The Phase Based Behavior of Objects

高性能处理器为支持社会机构的计算基础设施提供了强大的动力。在20世纪90年代，投机主题推动了处理器设计各个方面的创新，并导致了惊人的性能提升。今天，单处理器性能也没有扩展。这通常归因于技术问题，但更令人不安的是缺乏一个令人信服的主题来驱动新一代的微架构创新，就像过去的猜测一样。虽然同一芯片上的多个处理器（多核或众核）是一个重要的主题，但它并不是万能的。许多软件是非并行的、复杂的和不规则的，并且不能以传统方式利用多个处理器。因此，多核主题必须与一个新的顺序程序为中心的推力相结合。该项目提出了一个新的微架构的主题，其中的处理器有一个全局的角度来看，一个正在运行的程序的数据对象和他们目前的影响程序作为一个整体。在主要提出的设计中，通过专门化接下来可以在对象上操作的少数候选代码片段，基于存储在对象中的新数据简化和解析代码，实时满足对象的更改。这大大缩短了运行程序的长度。这种新的执行模型提出了一种独特的并行化形式：尽管程序本身不是并行化的（它是精简的），但通过将不同对象的责任分配给多核或众核平台中的不同处理器，专门化任务是高度并行的。另一种解释可能是，许多核心之间的专门化任务构成整个程序的Meta并行执行。另一个强大的方面是在专业化核心之间维护关于程序的持久元数据-编译器拥有的信息（以及更多），但现在嵌入在执行结构中，由未来多核基底的计算和内存容量实现。

High-performance processors provide the horsepower beneath the computing infrastructure that supports society's institutions. In the 1990s, the theme of speculation drove innovation in all aspects of processor design and led to phenomenal performance gains. Today, single processor performance is not scaling as well. This is often attributed to technology issues, but more unsettling is the lack of a compelling theme to drive a new generation of microarchitecture innovation, like speculation in the past. While multiple processors on the same chip (multicore or many-core) is an important theme, it is not a panacea. Much software is non-parallel, complex, and irregular, and cannot take advantage of multiple processors in traditional ways. Thus, the multicore theme must be combined with a new sequential-program-centric thrust.This project puts forward a new microarchitecture theme, in which the processor has a global perspective of a running program's data objects and their current influence on the program as a whole. In the chief proposed design, changes to objects are met in real-time by specializing the few candidate fragments of code that could operate next on the objects, simplifying and resolving the code based on the new data stored within the objects. This significantly reduces the length of the running program. This new execution model presents a unique form of parallelization: although the program itself is not parallelized (it is reduced), specialization tasks are highly parallel by virtue of assigning responsibility for different objects to different processors in a multicore or many-core platform. An alternative interpretation may be that the specialization tasks among many cores constitute a meta dataflow execution of the whole program. Another powerful aspect is maintaining persistent meta-data about the program among specialization cores -- information a compiler has (and more) but which is now embedded in the execution fabric, enabled by the computation and memory capacity of future multicore substrates.