Communication-aware task assignment algorithm for MPSoC using shared memory

Lee, Ser-Hoon; Yoon, Yeo-Chan; Hwang, Sung Yeon

doi:10.1016/j.sysarc.2010.03.001

Cited by 12 publications

(8 citation statements)

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“…Ser-Hoon Lee etc. [3] addressed a task assignment algorithm and switch structure for MPSoC using shared memory. Their switch supports simultaneous access from in/out port to the shared memory and their algorithm ensures the data-access-intensive task to be assigned close to the shared memory.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Motivation. When multiple-application workload running on the MPSoC includes streaming applications, adopting novel switch and multi-port memory [3] becomes a practical way to improve the system performance by enabling data packets from different in/out ports to be simultaneously transferred through memory ports. Fig.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Platform description. The NoC structure in this paper follows the 2D-Mesh shape and adopts the performance enhanced switch and multi-port shared memory proposed in [3]. As such switch is used to increase the transmission bandwidth of the shared memory, it should be located on the position where the number of paths between a switch and PEs is the largest to maximize the system performance.…”

Section: Preliminariesmentioning

confidence: 99%

“…The simultaneous accesses to the shared memory, a specific module in MPSoC, bring great contention, thus increasing communication congestion. Scholars devised performance-enhanced switch to connect a multi-port shared memory and corresponding task assignment algorithm [3]. Their enhanced architecture enables simultaneous accesses to the shared memory being served at the same time, reducing the communication congestion.…”

Section: Introductionmentioning

confidence: 99%

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Task Assignment for Multiple-Application Workload with Streaming Ones in MPSoC Using Shared Memory

Zuo

2012

AMM

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Large scale Multi-Processor System-on-a-chip (MPSoC) based on Network on Chip (NoC) can support multiple applications running simultaneously. When the multiple-application workload includes streaming applications processing massive data, the communication concentrated on shared memory can't be ignored. In this paper, we propose a task assignment strategy for multiple-application workload which includes one streaming application on a NoC-based MPSoC. The proposed algorithm first assigns the streaming application centering the multi-port shared memory, and then assigns the other applications minimizing external communication congestion. By adopting the proposed algorithm, the memory-contention tasks are assigned to the PEs close to the shared memory and the overall congestion is minimized. This allows the system to provide better overall performance.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Task Assignment for Multiple-Application Workload with Streaming Ones in MPSoC Using Shared Memory

Zuo

2012

AMM

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“…Uma proposta de mapeamento de tarefas baseado em conhecimento de comunicação foi proposta em [18]. A abordagem é focada na prioridade de acesso à memória compartilhada quando a tarefa tem uma grande quantidade de comunicação.…”

Section: Trabalhos Relacionadosunclassified

A Greedy Heuristic for Process Mapping on Networks-on-Chip

Oliveira

Avelar

Guimarães

et al. 2011

Anais Do XII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2011)

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The state-of-the-art related to many-core processors focuses on Networks-on-Chip (NoCs) as approach to provide on-chip message-passing communication. The main problem is the number of hops from source to destination increasing the latency to exchange data based on network packets. Our goal is to propose a greedy heuristic for process mapping on NoCs clustering processes that have more communication on nearest cores. The evaluation method is based on simulation of parallel workloads on a NoC. The greedy heuristic achieves a higher throughput (up to 23.04%) and lower energy consumption (up to 9.87%) than a direct mapping approach for most workloads.

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