Performance-Aware Resource Management of Multi-Threaded Applications on Many-Core Systems

Olsen, Daniel; Anagnostopoulos, Iraklis

doi:10.1145/3060403.3060426

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…Regarding software actuators, most of the works found in the literature consider Application Admission as a problem prior to the TM, which makes AA the least employed. For example, authors [16,[37][38][39][40][41] present frameworks that focus directly on deciding the best number of tasks for a given application before the TM. In Rahmani et al [30] , the application enters the system only if there are available processors, although applications can also be killed suddenly if the power overcomes the capping.…”

Section: Related Workmentioning

confidence: 99%

The power impact of hardware and software actuators on self-adaptable many-core systems

Martins

Garibotti

Dutt

et al. 2019

Journal of Systems Architecture

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Section: Related Workmentioning

confidence: 99%

The power impact of hardware and software actuators on self-adaptable many-core systems

Martins

Garibotti

Dutt

et al. 2019

Journal of Systems Architecture

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“…In order to minimize these undesirable side efects, thermal and resource management strategies are needed. Besides dynamic voltage frequency scaling (DVFS), thermal-aware mapping and scheduling strategies have been proposed to improve the reliability of the processor [34,42]. For instance, it is possible to change the mapping of compute intensive applications at run-time to prevent thermal hot spots on the processor [15].…”

Section: Introductionmentioning

confidence: 99%

An FPGA-based Approach to Evaluate Thermal and Resource Management Strategies of Many-core Processors

Mettler

Rapp

Khdr

et al. 2022

ACM Trans. Archit. Code Optim.

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The continuous technology scaling of integrated circuits results in increasingly higher power densities and operating temperatures. Hence, modern many-core processors require sophisticated thermal and resource management strategies to mitigate these undesirable side effects. A simulation-based evaluation of these strategies is limited by the accuracy of the underlying processor model and the simulation speed. Therefore, we present, for the first time, an FPGA-based evaluation approach to test and compare thermal and resource management strategies using the combination of benchmark generation, FPGA-based ASIC emulation and run-time monitoring. The proposed benchmark generation method enables an evaluation of run-time management strategies for applications with various run-time characteristics. Furthermore, the ASIC emulation platform features a novel distributed temperature emulator design, whose overhead scales linearly with the number of integrated cores, and a novel dynamic voltage frequency scaling emulator design, which precisely models the timing and energy overhead of voltage and frequency transitions. In our evaluations, we demonstrate the proposed approach for a tiled many-core processor with 80 cores on four Virtex-7 FPGAs. Additionally, we present the suitability of the platform to evaluate state-of-the-art run-time management techniques with a case study.

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“…An orthogonal aspect of resource allocation schemes is, whether they work at design time, runtime, or both (hybrid). An example of a hybrid scheme is presented in [16], which is a performance-aware resource management scheme for many-core architectures. Note that [16] performs application mapping in a centralized way.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…An example of a hybrid scheme is presented in [16], which is a performance-aware resource management scheme for many-core architectures. Note that [16] performs application mapping in a centralized way.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Agent-based Constraint Solving for Resource Allocation in Manycore Systems

Wenzel¹,

Bauer²,

Schröder-Preikschat³

et al. 2022

Preprint

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For efficiency reasons, manycore systems are increasingly heterogeneous, which makes the mapping of complex workloads a key problem with a high optimization potential. Constraints express the application requirements like which core type to choose, how many cores to choose, exclusively or non-exclusively, using a certain core, etc. In this work, we propose a decentralized solution for solving application resource constraints by means of an agent-based approach in order to obtain scalability. We translate the constraints into a Distributed Constraint Optimization Problem (DCOP) and propose a local search algorithm RESMGM to solve them. For the first time, we demonstrate the viability and efficiency of the DCOP approach for heterogeneous manycore systems. Our RESMGM algorithm supports a far wider range of constraints than state-of-the-art, leading to superior results, but still has comparable overheads w.r.t. computation and communication.

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Performance-Aware Resource Management of Multi-Threaded Applications on Many-Core Systems

Cited by 11 publications

References 22 publications

The power impact of hardware and software actuators on self-adaptable many-core systems

The power impact of hardware and software actuators on self-adaptable many-core systems

An FPGA-based Approach to Evaluate Thermal and Resource Management Strategies of Many-core Processors

Agent-based Constraint Solving for Resource Allocation in Manycore Systems

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