Prediction-Based Task Migration on S-NUCA Many-Cores

Rapp, Martin; Pathania, Anuj; Mitra, Tulika; Henkel, Jörg

doi:10.23919/date.2019.8714974

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Communication-oriented migration algorithms focus on minimizing the communication latency while migrating tasks [10], [27]- [29]. However, most of them do not consider thermal constraint, or use a pessimistic power budget when making task migration decisions.…”

Section: Dynamic Resource Allocation With Task Migrationmentioning

confidence: 99%

“…However, this thermal overheating problem can be mitigated by adopting a task migration strategy that allows tasks running at a hot core to migrate to a processor core with lower temperatures ("cooler" cores). The task migration approaches [7]- [10] migrate a task running on an overheated core to another core that has a lower temperature. The first issue is to determine how many dark cores should be allocated to the currently running applications, and how many dark/free cores should be reserved for the incoming applications.…”

Section: Introductionmentioning

confidence: 99%

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Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Wen

Wang

Singh

et al. 2022

IEEE Trans. Comput.

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As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for "hot" tasks to be migrated to run on a "cool" core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this paper, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50% reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.

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Section: Dynamic Resource Allocation With Task Migrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Wen

Wang

Singh

et al. 2022

IEEE Trans. Comput.

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“…Models of the properties of the platform and its environment can be built with SL algorithms, where training data is extracted with the help of run-time or design-time profiling. Such techniques have been successfully employed, for example, for deciding task migrations [95,96]. In this scope, Reference [95] uses a lightweight NN to predict the steady-state temperature after a task migration.…”

Section: Learning Properties Of the Platform And Its Environmentmentioning

confidence: 99%

“…In this scope, Reference [95] uses a lightweight NN to predict the steady-state temperature after a task migration. Reference [96] employs a NN-based model to predict the performance of a task after migrating it to another core. This model takes into account the complex workload-specific dependencies of the performance on average cache latency and power budget.…”

Section: Learning Properties Of the Platform And Its Environmentmentioning

confidence: 99%

Hybrid Application Mapping for Composable Many-Core Systems: Overview and Future Perspective

Pourmohseni

Glaß

Henkel

et al. 2020

JLPEA

Self Cite

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Many-core platforms are rapidly expanding in various embedded areas as they provide the scalable computational power required to meet the ever-growing performance demands of embedded applications and systems. However, the huge design space of possible task mappings, the unpredictable workload dynamism, and the numerous non-functional requirements of applications in terms of timing, reliability, safety, and so forth. impose significant challenges when designing many-core systems. Hybrid Application Mapping (HAM) is an emerging class of design methodologies for many-core systems which address these challenges via an incremental (per-application) mapping scheme: The mapping process is divided into (i) a design-time Design Space Exploration (DSE) step per application to obtain a set of high-quality mapping options and (ii) a run-time system management step in which applications are launched dynamically (on demand) using the precomputed mappings. This paper provides an overview of HAM and the design methodologies developed in line with it. We introduce the basics of HAM and elaborate on the way it addresses the major challenges of application mapping in many-core systems. We provide an overview of the main challenges encountered when employing HAM and survey a collection of state-of-the-art techniques and methodologies proposed to address these challenges. We finally present an overview of open topics and challenges in HAM, provide a summary of emerging trends for addressing them particularly using machine learning, and outline possible future directions. While there exists a large body of HAM methodologies, the techniques studied in this paper are developed, to a large extent, within the scope of invasive computing. Invasive computing introduces resource awareness into applications and employs explicit resource reservation to enable incremental application mapping and dynamic system management.

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“…Peak power minimization in the context of multi-/many-core scheduling is an active subject of research [15]. The problem is important in both embedded [16][17][18] as well as super-computing domain [19,20]. The authors in [21] propose an algorithm to minimize peak power for an application with a task graph without a deadline.…”

Section: Related Workmentioning

confidence: 99%

PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

Maity

Pathania

Mitra

2020

JLPEA

Self Cite

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Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

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Prediction-Based Task Migration on S-NUCA Many-Cores

Cited by 16 publications

References 13 publications

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Hybrid Application Mapping for Composable Many-Core Systems: Overview and Future Perspective

PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

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