Multi-objective exploitation of pipeline parallelism using clustering, replication and duplication in embedded multi-core systems

Lin, Chih‐Sheng; Lin, Chao-Sheng; Lin, Yu-Shin; Hsiung, Pao-Ann; Shih, Chihhsiong

doi:10.1016/j.sysarc.2013.05.024

Cited by 30 publications

(20 citation statements)

References 8 publications

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“…The third set of simulation shows the performance of the EATSDCD algorithm with CCR = 1 and throughput set to 0.25Th max (G), 0.5Th max (G) and 0.75Th max (G). against previously proposed schemes: PaPilo [41], TCLO & TCLO-P [8]. The results are shown in Table 6.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against PALS [20] and PATC [20] algorithms without performance loss, respectively. Furthermore, there is 16% improvement over PaPilo [41] algorithm with En cur = 1.2En min (G). In comparison with RASD [33] and HEFT [51] algorithm, the execution time has been reduced in heterogeneous environments.…”

Section: Discussionmentioning

confidence: 97%

“…Target platform Scheduling objective TDGA [11] Homo/heter Schedule length Load balancing satisfaction RASD [33] Heter Reliability Makespan CA-D [6] Homo/heter Speed up Energy consumption AES [34] Homo Performance Schedule length EPTAC [2] Homo Energy consumption CPU utilization ASA [35] Homo Makespan EAMD [22] Heter Energy consumption Cost Reliability Makespan CPFD [36], PY [37], LWB [38], BTDH [39], DSH [40] Homo/heter Efficiency Cost Normalized scheduling length TCLO [8] Homo Latency Throughput Power consumption SDS [54] Homo Schedule length Number of processors NEADS [43] Homo Makespan Energy consumption PaPIio [41] Homo Latency Throughput Power Table 1 summarizes the previous studies on scheduling policies with duplicate technique for allocating tasks on processors with different objectives. We presented algorithms in each reference, while target platforms can be classified to homogeneous and heterogeneous environments and scheduling objectives.…”

Section: Referencementioning

confidence: 99%

“…The proposed model for evaluation of throughput has been adopted from [41]. It is essential to include communication rate (CommR(G)) and computation rate (CompR(G)) for the given DAG, G.…”

Section: Throughput Estimationmentioning

confidence: 99%

“…This section presents the experiments carried out to evaluate the proposed heuristic algorithm, Energy-Aware Task Scheduling with Duplication Clustering Dynamic voltage/frequency Algorithm (EATSDCD) and compare EATSDCD against previous work, namely power aware task clustering (PATC) [20], power aware list-based scheduling (PALS) [20], Energy Aware Duplication Scheduling (EADUS) & TEBUS [44] with objective energy saving, RASD [33], Heterogeneous Earliest Finish Time (HEFT) [51] with objective execution time and PaPilo [41], Throughput Constrained Latency Optimization heuristic (TCLO) & Throughput Constrained Latency Optimization Pipelined (TCLO-P) [8] in homogeneous and heterogeneous environments while considering energy consumption and throughput.…”

Section: Performance Analysis With Simulationmentioning

confidence: 99%

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EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Barzegar

Motameni

Movaghar

2019

IFS

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Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with En cur = 1.2En min (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 97%

Section: Referencementioning

confidence: 99%

“…The proposed model for evaluation of throughput has been adopted from [41]. It is essential to include communication rate (CommR(G)) and computation rate (CompR(G)) for the given DAG, G.…”

Section: Throughput Estimationmentioning

confidence: 99%