Collaborative Research: CCRI: Planning-C: Accelerated Infrastructure for Simulating Future Systems

基于软件的仿真是计算机体系结构研究和开发的支柱。该项目的新颖性在于发现了这些方法存在的以下问题：(1)使用本地服务器运行模拟存在较高的运行成本和体现的能源成本。虽然仿真性能一直是基于软件的仿真器的主要工程重点，但详细的硬件仿真对能耗和环境的影响一直被忽视。(2)基于软件的详细模拟具有不同于裸机应用的运行时特征。这些差异使得具有默认服务器配置的通用服务器在运行详细模拟时的效率极低。(3)最后，全系统仿真环境具有陡峭的学习曲线，随着被仿真的硬件和软件堆栈变得更加复杂，学习曲线进一步陡峭。该项目的影响是让CEISE社区关注本地模拟集群的能源浪费，并为将未来硬件评估的能源和碳足迹降至最低奠定基础。研究人员的目标是创建一个基于云的社区基础设施，用于运行模拟。这种社区基础设施消除了像NSF这样的资助机构投资于本地服务器的需要，并显著减少了模拟的端到端碳足迹。更具体地说，调查人员广泛分析流行的建筑模拟器的源代码和运行时，以了解它们作为需要在真实硬件上高效执行的软件程序的特征。研究人员使用分析结果来调整系统参数，并为运行建筑模拟提供高度优化的环境。他们用硬件加速器增强了拟议的云基础设施，进一步提高了流行的建筑模拟器的能效和性能。该基础设施遵循市场模式，共享和使用其他研究人员开发的模拟模型和设置，以减少详细模拟的学习曲线。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Software-based simulation is the backbone of computer architecture research and development. The project novelty is the identification of the following problems with these methodologies: (1) The use of on-premise servers for running simulations suffers from the high operation and embodied energy costs. Although simulation performance has always been a primary engineering focus of software-based simulators, energy consumption and environmental impacts of detailed hardware simulation have mainly been ignored. (2) Detailed software-based simulations have different run-time characteristics than baremetal applications. Such differences make the general-purpose servers with default server configurations extremely inefficient for running detailed simulations. (3) Lastly, full-system simulation environments have steep learning curves that further steepen as the hardware and software stacks to be simulated become more complex. The project’s impacts are to bring the CISE community's attention to the energy waste at the onpremise simulation clusters and provide a foundation for minimizing the energy and carbon footprint of future hardware evaluation.The investigators’ goal is to create a cloud-based community infrastructure for running simulations. This community infrastructure eliminates the need for funding agencies such as NSF to invest in on-premise servers and significantly reduce the end-to-end carbon footprint of simulations. More specifically, investigators extensively analyze the source code and run-time of the popular architectural simulators to understand their characteristics as a software program that needs to be efficiently executed on real hardware. The investigators use the profiling results to tune system parameters and provide a highly optimized environment for running architectural simulations. They enhance the proposed cloud infrastructure with hardware accelerators that further improve popular architectural simulators' energy efficiency and performance. The infrastructure follows a marketplace model for sharing and using simulation models and setups developed by other researchers to reduce the learning curve of detailed simulations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.