Adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor real-time systems

Saha, Shivashis; Deogun, Jitender S.; Lü, Ying

doi:10.1109/hpcsim.2012.6266904

Cited by 4 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many previous works in the context of multi-processor systems either propose energy reduction management techniques without considering reliability (e.g., [14], [15], [32], [33]) or consider reliability without considering energy consumption (e.g., [4], [30], [34]). [14] has considered variation in execution times to propose a scheduling algorithm based on dynamic voltage scaling (DVS) [12] for multi-processor systems.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

Salehi

Ejlali

Al-Hashimi

2016

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract-This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multicore systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energymanagement technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29% energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work.

show abstract

Section: Related Workmentioning

confidence: 99%

“…[32] has proposed a technique to minimize chiplevel peak power consumption in multi-core systems running sporadic real-time tasks. [33] has proposed an adaptive task partitioning for multi-core systems running independent periodic real-time tasks. [4] has evaluated scheduling heuristics for tasks with different criticality.…”

Section: Related Workmentioning

confidence: 99%

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

Salehi

Ejlali

Al-Hashimi

2016

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…The energy efficiency benefits of heterogeneity can only be exploited with the correct assignment of tasks or applications to each core [7], [10], [30]- [32]. Tasks must be assigned in order to maximise energy efficiency whilst ensuring performance deadlines are met.…”

Section: B Energy Efficiency Techniques In Heterogeneous Multi-core mentioning

confidence: 99%

“…Alternatively, Calcado et al propose division of tasks into mthreads to introduce fine-grain parallelism below thread level [33]. Moreover, Saha et al include power and temperature models into an adaptive task partitioning mechanism in order to allocate task according to their actual utilisations rather than based on a worst case execution time [10]. Simulation results confirm that the mechanism is effective in minimising energy consumption by 55% and reduces task migrations by 60% over alternative task partitioning schemes.…”

Section: B Energy Efficiency Techniques In Heterogeneous Multi-core mentioning

confidence: 99%

Energy Efficient Multi-Core Processing

Leech¹,

Kázmierski

2014

ELS

View full text Add to dashboard Cite

Abstract-This paper evaluates the present state of the art of energy-efficient embedded processor design techniques and demonstrates, how small, variable-architecture embedded processors may exploit a run-time minimal architectural synthesis technique to achieve greater energy and area efficiency whilst maintaining performance. The picoMIPS architecture is presented, inspired by the MIPS, as an example of a minimal and energy efficient processor. The picoMIPS is a variablearchitecture RISC microprocessor with an application-specific minimised instruction set. Each implementation will contain only the necessary datapath elements in order to maximise area efficiency. Due to the relationship between logic gate count and power consumption, energy efficiency is also maximised in the processor therefore the system is designed to perform a specific task in the most efficient processor-based form. The principles of the picoMIPS processor are illustrated with an example of the discrete cosine transform (DCT) and inverse DCT (IDCT) algorithms implemented in a multi-core context to demonstrate the concept of minimal architecture synthesis and how it can be used to produce an application specific, energy efficient processor.

show abstract

“…Typical workloads exhibit different levels of parallelism, e.g., Instruction Level Parallelism (ILP) and Thread Level Parallelism (TLP). In this work, we model a program as a single sequence of tasks [35] [36]. Each task can exhibit its own parallelism as shown in the example in Figure 1: two programs, each of which consists of a sequence of tasks with arbitrary execution times.…”

Section: Abstraction Level and Queueing Theorymentioning

confidence: 99%

Online Real-Time Task Scheduling in Heterogeneous Multicore System-on-a-Chip

Chen

Liao

Tsai

2013

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

In recent years, as the demand for low energy and high performance computing has steadily increased, heterogeneous computing has emerged as an important and promising solution. Because most w orkloads can typically run most efficiently on certain types of cores, mapping tasks on the best available resources can not only save energy but also deliver high performance. How ever, optimal task scheduling for performance and/or energy is yet to be solved for heterogeneous platforms. The w ork presented herein mathematically formulates the optimal heterogeneous system task scheduling as an optimization problem using queueing theory. We analytically solve for the common case of tw o processor types, e.g., CPU+GPU, and give an optimal policy (CAB). We design the GrIn heuristic to efficiently solve for near-optimal policy for any number of processor types (within 1.6% of the optimal). Both policies w ork for any task size distribution and processing order, and are therefore, general and practical. We extensively simulate and validate the theory, and implement the proposed policy in a CPU-GPU real platform to show the optimal throughput and energy improvement. Comparing to classic policies like load-balancing, our results range from 1.08x~2.24x better performance or 1.08x~2.26x better energy efficiency in simulations, and 2.37x~9.07x better performance in experiments.

show abstract

Adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor real-time systems

Cited by 4 publications

References 32 publications

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

Energy Efficient Multi-Core Processing

Online Real-Time Task Scheduling in Heterogeneous Multicore System-on-a-Chip

Contact Info

Product

Resources

About