Tessellation

Colmenares, Juan A.; Eads, Gage; Hofmeyr, Steven; Bird, Sarah; Moretó, Miquel; Chou, David; Gluzman, Brian; Roman, Eric; Bartolini, Davide B.; Mor, Nitesh; Asanović, Krste; Kubiatowicz, John

doi:10.1145/2463209.2488827

Cited by 35 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance of CPU applications are observed by the decision engine that decides resource partitions using available actions defined by system designers. ARCC [2] is a self-computing framework implemented in the Tessellation many-core OS. It performs a two-level scheduling scheme: First, the resource allocation broker distributes global resources, and then at the user level scheduling policies are customized separately.…”

Section: Related Work Memory Schedulingmentioning

confidence: 99%

“…Priority-based resource allocation handles non-partitionable with little computation in comparison with previous management models [2,5,21]. This facilitates instant response from the memory system to QoS demands.…”

Section: Distributed System Responsementioning

confidence: 99%

“…However, adjusting both bounds at the same time could lead to unexpected results. For example, it is not obvious which one of [3,5] and [2,6] would be a better choice to fix QoS failure. To simplify the adjustment, we increment or decrement one bound at a time.…”

Section: Configurable Translationmentioning

confidence: 99%

“…This allows algorithms with much higher time complexity than that in Figure 5 to be deployed at the global regulator. Since prior work have extensively studied the management policies for best-effort cores and services [2,5], we will not further the discussion on global regulation algorithms.…”

Section: Global Regulationmentioning

confidence: 99%

“…Although the policy can be extended to include more media cores, it cannot be applied to real-time cores whose target QoS cannot be assessed in terms of frame rate. Moreover, holistic memory management frameworks for CPU-centric homogeneous systems have also been explored recently [2,5,21]. This series of work typically constructs a management model based on the control theory to partition computing and memory resources.…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Self-aware Resource Management Framework for Heterogeneous Multicore SoCs with Diverse QoS Targets

Song

Alavoine

Lin

2019

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose the Self-Aware Resource Allocation framework for heterogeneous MP-SoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores. Moreover, we present a runtime scheme to configure priority-based adaptation so that distinct sensitivities of heterogeneous QoS targets with respect to memory allocation can be accommodated. In addition, the priority of best-effort cores can also be regulated. This article is an extension of a conference paper: "SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs," published in DAC'18 (Song et al.). In particular, we extend our work in the following ways: -We add a new section to discuss the challenges of priority-based adaptation in the SARA framework. To deal with the challenges, we introduce a two-stage runtime configuration solution, including distributed self-configuration and global regulation, to accommodate best-effort cores and real-time cores that are particularly sensitive to memory allocation updates.-We expand the evaluation section to include more details on our simulation platform. We also present evaluation results on the runtime configuration scheme for the SARA framework.-In addition, a more comprehensive review of related work is provided in this paper to summarize prior efforts on memory scheduling and management.16:2 Y. Song et al. INTRODUCTIONModern heterogeneous MPSoCs [15,17] have been widely deployed in mobile devices thanks to their energy efficiency. These MPSoCs typically integrate a diverse collection of cores. Figure 1 depicts an example of a heterogeneous MPSoC. Besides general-purpose cores like the CPU for running applications, most heterogeneous cores are dedicated to certain functions, such as the GPU, the DSP, and the display. These cores have diverse notions of Quality-of-Service (QoS). For example, the GPU measures target real-time performance in terms of frame rate, the DSP demands the memory latency to remain below a certain limit, and the display requires sufficient bandwidth to refresh frames at a constant rate.To save cost and energy, heterogeneous cores commonly share resources, among which the sharing of the memory system including the network-on-chip (NoC) and the memory controller (MC) are the most challenging, because memory performance often has a direct and...

show abstract

Section: Related Work Memory Schedulingmentioning

confidence: 99%

Section: Distributed System Responsementioning

confidence: 99%