Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges

AdaMD: Adaptive Mapping and DVFS for Energy-efficient Heterogeneous Multi-cores

Modern heterogeneous multi-core systems, containing various types of cores, are increasingly dealing with concurrent execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread-to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multi-threaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints

Компьютерное сопровождение учебного процесса

Thermal cycling as well as temperature gradient in time and space affects the lifetime reliability and performance of heterogeneous multiprocessor systems-on-chips (MPSoCs). Conventional temperature management techniques are not intelligent enough to cater for performance, energy efficiency as well as operating temperature of the system. In this paper we propose a light-weight novel thermal management mechanism in the form of intelligent software agent, which monitors and regulates the operating temperature of the CPU cores to improve reliability of the system. We validated our methodology on the Odroid-XU4 SoC and it has been successful to reduce the operating temperature by 6.32% while improving performance by 7.96% and reducing power consumption by 9.45% than the state-of-the-art.</p

Energy efficient run-time mapping and thread partitioning of concurrent OpenCL applications on CPU-GPU MPSoCs

Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. However, as will be shown in this paper, existing approaches are not well suited for concurrent applications as they are developed either by considering only a single application or they do not exploit both CPU and GPU cores at the same time. In this paper, we propose an energy-efficient run-time mapping and thread partitioning approach for executing concurrent OpenCL applications on both GPU and GPU cores while satisfying performance requirements. Depending upon the performance requirements, for each concurrently executing application, the mapping process finds the appropriate number of CPU cores and operating frequencies of CPU and GPU cores, and the partitioning process identifies an efficient partitioning of the applications’ threads between CPU and GPU cores. We validate the proposed approach experimentally on the Odroid-XU3 hardware platform with various mixes of applications from the Polybench benchmark suite. Additionally, a case-study is performed with a real-world application SLAMBench. Results show an average energy saving of 32% compared to existing approaches while still satisfying the performance requirements

Dynamic Energy and Thermal Management of Multi-Core Mobile Platforms: A Survey

Multi-core mobile platforms are on rise as they enable efficient parallel processing to meet ever-increasing performance requirements. However, since these platforms need to cater for increasingly dynamic workloads, efficient dynamic resource management is desired mainly to enhance the energy and thermal efficiency for better user experience with increased operational time and lifetime of mobile devices. This article provides a survey of dynamic energy and thermal management approaches for multi-core mobile platforms. These approaches do either proactive or reactive management. The upcoming trends and open challenges are also discussed

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM's big.LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Temperature has also become a critical parameter since it affects reliability, power consumption, and performance and, hence, must be managed. This paper proposes an accurate temperature prediction scheme coupled with a runtime energy management approach to proactively avoid exceeding temperature thresholds while maintaining performance targets. Experiments show up to 20% energy savings while maintaining high-temperature averages and peaks below the threshold. Compared with state-of-the-art temperature predictors, this paper predicts 35% faster and reduces the mean absolute error from 3.25 to 1.15 °C for the evaluated applications' scenarios

The PE-PPE Domain in Mycobacterium Reveals a Serine α/β Hydrolase Fold and Function: An In-Silico Analysis

The PE and PPE proteins first reported in the genome sequence of Mycobacterium tuberculosis strain H37Rv are now identified in all mycobacterial species. The PE-PPE domain (Pfam ID: PF08237) is a 225 amino acid residue conserved region located towards the C-terminus of some PE and PPE proteins and hypothetical proteins. Our in-silico sequence analysis revealed that this domain is present in all Mycobacteria, some Rhodococcus and Nocardia farcinica genomes. This domain comprises a pentapeptide sequence motif GxSxG/S at the N-terminus and conserved amino acid residues Ser, Asp and His that constitute a catalytic triad characteristic of lipase, esterase and cutinase activity. The fold prediction and comparative modeling of the 3-D structure of the PE-PPE domain revealed a “serine α/β hydrolase” structure with a central β-sheet flanked by α-helices on either side. The structure comprises a lid insertion with a closed structure conformation and has a solvent inaccessible active site. The oxyanion hole that stabilizes the negative charge on the tetrahedral intermediate has been identified. Our findings add to the growing list of serine hydrolases in mycobacterium, which are essential for the maintenance of their impermeable cell wall and virulence. These results provide the directions for the design of experiments to establish the function of PE and PPE proteins

Online concurrent workload classification for multi-core energy management

Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V-f ). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V-f setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V-f to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches