Communication-aware mapping of KPN applications onto heterogeneous MPSoCs

Castrillón, Jerónimo; Tretter, Andreas; Leupers, Rainer; Ascheid, Gerd

doi:10.1145/2228360.2228597

Cited by 47 publications

(15 citation statements)

References 21 publications

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“…Several resource allocation approaches have been proposed while following different policies. Most of these approaches map communicating tasks of each application close to each other such that communication overhead and power are reduced [2], [7], [8], [9], [11], [21], [23], [31], [34]. Some of these approaches also reduce computation power of the cores by employing voltage/frequency scaling [9].…”

Section: Related Workmentioning

confidence: 99%

“…(8), where |Q| is the network size, |A i | is the average number of tasks in each application, r is the average percentage of bubble count in an application's core region, defined as bubble count divided by the core count in each application's core region, ET i is the average execution time of the tasks, and λ is the average application arrival rate. Using this model, r can be a decision variable such that, when the waiting time is estimated to be high, a smaller r is preferred.…”

Section: Waiting Time Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

Wang

Singh

et al. 2018

IEEE Trans. Comput.

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Abstract-All the cores of a many-core chip cannot be active at the same time, due to reasons like low CPU utilization in server systems and limited power budget in dark silicon era. These free cores (referred to as bubbles) can be placed near active cores for heat dissipation so that the active cores can run at a higher frequency level, boosting the performance of applications that run on active cores. Budgeting inactive cores (bubbles) to applications to boost performance has the following three challenges. First, the number of bubbles varies due to open workloads. Second, communication distance increases when a bubble is inserted between two communicating tasks (a task is a thread or process of a parallel application), leading to performance degradation. Third, budgeting too many bubbles as coolers to running applications leads to insufficient cores for future applications. In order to address these challenges, in this paper, a bubble budgeting scheme is proposed to budget free cores to each application so as to optimize the throughput of the whole system. Throughput of the system depends on the execution time of each application and the waiting time incurred for newly arrived applications. Essentially, the proposed algorithm determines the number and locations of bubbles to optimize the performance and waiting time of each application, followed by tasks of each application being mapped to a core region. A Rollout algorithm is used to budget power to the cores as the last step. Experiments show that our approach achieves 50% higher throughput when compared to state-of-the-art thermal-aware runtime task mapping approaches. The runtime overhead of the proposed algorithm is in the order of 1M cycles, making it an efficient runtime task management method for large-scale many-core systems.

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Section: Related Workmentioning

confidence: 99%

Section: Waiting Time Estimationmentioning

confidence: 99%

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

Wang

Singh

et al. 2018

IEEE Trans. Comput.

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“…The resource allocation process is carried out either at design-time (statically) or run-time (dynamically). Most of the existing literature for resource allocation fall under static resource allocation (e.g., [Murali et al 2006;Hu and Marculescu 2003;Javaid and Parameswaran 2009;Marcon et al 2008;Thiele et al 2011;Meyer et al 2010;Choi et al 2012;Castrillon et al 2012]). However, they cannot handle dynamic workloads and changing environments, e.g.…”

Section: Resource Allocation For Multi/many-core Systemsmentioning

confidence: 99%

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

et al. 2017

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Multi/Many-core systems are envisioned to satisfy the ever increasing performance requirements of complex applications in various domains such as embedded and high performance computing (HPC). Such systems need to cater for increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies in order to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed over the last two decades and highlights the emerging trends for multi/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles such as market and biological concepts to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided.

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“…We model an architecture by a multigraph which has a node for every PE, labeled with the PE type. For every different communication resource between two PEs, the graph has an edge annotated with that communication resource [9]. This graph is sometimes called the architecture graph.…”

Section: Computing Symmetry Groups and Inverse Semigroupsmentioning

confidence: 99%

Symmetry in Software Synthesis

Goens

Siccha

Castrillón

2017

ACM Trans. Archit. Code Optim.

Self Cite

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With the surge of multi- and manycores, much research has focused on algorithms for mapping and scheduling on these complex platforms. Large classes of these algorithms face scalability problems. This is why diverse methods are commonly used for reducing the search space. While most such approaches leverage the inherent symmetry of architectures and applications, they do it in a problem-specific and intuitive way. However, intuitive approaches become impractical with growing hardware complexity, like Network-on-Chip interconnect or heterogeneous cores. In this paper, we present a formal framework that can determine the inherent symmetry of architectures and applications algorithmically and leverage these for problems in software synthesis. Our approach is based on the mathematical theory of groups and a generalization called inverse semigroups. We evaluate our approach in two state-of-the-art mapping frameworks. Even for the platforms with a handful of cores of today and moderate-size benchmarks, our approach consistently yields reductions of the overall execution time of algorithms, accelerating them by a factor up to 10 in our experiments, or improving the quality of the results.Comment: 31 pages, 18 figure

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Communication-aware mapping of KPN applications onto heterogeneous MPSoCs

Cited by 47 publications

References 21 publications

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Symmetry in Software Synthesis

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