Optimizing Task and Data Assignment on Multi-Core Systems with Multi-Port SPMs

Gu, Shuhang; Zhuge, Qingfeng; Yi, Juan; Hu, Jingtong; Sha, Edwin Hsing-Mean

doi:10.1109/tpds.2014.2356194

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…In recent years, the research of joint consideration of TSDA has received lots of attention (see Table 2). For homogeneous architecture situations, there have been many studies on TSDA 9,10,39,40,42,45 . Wang et al 9 investigated the energy optimization for TSDA problem with hybrid SRAM+NVM SPM.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al 10 studied the problem of reducing the execution costs and lowering the number of write operations for dynamic data allocation and task scheduling on multiprocessor systems with NVM‐based SPM. Gu et al 39 proposed an ILP formulation and a heuristic algorithm to optimize task and data assignment on multicore systems with multiport SPMs. However, many problems in reality are heterogeneous architecture, and the problem becomes more complicated in the heterogeneous case than that in the homogeneous case.…”

Section: Related Workmentioning

confidence: 99%

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Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Deng

Shen

Cao

et al. 2021

Concurrency and Computation

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Energy consumption has become one of the main bottlenecks that limit the performance improvement of heterogeneous multiprocessor systems. In a heterogeneous distributed shared‐memory multiprocessor system (HDSMS), each processor can access all the memories, and each data can be stored in different memories. This article aims at addressing the problem of task scheduling and data allocation (TSDA) on HDSMS. To minimize the total energy consumption under a time constraint for TSDA, we propose two algorithms: the extended tree assignment for task scheduling incorporating data allocation (ETATS‐DA) and critical path task scheduling and data allocation (CPTSDA). The ETATS‐DA algorithm first utilizes the extended tree assignment to search the near optimal solution for task assignment, and then allocates data to memory based on the result of assignment. The CPTSDA algorithm considers TSDA jointly on a critical path simultaneously. Our proposed algorithms perform coherent data allocation under the consideration of best task scheduling by running two different heuristic strategies, respectively, and taking the best result as the final result. We conduct a large number of simulation experiments to test the performance of our algorithms, and the results validate the higher performance of our methods compared with the state‐of‐the‐art algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Deng

Shen

Cao

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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“…The required tasks and data are loaded at the system initialization stage and remain unchanged during the execution. For example, Gu et al [4] studied static task assignments and data allocation on multicore systems with multi-port SPMs to minimize the cost. The authors also formulated the problem as an integer linear programming and used a heuristic algorithm to solve it.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Data Allocation and Task Scheduling on Multiprocessor Systems With NVM-Based SPM

Wang

2019

IEEE Access

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Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

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“…However, like Suhendra et al [2010], they assume that task mapping to cores is known a priori. Guo et al [2011Guo et al [ , 2013 proposed dynamic programming approaches to place data in VS-SPM to reduce the total time of memory accesses and energy consumption. However, they assume that applications are partitioned a priori into parallel regions and for each region the mapping of threads to cores are known.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [2010] proposed two heuristic algorithms to jointly schedule/map tasks and to partition the variables of an application running on a VS-SPM-based multicore processor. Gu et al [2013Gu et al [ , 2014 considered joint variable partitioning and task scheduling for a multibank [Gu et al 2013] and multiport [Gu et al 2014] VS-SPM-based multicore processor and proposed ILP and heuristic methods. Like [Suhendra et al 2006], the last two mentioned works did not consider SPM space reuse to further reduce execution time.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Shared SPM Reuse for Real-Time Multicore Embedded Systems

Kafshdooz

Ejlali

2015

ACM Trans. Archit. Code Optim.

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Allocating the scratchpad memory (SPM) space to tasks is a challenging problem in real-time multicore embedded systems that use shared SPM. Proper SPM space allocation is important, as it considerably influences the application worst-case execution time (WCET), which is of great importance in real-time applications. To address this problem, in this article we present a dynamic SPM reuse scheme, where SPM space can be reused by other tasks during runtime without requiring any static SPM partitioning. Although the proposed scheme is applied dynamically at runtime, the required decision making is fairly complex and hence cannot be performed at runtime. We have developed techniques to perform the decision making offline at design time in the form of optimization problems combined with task scheduling/mapping. The proposed work is unlike previous works that either exploit static schemes for SPM space allocation or perform task scheduling/mapping and SPM space allocation incoherently. The experimental results show that our dynamic SPM reuse scheme can reduce WCET by up to 55% as compared to recent previous works on SPM allocation in real-time multicore embedded systems.

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Optimizing Task and Data Assignment on Multi-Core Systems with Multi-Port SPMs

Cited by 16 publications

References 27 publications

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Dynamic Data Allocation and Task Scheduling on Multiprocessor Systems With NVM-Based SPM

Dynamic Shared SPM Reuse for Real-Time Multicore Embedded Systems

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