以少博多：融合能力主导型与容量主导型计算
More for Less: Integrating Capability-Predominant and Capacity-Predominant Computing
AI总结：
本研究通过分析DOE实验室两类真实工作负载，提出工作负载融合与注入两种方法，并利用事件驱动模拟验证统一系统协同调度能力型与容量型作业可提升资源利用率并降低成本。
摘要：
能力型作业（例如大型、长时间运行的任务）和容量型作业（例如小型、短时间运行的任务）是高性能计算（HPC）中两种常见的工作负载类型。通常部署不同的HPC系统来处理不同的计算工作负载。例如，阿贡国家实验室领导力计算设施（ALCF）的Theta系统主要服务于能力型作业，而国家能源研究科学计算中心（NERSC）的Cori系统则主要处理容量型工作负载。然而，这种分离往往导致资源利用效率低下，并且由于需要运行独立的计算平台而增加成本。本研究探讨了整合孤立平台的各种假设情景。具体而言，我们收集并刻画了来自美国能源部（DOE）实验室生产系统的两个真实工作负载，分别代表能力主导型和容量主导型计算。我们研究了两种统一方法。工作负载融合探讨了当统一系统容纳多样化工作负载时资源的利用效率，而工作负载注入则识别了通过利用容量型作业来提升能力计算系统资源利用率的机遇。最后，通过基于轨迹的广泛事件驱动模拟，我们探索了在统一系统上协同调度两类作业以提升资源利用率并降低成本的潜在益处，为未来统一计算研究提供了新的见解。
英文摘要：
Capability jobs (e.g., large, long-running tasks) and capacity jobs (e.g., small, short-running tasks) are two common types of workloads in high-performance computing (HPC). Different HPC systems are typically deployed to handle distinct computing workloads. For example, Theta at the Argonne Leadership Computing Facility (ALCF) primarily serves capability jobs, while Cori at the National Energy Research Scientific Computing Center (NERSC) predominantly handles capacity workloads. However, this segregation often leads to inefficient resource utilization and higher costs due to the need for operating separate computing platforms. This work examines what-if scenarios for integrating siloed platforms. Specifically, we collect and characterize two real workloads from production systems at DOE laboratories, representing capabilitypredominant and capacity-predominant computing, respectively. We investigate two approaches to unification. Workload fusion explores how efficiently resources are utilized when a unified system accommodates diverse workloads, whereas workload injection identifies opportunities to enhance resource utilization on capability computing systems by leveraging capacity jobs. Finally, through extensive trace-based, event-driven simulations, we explore the potential benefits of co-scheduling both types of jobs on a unified system to enhance resource utilization and reduce costs, offering new insights for future research in unified computing.
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