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122 P. Petoumenos et al. As long as the ILP-feedback technique does not hurt the MLP, it yields benefits. When that changes, the performance loss is unacceptable. This hazard is avoided when the MLP mechanism is used because it works as a safety net. With the help of MLP mechanism, resizing can be pushed to the limit. 6.2.2 Direct Comparison of ILP- and ILP/MLP-Aware Techniques In this section, we compare the behavior of the two approaches, each evaluated for a configuration which minimizes its average EDP. An additional consideration was to find configurations that do not harm EDP over the base case for any benchmark. This, however, is not possible for the ILP-feedback technique, lest we are content with marginal EDP improvements. Thus, for the ILP-feedback we remove this restriction and we simply select the threshold that minimizes average EDP, which is 256instructions. The ILP-feedback with the MLP mechanism can be pushed much harder as it is evident from Fig.4 with the 768-instruction threshold. However, EDP worsens over the base case for two programs (applu and art), even though the average EDP is minimized. The threshold that gives the second best average EDP —giving up less than 2% over the previous best case— for the combined ILP/MLP mechanism is the 512-instruction threshold which satisfies our requirement for EDP improvement across all benchmarks. Figures 5, 6, 7 illustrate the normalized EDP, execution time increase and energy savings respectively for the “best” thresholds for each mechanism. The end result is that the very aggressive resizing of the ILP/MLP technique harms performance comparably to the conservative ILP-feedback technique but at the same time manages to 1.2 1.1 1 0.9 0.8 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB Conservative Aggressive ILP-feedback ILP-feedback with MLP 60% 50% 40% 30% 20% 10% 0% 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB Conservative Aggressive ILP-feedback ILP-feedback with MLP Fig. 4. Average (geometric mean) Normalized EDP (left) and Performance Degradation (right) for ILP-feedback and ILP-feedback with MLP-awareness 1.2 1.1 1 0.9 0.8 0.7 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB swim mgrid applu gcc galgel art crafty facerec lucas parser vortex apsi wupwise vpr twolf mcf ammp ILP-feedback ILP-feedback with MLP Fig. 5. Normalized Energy-Delay Product for ‘best’ configurations: ILPFeedback (256 threshold) and ILP-Feedback with MLP (512 threshold)
80% 70% 60% 50% 40% 30% 20% 10% 0% MLP-Aware Instruction Queue Resizing: The Key to Power-Efficient Performance 123 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB swim mgrid applu gcc galgel art crafty facerec lucas parser vortex apsi wupwise vpr twolf mcf ammp ILP-feedback ILP-feedback with MLP Fig. 6. Execution Time Increase for ILP-Feedback and ILP-Feedback with MLP (each for its best configuration) 40% 30% 20% 10% 0% 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB 256KB 512KB 1MB 2MB swim mgrid applu gcc galgel art crafty facerec lucas parser vortex apsi wupwise vpr twolf mcf ammp ILP-feedback ILP-feedback with MLP Fig. 7. Normalized Energy Savings for ILP-Feedback and ILP-Feedback with MLP (each for its best configuration) reduce the IQ size more and produce significantly higher energy savings. This results in an EDP for the ILP/MLP technique that is consistently better than the EDP of the ILP-feedback technique, almost doubling the benefit on average (6.1-7.2% compared to 1.4-3.4% of the ILP-feedback technique). The added benefits of MLP-aware management diminish slightly with cache size, since with fewer misses we have less opportunities for MLP. Finally, note that the performance degradation of the ILP/MLP technique is kept at reasonable levels, while even for the conservative ILP-feedback it can vary considerably more (e.g., mcf execution time increases 67%-69%). 7 Conclusions In this paper, we revisit techniques for resizing the instruction queue aiming to improve the power-efficiency of high-performance out-of-order cores. Prior approaches resized the IQ paying attention primarily to ILP. In many cases this results in considerable loss of performance while the energy gains from the IQ are bounded with respect to the energy of the whole processor. The result is that EDP improves in some cases but worsens in others making such techniques inconsistent. The culprit for this is MLP —Memory-Level Parallelism. Resizing the IQ can reduce the amount of MLP in programs with serious consequences on performance. With this realization, we set out to provide a technique that can be applied on top of previous IQ resizing techniques. Our technique, detects possible MLP at runtime and uses prediction to guide IQ resizing decisions. Because we need to manage the whole IQ, our basic unit of management is a sequence of basic blocks, called superpath,
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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6 J. Mische et al. 4.1 Instruction
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8 J. Mische et al. Additionally the
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10 J. Mische et al. % of unused pip
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12 J. Mische et al. % of cycles spe
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14 J. Mische et al. 16. Lickly, B.,
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16 G. Aşılıoğlu, E.M. Kaya, and
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26 T.B. Preußer, P. Reichel, and R
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38 P. Bellasi, W. Fornaciari, and D
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50 J. Zeppenfeld and A. Herkersdorf
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62 B. Jakimovski, B. Meyer, and E.
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172 M.F. Dolz et al. at the inactiv
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Effect of the Degree of Neighborhoo
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176 T. Abdullah et al. A zone based
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178 T. Abdullah et al. A consumer/p
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180 T. Abdullah et al. Messages 140
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182 T. Abdullah et al. (except when
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184 T. Abdullah et al. % Matchmakin
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186 T. Abdullah et al. show that th
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200 R. Plyaskin and A. Herkersdorf
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212 M.Y. Qadri, D. Matichard, and K
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A Tightly Coupled Accelerator Infra
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224 F. Nowak and R. Buchty where A
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226 F. Nowak and R. Buchty Fig. 3.
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228 F. Nowak and R. Buchty Table 2.
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230 F. Nowak and R. Buchty Table 4.
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232 F. Nowak and R. Buchty 5.3 Comp
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Optimizing Stencil Application on M
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236 F. Xudong et al. compared to th
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238 F. Xudong et al. stencil comput
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240 F. Xudong et al. threads in the
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242 F. Xudong et al. Speedup 14 12
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244 F. Xudong et al. 5 Related Work
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Abdullah, Tariq 174 Alima, Luc Onan
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