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首页> 外文会议>ACM/IEEE Annual International Symposium on Computer Architecture >Focused Value Prediction* : Concepts, techniques and implementations presented in this paper are subject matter of pending patent applications, which have been filed by Intel Corporation
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Focused Value Prediction* : Concepts, techniques and implementations presented in this paper are subject matter of pending patent applications, which have been filed by Intel Corporation

机译：重点价值预测*：本文介绍的概念，技术和实现是正在申请的专利的主题，该专利申请已由英特尔公司提交

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摘要

Value Prediction was proposed to speculatively break true data dependencies, thereby allowing Out of Order (OOO) processors to achieve higher instruction level parallelism (ILP) and gain performance. State-of-the-art value predictors try to maximize the number of instructions that can be value predicted, with the belief that a higher coverage will unlock more ILP and increase performance. Unfortunately, this comes at increased complexity with implementations that require multiple different types of value predictors working in tandem, incurring substantial area and power cost.In this paper we motivate towards lower coverage, but focused, value prediction. Instead of aggressively increasing the coverage of value prediction, at the cost of higher area and power, we motivate refocusing value prediction as a mechanism to achieve an early execution of instructions that frequently create performance bottlenecks in the OOO processor. Since we do not aim for high coverage, our implementation is light-weight, needing just 1.2 KB of storage. Simulation results on 60 diverse workloads show that we deliver 3.3% performance gain over a baseline similar to the Intel Skylake processor. This performance gain increases substantially to 8.6% when we simulate a futuristic up-scaled version of Skylake. In contrast, for the same storage, state-of-the-art value predictors deliver a much lower speedup of 1.7% and 4.7% respectively. Notably, our proposal is similar to these predictors in performance, even when they are given nearly eight times the storage and have 60% more prediction coverage than our solution.
机译：提出了值预测来推测性地打破真实的数据依赖关系，从而使乱序（OOO）处理器可以实现更高的指令级并行度（ILP）并获得性能。最先进的价值预测器会尝试最大化可以进行价值预测的指令数量，并相信更高的覆盖率将解锁更多的ILP并提高性能。不幸的是，随着要求多种不同类型的价值预测器协同工作的实现方式的复杂性增加，这会导致相当大的面积和电力成本。在本文中，我们致力于降低覆盖率，但要有针对性地进行价值预测。我们没有以更大的面积和更大的功耗为代价来积极地增加价值预测的覆盖范围，而是将价值预测重新聚焦为一种机制，以实现早期执行指令，从而经常在OOO处理器中造成性能瓶颈。由于我们的目标不是高覆盖率，因此我们的实现是轻量级的，仅需要1.2 KB的存储空间。在60种不同工作负载上的仿真结果表明，与基于Intel Skylake处理器的基准相比，我们的性能提高了3.3％。当我们模拟未来派的Skylake升级版本时，此性能提升将显着提高到8.6％。相反，对于相同的存储，最新的价值预测器分别提供了低得多的1.7％和4.7％的加速。值得注意的是，我们的建议在性能上与这些预测变量相似，即使它们的存储量接近其八倍，并且预测覆盖范围比我们的解决方案多60％。

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