adBoost: Thermal Aware Performance Boosting Through Dark Silicon Patterning

Kanduri, Anil; Haghbayan, Mohammad-Hashem; Rahmani, Amir M.; Shafique, Muhammad; Jantsch, Axel; Liljeberg, Pasi

doi:10.1109/tc.2018.2805683

Cited by 28 publications

(15 citation statements)

References 35 publications

(41 reference statements)

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“…In Kanduri et al (2015) a dark silicon aware runtime mapping method was proposed that activates and deactivates cores as needed in order to evenly distribute power density across the chip. The same authors expand this idea Kanduri et al (2018a) by providing enough thermal headroom for boosting the frequency of active cores upon performance surges. Lower operating temperatures from patterning also allows sustaining the boosting for longer periods, improving the performance further.…”

Section: Dynamic Thermal-aware Management Methodsmentioning

confidence: 99%

“…. (2005),Ayoub et al (2010),Vassighi and Sachdev (2006),Donald and Martonosi (2005),Coskun et al (2007),Liao et al (2005),Jung and Pedram (2006),,Kanduri et al (2015),Chou et al (2017),Kanduri et al (2018a) Resource allocationBrooks and Martonosi (2001),Skadron et al (2003),Hajimiri et al (2013),Christoforakis et al (2015),Lee et al (2015),Lo et al (2016),Wolf et al (2016),Murray et al (2014) …”

mentioning

confidence: 99%

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On-Chip Dynamic Resource Management

Miele

Kanduri²,

Moazzemi³

et al. 2019

FNT in Electronic Design Automation

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The need for dynamic resource management has shadowed the exponential growth of on-chip transistor capacity, and the challenge is accentuated by the heterogeneity of resources, and the bewildering variety of constraints and requirements of applications, platforms and users. The field has started with a few research papers in the early 1990s but has grown today to over hundred yearly publications, leading to an accumulated body of literature presumable far above 1000 papers. We focus on the dynamic (run-time) management of onchip resources and mostly ignore design-time techniques and off-chip resources of larger electronic systems. Moreover, we do not attempt a complete review of all published work on the topic. Rather, this survey provides a structured review and discussion of the state of the art and is divided along the primary objectives of resource management techniques: performance, power, reliability and quality of service, each

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Section: Dynamic Thermal-aware Management Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

On-Chip Dynamic Resource Management

Miele

Kanduri²,

Moazzemi³

et al. 2019

FNT in Electronic Design Automation

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“…Control theoretic approaches for resource management (e.g., [8], [9], [10], [11], [12], [13], [14]) provide formal guarantees for achieving robustness and stability, particularly in the presence of workload variability. Multiple-Input-Multiple-Output (MIMO) control theory is effective for coordinating management of multiple goals in unicore processors [15].…”

Section: Design Methodology For Responsive and Robust Mimo Control Ofmentioning

confidence: 99%

Design Methodology for Responsive and Rrobust MIMO Control of Heterogeneous Multicores

Mück

Donyanavard

Moazzemi

et al. 2018

IEEE Trans. Multi-Scale Comp. Syst.

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Heterogeneous multicore processors (HMPs) are commonly deployed to meet the performance and power requirements of emerging workloads. HMPs demand adaptive and coordinated resource management techniques to control such complex systems. While Multiple-Input-Multiple-Output (MIMO) control theory has been applied to adaptively coordinate resources for single-core processors, the coordinated management of HMPs poses significant additional challenges for achieving robustness and responsiveness, due to the unmanageable complexity of modeling the system dynamics. This paper presents, for the first time, a methodology to design robust MIMO controllers with rapid response and formal guarantees for coordinated management of HMPs. Our approach addresses the challenges of: (1) system decomposition and identification; (2) selection of suitable sensor and actuator granularity; and (3) appropriate system modeling to make the system identifiable as well as controllable. We demonstrate the practical applicability of our approach on an ARM big.LITTLE HMP platform running Linux, and demonstrate the efficiency and robustness of our method by designing MIMO-based resource managers.

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“…To efficiently exploit dark cores, many dark-core-aware approaches have been considered [4]- [8]. The mapping approaches in [5] [6] assume that the system has a fixed number of dark cores, but in reality, the number of dark cores can vary significantly even in a short period of time [10]. Approaches in [5] [7] [8] do not consider the application arrival rate, and thus, their mapping results tend to cause applications to wait too long before they can start their execution.…”

Section: A Dynamic Mapping Without Task Migrationmentioning

confidence: 99%

“…1(b), the ratio of the maximum number of applications to the minimum can be as high as 6:1 [10], which implies that the number of dark cores can also vary greatly over that short time span. However, for the sake of simplicity, both schemes in [6] [7] inaccurately assume that the number of dark cores remains unchanged over a long time interval, undermining the quality of the application mapping results.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Allocation/Reallocation of Dark Cores in Many-Core Systems for Improved System Performance

et al. 2020

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A significant number of processing cores in any many-core systems nowadays and likely in the future have to be switched off or forced to be idle to become dark cores, in light of ever increasing power density and chip temperature. Although these dark cores cannot make direct contributions to the chip's throughput, they can still be allocated to applications currently running in the system for the sole purpose of heat dissipation enabled by the temperature gradient between the active and dark cores. However, allocating dark cores to applications tends to add extra waiting time to applications yet to be launched, which in return can have adverse implications on the overall system performance. Another big issue related to dark core allocation stems from the fact that application characteristics are prone to undergo rapid changes at runtime, making a fixed dark core allocation scheme less desirable. In this paper, a runtime dark core allocation and dynamic adjustment scheme is thus proposed. Built upon a dynamic programming network (DPN) framework, the proposed scheme attempts to optimize the performance of currently running applications and simultaneously reduce waiting times of incoming applications by taking into account both thermal issues and geometric shapes of regions formed by the active/dark cores. The experimental results show that the proposed approach achieves an average of 61% higher throughput than the two state-of-the-art thermal-aware runtime task mapping approaches, making it the runtime resource management of choice in many-core systems. INDEX TERMS Dark core, many-core, dynamic resource allocation, throughput optimization.

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adBoost: Thermal Aware Performance Boosting Through Dark Silicon Patterning

Cited by 28 publications

References 35 publications

On-Chip Dynamic Resource Management

On-Chip Dynamic Resource Management

Design Methodology for Responsive and Rrobust MIMO Control of Heterogeneous Multicores

Dynamic Allocation/Reallocation of Dark Cores in Many-Core Systems for Improved System Performance

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