Thermal aware task assignment for multicore processors using genetic algorithm (original) (raw)
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Task Allocation and Migration Algorithm for Temperature-Constrained Real-Time Multi-Core Systems
2010
Temperature rise will affect the stability and performance of multi-core processors. A temperature-aware task scheduling algorithm for real-time multi-core systems, called LTEDF (Low Thermal Early Deadline First), is proposed in this paper. In LTEDF, a HCNF ( History Coolest Neighborhood First) task allocation algorithm is employed to balance the loads. When some cores are thermally saturated, task migration is performed to alleviate thermal saturation to get more uniform power density map. A thermal aware multicore scheduling simulator(TAMSS) based on HotSpot thermal model is implemented. TAMSS simulation results show that LTEDF algorithm can not only meet real-time guarantee, but also minimize the thermal penalty. Moreover, it can create a more uniform power density map than other thermalaware algorithms, and significantly reduce thread migration frequency.
Optimum: Thermal-aware task allocation for heterogeneous many-core devices
2014 International Conference on High Performance Computing & Simulation (HPCS), 2014
Temperature management is a key challenge for many-core platforms in the dark silicon era as all the cores cannot be powered-on together at the maximum frequency and either some cores should run at lower frequency or only a portion can be used without burning the device. In addition, due to process variations and/or design optimization, not all the integrated processing elements (PEs) are identical and each of them may feature a different power/temperature/frequency trade-off. Many works have been proposed to tackle the thermalaware task mapping problem in multicore devices, but none has yet demonstrated the capability to find optimal solutions within seconds for a large number of cores, with heterogeneous power/frequency operating points, while ensuring a safe transient thermal map. In this paper we propose a new Integer Linear Programming formulation, based on a coarse-grain dynamic thermal model, for this class of problems. Our solver finds optimal solutions in few seconds for a 64 core system. Furthermore, we show that by limiting the number of iterations in the solver, we achieve low optimality gaps, with times compatible to an on-line (execution time) use of the optimal allocator.
Thermal-aware scheduling for future chip multiprocessors
EURASIP Journal on Embedded …, 2007
The increased complexity and operating frequency in current single chip microprocessors is resulting in a decrease in the performance improvements. Consequently, major manufacturers offer chip multiprocessor (CMP) architectures in order to keep up with the expected performance gains. This architecture is successfully being introduced in many markets including that of the embedded systems. Nevertheless, the integration of several cores onto the same chip may lead to increased heat dissipation and consequently additional costs for cooling, higher power consumption, decrease of the reliability, and thermal-induced performance loss, among others. In this paper, we analyze the evolution of the thermal issues for the future chip multiprocessor architectures and show that as the number of on-chip cores increases, the thermal-induced problems will worsen. In addition, we present several scenarios that result in excessive thermal stress to the CMP chip or significant performance loss. In order to minimize or even eliminate these problems, we propose thermal-aware scheduler (TAS) algorithms. When assigning processes to cores, TAS takes their temperature and cooling ability into account in order to avoid thermal stress and at the same time improve the performance. Experimental results have shown that a TAS algorithm that considers also the temperatures of neighboring cores is able to significantly reduce the temperature-induced performance loss while at the same time, decrease the chip's temperature across many different operation and configuration scenarios.
Efficient Implementation of Thermal-Aware Scheduler on a Quad-core Processor
2011
Due to power wall and slow performance improvement in a single core micro-architecture, multiple even many cores based processors rose as the main stream processor. Nevertheless, thermal threats regarding reliability and lifetime of processors are still among the major concerns which received much attention in terms of algorithms and hardware design to reduce processor temperature and keep application performance in recent years. In this paper, we propose and implement a thermal-aware Round-Robin scheduling algorithm for process migration in the Linux environment on a quad-core processor. Bearing designer's goals in mind, such as performance, load-balancing, and reliability, we managed to achieve much bigger temperature fall than previous results of Round-Robin scheduler on a dual-core processor as well as baseline Linux scheduler on a quad-core processor. Moreover, the performance loss due to scheduling overhead is modest in our approach. Our results indicate that thermal-aware scheduling is a valid approach to tackling thermal issues on multi-core processors. There will be increasing demand for thermal-aware scheduling as the number of cores on a single processor increases.
A Multi-Agent Based Thermal Aware Task Migration Scheme in Multi-Core System
As feature sizes decrease, power dissipation and heat generation density exponentially increase. Thus, thermal hot spots and large temperature gradients in Multiprocessor Systems on Chip (MPSoCs) can seriously impact the system performance, reliability, cost, and leakage power. Increasing complexity of the system makes it more difficult to perform thermal management in a centralized manner. In this paper, a framework is proposed for distributed thermal management in many-core systems where balanced thermal profile can be achieved by proactive task migration among neighboring cores. The framework has a low cost agent residing in each core that observes the local workload and temperature and communicates with its nearest neighbor for task migration and exchange. By choosing only those migration requests that will result in balanced workload without generating thermal emergency, the proposed framework maintains workload balance across the system and avoids unnecessary migration. Compared with existing proactive task migration technique, the approach used in this paper generates less hotspot, less migration overhead with negligible performance overhead.
IEEE Transactions on Parallel and Distributed Systems, 2016
This paper proposes a static multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific techniques for solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, total energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and architecture pair. We also propose a methodology to select one solution from the Pareto front given the user's preference. The proposed algorithm for solving the task to core scheduling effectively achieves 3-way optimization and does so with fast turnaround time. We show that the proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. The proposed algorithm is evaluated using both implementation and simulation and is compared with integer linear programming solutions as well as with other scheduling algorithms that are energy-or thermal-aware. The time complexity of the proposed scheme is also considerably better than the compared algorithms.
Physical-aware task migration algorithm for dynamic thermal management of SMT multi-core processors
2014
This paper presents a task migration algorithm for dynamic thermal management of SMT multi-core processors. The unique features of this algorithm include: 1) considering SMT capability of the processors for task scheduling, 2) using adaptive task migration threshold, and 3) considering cores physical features. This algorithm is evaluated on a commercial SMT quad-core processor. The experimental results indicate that our technique can significantly decrease the average and peak temperature compared to Linux standard scheduler, and two well-known thermal management techniques.
An online temperature-aware scheduling technique to avoid thermal emergencies in multiprocessor systems, 2019
Reliability, performance and power consumption of a real-time multiprocessor system are negatively affected by high temperatures, spatial gradients and thermal cycles. Software-based load balancing techniques have been used for thermal management. The transfer of workload in such techniques is based on temperature estimation. In this paper, we proposed an online temperature-aware scheduling technique that performs load balancing based on dynamic temperature measurement at a fixed ambient temperature. Contrary to the static techniques that utilize the principle of temperature prediction, the proposed technique does not require any thermal history of workload and is effective for any kind of workload without prior knowledge. Moreover, it reduces the energy consumption and avoids the workload switching delays among the cores. The simulation results show that the technique reduces overall temperature up to 5%, thermal cycles up to 3% and lowers the temporal and spatial gradients compared to the commonly used techniques .
Architecture-aware Task-scheduling: A thermal approach
2011
Current task-centric many-core schedulers share a "naive" view of processor architecture; a view that does not care about its thermal, architectural or power consuming properties. Future processor will be more heterogeneous than what we see today, and following Moore's law of transistor doubling, we foresee an increase in power consumption and thus temperature. Thermal stress can induce errors in processors, and so a common way to counter this is by slowing the processor down; something task-centric schedulers should strive to avoid. The Thermal-Task-Interleaving scheduling algorithm proposed in this paper takes both the application temperature behavior and architecture into account when making decisions. We show that for a mixed workload, our scheduler outperforms some of the standard, architecture-unaware scheduling solutions existing today.
Thermal-aware resource allocation in earliest deadline first using fluid scheduling
International Journal of Distributed Sensor Networks, 2019
Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-pro...