文章探讨了电力系统中温控负荷的作用与技术演进。为确保系统稳定,需整合硬件和软件。硬件提供物理支持,软件注入智能化。硬软件协同作用尤为关键,需在设计阶段考虑整合。以大型商业楼宇为例,展示了高效节能的实际应用和潜能。未来,温...文章探讨了电力系统中温控负荷的作用与技术演进。为确保系统稳定,需整合硬件和软件。硬件提供物理支持,软件注入智能化。硬软件协同作用尤为关键,需在设计阶段考虑整合。以大型商业楼宇为例,展示了高效节能的实际应用和潜能。未来,温控负荷有望与人工智能(Artificial Intelligence,AI)、物联网(Internet of Things,IoT)等技术深度结合,进一步增强其价值。展开更多
Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In...Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In this paper, a new profiling technique is proposed, that incorporates the strength of both software and hardware to achieve near-zero overhead profiling. The compiler passes profiling requests as a few bits of information in branch instructions to the hardware, and the processor executes profiling operations asynchronously in available free slots or on dedicated hardware. The compiler instrumentation of this technique is implemented using an Itanium research compiler. The result shows that the accurate block profiling incurs very little overhead to the user program in terms of the program scheduling cycles. For example, the average overhead is 0.6% for the SPECint95 benchmarks. The hardware support required for the new profiling is practical. The technique is extended to collect edge profiles for continuous phase transition detection. It is believed that the hardware-software collaborative scheme will enable many profile-driven dynamic optimizations for EPIC processors such as the Itanium processors.展开更多
文摘文章探讨了电力系统中温控负荷的作用与技术演进。为确保系统稳定,需整合硬件和软件。硬件提供物理支持,软件注入智能化。硬软件协同作用尤为关键,需在设计阶段考虑整合。以大型商业楼宇为例,展示了高效节能的实际应用和潜能。未来,温控负荷有望与人工智能(Artificial Intelligence,AI)、物联网(Internet of Things,IoT)等技术深度结合,进一步增强其价值。
文摘Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In this paper, a new profiling technique is proposed, that incorporates the strength of both software and hardware to achieve near-zero overhead profiling. The compiler passes profiling requests as a few bits of information in branch instructions to the hardware, and the processor executes profiling operations asynchronously in available free slots or on dedicated hardware. The compiler instrumentation of this technique is implemented using an Itanium research compiler. The result shows that the accurate block profiling incurs very little overhead to the user program in terms of the program scheduling cycles. For example, the average overhead is 0.6% for the SPECint95 benchmarks. The hardware support required for the new profiling is practical. The technique is extended to collect edge profiles for continuous phase transition detection. It is believed that the hardware-software collaborative scheme will enable many profile-driven dynamic optimizations for EPIC processors such as the Itanium processors.
