构建跨云边端的科学工作流系统，提升科研效率与安全。
Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security
- 设计跨云、边、端的分布式工作流架构
- 实现多源数据集长期协同管理
- 适合需要高效协同计算的科研团队
自动化理论-实验循环需要有效利用跨越实验室仪器、边缘传感器、多个设施的计算资源、分布在多个信息源的数据集以及云端的计算能力。然而，传统的连续体平台构建、任务编排和数据集长期管理方法无法满足科学对性能、能耗、安全性和可靠性的要求。高效组合与执行工作流任务——包括数值求解器、数据分析和机器学习——是最大化连续体资源利用率的关键。太平洋西北国家实验室的LDRD项目“云、高性能计算（HPC）和边缘用于科学与安全”（CHESS）开发了一套相互关联的能力，以支持分布式科学工作流和数据集的持续管理。本报告从开放科学视角描述了CHESS项目的研究成果与成功经验。
原文摘要 · Abstract (English)
Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information sources, and potentially cloud. Unfortunately, the obvious methods for constructing continuum platforms, orchestrating workflow tasks, and curating datasets over time fail to achieve scientific requirements for performance, energy, security, and reliability. Furthermore, achieving the best use of continuum resources depends upon the efficient composition and execution of workflow tasks, i.e., combinations of numerical solvers, data analytics, and machine learning. Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets. This report describes the results and successes of CHESS from the perspective of open science.
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