SHF: Small: Architectural Support for New Parallel Execution Paradigms Via Agile Threads

在多核处理器时代，微处理器只有在存在丰富的线程级并行性的情况下才能继续在性能上扩展。要实现这一不断扩展软件并行性的目标，显然需要开发新的编译、语言和执行范例。许多建议的执行范例的可行性和新范例的引入的一个巨大障碍是现代处理器架构无法有效地执行短线程。 如果这些新的执行模型能够自由地识别和利用10秒到100秒指令长的并行性机会，那么它们中的许多可以在向硬件暴露并行性方面非常有效。 然而，当前的机器并没有被设计为很好地执行短线程。这项研究的目标是显着降低启动成本的新线程（或线程新的核心）。这反过来又降低了决定一段代码是否可并行化的盈亏平衡点。术语“线程迁移”用于表示大量的并行执行操作，所有这些操作都涉及将存储或缓存的状态从一个核心移动到另一个核心。 这些操作包括分叉线程、用于热管理或负载平衡的迁移/移动线程、循环并行线程、任务级并行、辅助线程、事务执行和推测性多线程-所有这些操作都将通过本研究在一定程度上加速。这项研究将攻击线程启动成本的所有来源，包括软件（例如，操作系统）开销、分支预测器状态、高速缓存的数据和指令状态、提交等待时间以及在核之间传送主线程状态的开销。除了减少并行编程的复杂性，更广泛的imapcts这项研究包括研究生和本科生的培训，一个开源的仿真基础设施的可用性。

In the multi-core processor era, microprocessors will only continue to scale in performance in the presence of abundant thread level parallelism. Achieving this goal of continuously scaling software parallelism will clearly require the exploitation of new compilation, language, and execution paradigms. One huge barrier to the viability of many proposed execution paradigms and the introduction of new paradigms is the inability of modern processor architectures to execute short threads efficiently. Many of these new execution models can be highly effective at exposing parallelism to the hardware if they have the freedom to identify and exploit opportunities for parallelism that are 10s to 100s of instructions long. However, current machines are not designed to execute short threads well. The goal of this research is to significantly reduce the startup cost for a new thread (or thread new to a core). This in turn reduces the break-even point that determines whether a piece of code is parallelizable or not.The term "thread migration" is used to indicate a large number of parallel execution operations, all of which involve moving stored or cached state from one core to another. These operations include forked threads, migrated/moved threads for thermal management or load balancing, loop-parallel threads, task-level parallelism, helper threading, transactional execution, and speculative multithreading - all of these operations will be accelerated to some degree by this research. This research will attack all sources of the thread startup cost, including software (e.g., operating system) overheads, branch predictor state, cached data and instruction state, the commit latency, and the overhead of transferring the primary thread state between cores. In addition to reducing the parallel programming complexity, the broader imapcts of this research include graduate and undergraduate student training, availability of an open-souce simulation infrastructure.