• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Operating systems review >Analysis of Branch Prediction via Data Compression
【24h】

Analysis of Branch Prediction via Data Compression

机译：通过数据压缩分析分支预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Branch prediction is an important mechanism in modern microprocessor design. The focus of research in this area has been on designing new branch prediction schemes. In contrast, very few studies address the theoretical basis behind these prediction schemes. Knowing this theoretical basis helps us to evaluate how good a prediction scheme is and how much we can expect to improve its accuracy. In this paper, we apply techniques from data compression to establish a theoretical basis for branch prediction, and to illustrate alternatives for further improvement. To establish a theoretical basis, we first introduce a conceptual model to characterize each component in a branch prediction process. Then we show that current "two-level" or correlation based predictors are, in fact, simplifications of an optimal predictor in data compression, Prediction by Partial Matching (PPM). If the information provided to the predictor remains the same, it is unlikely that significant improvements can be expected (asymptotically) from two-level predictors, since PPM is optimal. However, there are a rich set of predictors available from data compression, several of which can still yield some improvement in cases where resources are limited. To illustrate this, we conduct trace-driven simulation running the Instruction Benchmark Suite and the SPEC CINT95 benchmarks. The results show that PPM can outperform a two-level predictor for modest sized branch target buffers.
机译：分支预测是现代微处理器设计中的重要机制。该领域的研究重点是设计新的分支预测方案。相反，很少有研究讨论这些预测方案背后的理论基础。了解这一理论基础有助于我们评估预测方案的质量以及可以提高预测精度的程度。在本文中，我们应用数据压缩技术为分支预测建立理论基础，并举例说明进一步改进的方法。为了建立理论基础，我们首先引入概念模型来表征分支预测过程中的每个组件。然后，我们证明当前的基于“两级”或相关性的预测变量实际上是数据压缩中的最佳预测变量的简化，即部分匹配预测（PPM）。如果提供给预测变量的信息保持不变，则由于PPM是最佳的，因此不太可能期望（渐近）两级预测变量的显着改善。但是，数据压缩有很多可用的预测器，在资源有限的情况下，其中一些仍可以带来一些改进。为了说明这一点，我们使用“ Instruction Benchmark Suite”和“ SPEC CINT95”基准进行跟踪驱动的仿真。结果表明，对于中等大小的分支目标缓冲区，PPM的性能优于二级预测器。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号