Flicker

Author:

Petrica Paula1,Izraelevitz Adam M.1,Albonesi David H.1,Shoemaker Christine A.1

Affiliation:

1. Cornell University, Ithaca, NY

Abstract

Future microprocessors may become so power constrained that not all transistors will be able to be powered on at once. These systems will be required to nimbly adapt to changes in the chip power that is allocated to general-purpose cores and to specialized accelerators. This paper presents Flicker , a general-purpose multicore architecture that dynamically adapts to varying and potentially stringent limits on allocated power. The Flicker core microarchitecture includes deconfigurable lanes --horizontal slices through the pipeline--that permit tailoring an individual core to the running application with lower overhead than microarchitecture-level adaptation, and greater flexibility than core-level power gating. To exploit Flicker's flexible pipeline architecture, a new online multicore optimization algorithm combines reduced sampling techniques, application of response surface models to online optimization, and heuristic online search. The approach efficiently finds a near-global-optimum configuration of lanes without requiring offline training, microarchitecture state, or foreknowledge of the workload. At high power allocations, core-level gating is highly effective, and slightly outperforms Flicker overall. However, under stringent power constraints, Flicker significantly outperforms core-level gating, achieving an average 27% performance improvement.

Funder

Division of Computing and Communication Foundations

Cornell Engineering Learning Initiatives

Division of Computer and Network Systems

Intel Corporation

Publisher

Association for Computing Machinery (ACM)

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SparseAdapt: Runtime Control for Sparse Linear Algebra on a Reconfigurable Accelerator;MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture;2021-10-17

2. Post-silicon CPU adaptation made practical using machine learning;Proceedings of the 46th International Symposium on Computer Architecture;2019-06-22

3. Simplifying and implementing service level objectives for stream parallelism;The Journal of Supercomputing;2019-06-05

4. An experiment-driven energy consumption model for virtual machine management systems;Sustainable Computing: Informatics and Systems;2018-06

5. Bespoke Processors for Applications with Ultra-low Area and Power Constraints;ACM SIGARCH Computer Architecture News;2017-09-14

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