE降低了高达63.85%，而加性训练策略与静态模型相比将MAE降低了高达71.88%。端到端来看，BOOSTEDSOSA与AVX优化的软件基线相比实现了平均17倍的加速，并且每秒可处理多达1711个作业。
英文摘要
Heterogeneous scheduling in stochastic, online envi- ronments, such as high-performance computing (HPC) systems, presents a significant challenge. Stochastic Online Scheduling Accelerators (SOSAs) offer a promising solution, but their effectiveness is compromised by a reliance on runtime estimates provided by users. These estimates introduce substantial vari- ance into the scheduling process (mean MAE in hundreds of Core-Days), thereby weakening the competitiveness of Stochastic Online Scheduling algorithms as their competitive-ratio bound increases with runtime variability. To address this limitation, we introduce BOOSTEDSOSA, a dual-FPGA ML-assisted Scheduling architecture that integrates a Machine Learning predictor for expected processing times, with a novel temporal-aware training policy. The predictor estimates job runtimes using only scheduler parameters available at submission time, enabling its use in existing HPC systems. Using historical real-world HPC job data (from the Argonne Leadership Comput- ing Facility, MIT Supercloud and UIUC Blue Waters workload datasets), we show that the predictor reduces MAE by up to 63.85% compared to user runtime estimates, and the additive training policy reduces MAE by up to 71.88% compared to a static model. End-to-end, BOOSTEDSOSA achieves an average 17x speedup over an AVX-optimized software baseline and processes up to 1,711 jobs/seconds
