Mapping of applications to MPSoCs

Marwedel, Peter; Bacivarov, Iuliana; Lee, Chanhee; Teich, Jürgen; Thiele, Lothar; Xu, Qiang; Kouveli, Georgia; Ha, Soonhoi; Huang, Lin

doi:10.1145/2039370.2039390

Cited by 42 publications

(13 citation statements)

References 41 publications

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“…Mapping: For future scenarios, with very large numbers of diverse applications and resources, high quality process-resource mapping can be identified as one of the most urgent problems to be solved [47][48][49]. The quality of mapping is directly relevant to the accuracy of both resource identification (i.e., precise identification of required resources for each given process) and resource allocation (precise process-resource mapping to meet process requirements).…”

Section: Main Requirementsmentioning

confidence: 99%

ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems

Zarrin

Aguiar

Barraca

2016

Microprocessors and Microsystems

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The next generation of many-core enabled large-scale computing systems relies on thousands of billions of heterogeneous processing cores connected to form a single computing unit. In such large-scale computing environments, resource management is one of the most challenging, and complex issues for efficient resource sharing and utilization, particularly as we move toward Future ManyCore Systems (FMCS). This work proposes a novel resource management scheme for future peta-scale many-core-enabled computing systems, based on hybrid adaptive resource discovery, called ElCore. The proposed architecture contains a set of modules which will dynamically be instantiated on the nodes in the distributed system on demand. Our approach provides flexibility to allocate the required set of resources for various types of processes/applications. It can also be considered as a generic solution (with respect to the general requirements of large scale computing environments) which brings a set of interesting features (such as auto-scaling, multitenancy, multi-dimensional mapping, etc,.) to facilitate its easy adaptation to any distributed technology (such as SOA, Grid and HPC many-core). The achieved evaluation results assured the significant scalability and the high-quality resource mapping of the proposed resource discovery and management over highly heterogeneous, hierarchical and dynamic computing environments with respect to several scalability and efficiency aspects while supporting flexible and complex queries with guaranteed discovery results accuracy. The simulation results prove that, using our approach, the mapping between processes and resources can be done with high level of accuracy which potentially leads to a significant enhancement in the overall system performance.

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Section: Main Requirementsmentioning

confidence: 99%

ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems

Zarrin

Aguiar

Barraca

2016

Microprocessors and Microsystems

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“…Allocating system resources to the tasks of multiple applications on on-chip many-core system has been an emerging research direction [27], [33]. Several resource allocation approaches have been proposed while following different policies.…”

Section: Related Workmentioning

confidence: 99%

Bubble budgeting: throughput optimization for dynamic workloads by exploiting dark cores in many core systems

Wang

Singh

et al. 2016

2016 Tenth IEEE/ACM International Symposium on Networks-on-Chip (NOCS)

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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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“…In our current implementation, we used a Genetic Algorithm (GA)-based technique as conducted in Marwedel et al [2011]. We define Schd k ( p) as a throughput-maximal schedule of application T k mapped to p processors.…”

Section: Compile-time Analysismentioning

confidence: 99%

Dynamic Behavior Specification and Dynamic Mapping for Real-Time Embedded Systems

Jung

Lee

Kang

et al. 2014

ACM Trans. Embed. Comput. Syst.

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As the number of processors in a chip increases and more functions are integrated, the system status will change dynamically due to various factors such as the workload variation, QoS requirement, and unexpected component failure. A typical method to deal with the dynamics of the system is to decide the mapping decision at runtime, based on the local information of the system status. It is very challenging to guarantee any realtime performance of a certain application in such a dynamically varying system. To solve this problem, we propose a hybrid specification of dataflow and FSM models to specify the dynamic behavior of a system distinguishing inter-and intra-application dynamism. At the top level, each application is specified by a dataflow task and the dynamic behavior is modeled as a control task that supervises the execution of applications. Inside a dataflow task, we specify the dynamic behavior using a similar way as FSM-based SADF in which an application is specified by a synchronous dataflow graph for each mode of operation. It enables us to perform compile-time scheduling of each graph to maximize the throughput varying the number of allocated processors, and store the scheduling information. When a change in system state is detected at runtime, the number of allocated processors to the active tasks is determined dynamically utilizing the stored scheduling information of those tasks in order to meet the real-time requirements. The proposed technique is implemented in the HOPES design environment. Through preliminary experiments with a simple smartphone example, we show the viability of the proposed methodology. Categories and Subject Descriptors: [Models of Computation]: Timed and Hybrid Models General Terms: Design, Performance Additional Key Words and Phrases: Model-based design, dynamic mapping ACM Reference Format: Hanwoong Jung, Chanhee Lee, Shin-Haeng Kang, Sungchan Kim, Hyunok Oh, and Soonhoi Ha. 2014. Dynamic behavior specification and dynamic mapping for real-time embedded systems: HOPES approach.

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Mapping of applications to MPSoCs

Cited by 42 publications

References 41 publications

ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems

ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems

Bubble budgeting: throughput optimization for dynamic workloads by exploiting dark cores in many core systems

Dynamic Behavior Specification and Dynamic Mapping for Real-Time Embedded Systems

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