2. 読んだ人 みよし たけふみ 2010.06.30 CITED BY 40

5. Extract hot data stream

7. Overview

10. Fast Hot Data Stream Detection

11. Bursty Tracing Framework [15] for Low-overhead temporal profiling 2 つのバージョンを用意 nCheck と nInst で実行コードを選択 Vulcan でバイナリ変換してチェックコード等を挿入

12. Extensions for Online Optilization

13. Fast Hot Data Stream Detection(1) = to compress the profile and infer its hierarchical structure. [23]

14. Fast Hot Data Stream Detection(2) v.heat = v.length*v.frequency A.heat = wA.length*A.coldUses

15. Overhead of profiling and analysis

17. Injecting Detection and Prefetching Code

18. Generating Detection and Prefetching Code Hot data stream v = v1v2...v{v.length} into a head v.head = v1v2...vheadLen and a tail v.tail = v{headLen+1}v{headLen+2}...v{v.length} .

20. Performance impact

22. 読んだ人 みよし たけふみ 2010.06.30 CITED BY 18

24. 重要なIrregularなload命令はストライドアクセスパタンをもっている(ようだ)

27. 読んだ人 みよし たけふみ 2010.06.30 CITED BY 2

30. CとJavaで効果を評価 [20] M. H. Lipasti, C. B. Wilkerson, and J. P. Shen. Value Locality and Load Value Prediction. In Proceedings of the second international conference on architectural support for programming languages and operating systems , pages 138–147, 1996.

32. Kind: object F ield, A rray element, S calar variable

34. 5 load-value predictors, 2048/infinite entries (i) lv , which predicts the last value for every load (ii) l4v , which predicts one of the last four values for every load (iii) st2d , which uses strides to predict loads (iv) fcm , which uses a representation of the context of preceding loads to predict a load (v) dfcm , which enhances fcm with strides.

36. 種類別キャッシュミス率

37. Predictionの成功率