SCALABLE DYNAMIC ADAPTIVE RESOURCE MANAGEMENT IN MULTICORE ARCHITECTURES

Transcription

1 SCALABLE DYNAMIC ADAPTIVE RESOURCE MANAGEMENT IN MULTICORE ARCHITECTURES José F. Martínez José F. Martínez. Unauthorized distribution prohibited. 1

2 MY VERSION OF MOORE S LAW Moore s Law Image: Greudin@Wikimedia Image: Morio@Wikimedia Motivation Prior Art Markets 2

3 MODERN PROCESSORS: A SILICON CITY It s not just a processor Heterogeneous processing Shared on-chip power Shared caches Multicore Shared I/O pins Image: Qualcomm Motivation Prior Art Markets 3

4 THE DDDAS CONTEXT Hierarchical relationship Application: Dynamic data-driven distributed processing (e.g., vision) Support system: Dynamic application-driven execution optimization OS policies Real-time mechanisms Command & Control Self-optimizing UAV ensemble Management of hardware resources Image/Video Capture Ground troops X X ROI Battlefield Data (LIDAR etc.) Motivation Prior Art Markets 4

5 TODAY S PRESENTATION Your chip needs a functioning townhall And your chip is not a village System optimization not a separate concern Integral part of a multi-level DDDAS loop Market-based framework very promising Based on sound, proven theory Fast, scalable, amenable to external tuning 1 st take: XChange (HPCA 15 best paper nominee) Self-optimizing, extensible Motivation Prior Art Markets 5

6 ANARCHY DOESN T WORK 2.5 Crafty-Ammp-Apsi-Art 10.69% Weighted IPC Speedup % -0.44% -7.50% % 1.5 unregulated cache memory power cache_memory cache_power memory_power all Motivation Prior Art Markets 6

7 AUTOCRACIES DON T SCALE Sampling + hill-climbing Search overhead too large: Not scalable Image: Choi and Yeung, ISCA 06 Motivation Prior Art Markets 7

8 A CUE FROM REAL LIFE: MARKET-BASED SOCIETIES Participants try to maximize their own utilities Selfish behavior still benefits social welfare (Largely) distributed solutions exist scalable It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own interest. Adam Smith, The Wealth of Nations Image: Prior Art Markets Utility 8

9 A PEEK INTO MARKET THEORY Competitive market Players have finite budgets Prices are posted, same to all players Players are price-takers: no monopolistic behavior Non-satiation: Can always take more (*) Competitive market equilibrium Prices are such that supply = demand (*) Strictly speaking this is monotonicity of preferences (stronger) Prior Art Markets Utility 9

10 FIRST WELFARE THEOREM Pareto optimality An allocation is Pareto-optimal if there is no way to reallocate goods so that someone is made better off without making someone else worse off Caveat: Pareto-optimal not necessarily perfect But much faster to compute (see results) First welfare theorem Any competitive market equilibrium is Pareto-optimal Prior Art Markets Utility 10

11 MARKET CHALLENGES Market side How does one set prices to satisfy demand? Price-taking heuristic (F. Kelly) price j = bid i ' i ' j total _resource j resource ij = bid ij price j Prior Art Markets Utility 11

12 MARKET CHALLENGES Agent side How do I maximize my bang-for-buck resource-wise? Utility estimation Agents concurrently discover their own utility Analytical models Learning models Heuristics Application-driven parameters possible (DDDAS) Prior Art Markets Utility 12

13 A SIMPLE HEURISTIC UTILITY MODEL Memory phase Cache: Proportional to memory phase Combine Miftakhutdinov + UMON Miftakhutdinov Assume constant across allocations Linear Utility Utility Practical Issues 13

14 A SIMPLE HEURISTIC UTILITY MODEL Compute phase Power: Assume compute phase ~linear to f 0 Measured (perf. counters) Miftakhutdinov Linear Utility Utility Practical Issues 14

15 CONVERGENCE Convergence Detected through price fluctuation (<1%) Price smoothing: Avoid ping-pong by incorporating memory in pricing mechanism Fall back to equal-share after 30 iterations Bankruptcy Everything is too expensive Guarantee one-way cache and 800 MHz operation Exclude from market for one interval Utility Practical Issues Results 15

16 WEALTH REDISTRIBUTION A way to bias optimality Performance-oriented: Give more budget to app with more potential for gain Internally derived, e.g., calculate for min/max resource allocation Mission-oriented: Give more budget to more critical app Externally derived, e.g., application feedback Fairness-oriented: Give same budget to everyone Utility Practical Issues Results 16

17 IMPLEMENTATION Leverage Linux s APIC timer interrupt Every 1 ms, for kernel statistics update Designate master core to post prices, collect bids Modest hardware overhead ~ 4 kb/core (mostly UMON) Utility Practical Issues Results 17

18 EXPERIMENTAL SETUP (FROM PAPER) Practical Issues Results Conclusions 18

19 SUMMARY OF 64-CORE RESULTS Practical Issues Results Conclusions 19

20 OVERHEAD 5M-cycle intervals Practical Issues Results Conclusions 20

21 TODAY S PRESENTATION Your chip needs a functioning townhall And your chip is not a village System optimization not a separate concern Integral part of a multi-level DDDAS loop Market-based framework very promising Based on sound, proven theory Fast, scalable, amenable to external tuning Ongoing work Tunable markets; market ensembles Practical Issues Results Conclusions 21

22 SCALABLE DYNAMIC ADAPTIVE RESOURCE MANAGEMENT IN MULTICORE ARCHITECTURES José F. Martínez José F. Martínez. Unauthorized distribution prohibited. 22