scholarly journals Coordinated Energy Management in Heterogeneous Processors

2014 ◽  
Vol 22 (2) ◽  
pp. 93-108 ◽  
Author(s):  
Indrani Paul ◽  
Vignesh Ravi ◽  
Srilatha Manne ◽  
Manish Arora ◽  
Sudhakar Yalamanchili
Keyword(s):  
Performance Management ◽  
Energy Management ◽  
Performance Optimization ◽  
High Performance ◽  
Average Energy ◽  
Management Algorithm ◽  
Tightly Coupled ◽  
Heterogeneous Processor ◽  
And Performance ◽  
Gpu Architectures

This paper examines energy management in a heterogeneous processor consisting of an integrated CPU–GPU for high-performance computing (HPC) applications. Energy management for HPC applications is challenged by their uncompromising performance requirements and complicated by the need for coordinating energy management across distinct core types – a new and less understood problem. We examine the intra-node CPU–GPU frequency sensitivity of HPC applications on tightly coupled CPU–GPU architectures as the first step in understanding power and performance optimization for a heterogeneous multi-node HPC system. The insights from this analysis form the basis of a coordinated energy management scheme, called DynaCo, for integrated CPU–GPU architectures. We implement DynaCo on a modern heterogeneous processor and compare its performance to a state-of-the-art power- and performance-management algorithm. DynaCo improves measured average energy-delay squared (ED2) product by up to 30% with less than 2% average performance loss across several exascale and other HPC workloads.

Autonomic power and performance management of high-performance servers

2008 ◽  
Author(s):  
Bithika Khargharia ◽  
Salim Hariri ◽  
Wael Kdouh ◽  
Manal Houri ◽  
Hesham El-Rewini ◽  
...  
Keyword(s):  
Performance Management ◽  
High Performance ◽  
And Performance

scholarly journals MG-RAST version 4—lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

2017 ◽  
Vol 20 (4) ◽  
pp. 1151-1159 ◽  
Author(s):  
Folker Meyer ◽  
Saurabh Bagchi ◽  
Somali Chaterji ◽  
Wolfgang Gerlach ◽  
Ananth Grama ◽  
...  
Keyword(s):  
Performance Optimization ◽  
High Performance ◽  
Lessons Learned ◽  
Added Value ◽  
Data Sets ◽  
Real World Data ◽  
Metagenome Analysis ◽  
Trade Offs ◽  
And Performance ◽  
The Right

Abstract As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.

scholarly journals Management Processes as Antecedents of Organizational Performance: A Literature Review

2021 ◽  
Vol 7 (1) ◽  
pp. 1-6
Author(s):  
Mohammed Ibrahim Abubakar
Keyword(s):  
Literature Review ◽  
Strategic Management ◽  
Organizational Performance ◽  
Performance Management ◽  
High Performance ◽  
Services Marketing ◽  
Marketing Management ◽  
Management Tools ◽  
Management Processes ◽  
And Performance

The purpose of this study is to critically analyse previous studies on management processes as antecedents of organizational performance. The study summarizes the level of understanding as regards the topic presently because of the importance of the information to the performance of organizations. The author searched Emerald, ScienceDirect.com, EBSCO and Google Scholar using a series of combinations of the following keywords: organizational management, performance management, high-performance organizations, management processes, management tools, influences of management processes, strategic management, marketing management, services marketing mix, , business organization and performance. This literature review has shown that performance is critical for the survival of the organization. It has also revealed that strategic management processes, marketing management processes and services marketing are key to organizational performance.

Sleep – A Game Changer in the Athletic World?

2018 ◽  
Vol 66 (4) ◽  
Keyword(s):  
Performance Optimization ◽  
High Performance ◽  
Recovery Process ◽  
Poor Sleep ◽  
Recovery Method ◽  
Lack Of Sleep ◽  
And Performance ◽  
The Individual ◽  
Traditional Approaches ◽  
And Control

The restorative qualities of sleep are fundamentally the basis of the individual athlete’s ability to recover and perform, and to optimally be able to challenge and control the effects of exercise regimes in high performance sport. Research consistently shows that a large percentage of the population fails to obtain the recommended 7–9 hours of sleep per night [17]. Moreover, recent years’ research has found that athletes have a high prevalence of poor sleep quality [6]. Given its implications on the recovery process, sleep affects the quality of the athlete’s training and outcome of competitions. Although an increasing number of recovery aids (such as cold baths, anti-inflammatory agents, high protein intake etc.) are available, recent years research show the important and irreplaceable role of sleep and that no recovery method can compensate for the lack of sleep. Every facet of an athlete’s life has the capacity to either create or take out energy, contribute to the overall stress level and subsequently the level of both recovery and performance. While traditional approaches to performance optimization focus simply on the physical stressors, this overview will highlight the benefits and the basic principles of sleep, its relation to recovery and performance, and provide input and reflect on what to consider when working with development and maintenance of athletic performance.

scholarly journals HCGrid: A convolution-based gridding framework for radio astronomy in hybrid computing environments

2020 ◽  
Author(s):  
Hao Wang ◽  
Ce Yu ◽  
Bo Zhang ◽  
Jian Xiao ◽  
Qi Luo
Keyword(s):  
High Performance ◽  
Optimal Parameter ◽  
Computing Time ◽  
Reduction Process ◽  
Hybrid Computing ◽  
Convolution Process ◽  
Computing Environments ◽  
And Performance ◽  
Gpu Architectures ◽  
Key Steps

Abstract Gridding operation, which is to map non-uniform data samples on to a uniformly distributed grid, is one of the key steps in radio astronomical data reduction process. One of the main bottlenecks of gridding is the poor computing performance, and a typical solution for such performance issue is the implementation of multi-core CPU platforms. Although such a method could usually achieve good results, in many cases, the performance of gridding is still restricted to an extent due to the limitations of CPU, since the main workload of gridding is a combination of a large number of single instruction, multi-data-stream operations, which is more suitable for GPU, rather than CPU implementations. To meet the challenge of massive data gridding for the modern large single-dish radio telescopes, e.g. the Five-hundred-meter Aperture Spherical radio Telescope (FAST), inspired by existing multi-core CPU gridding algorithms such as Cygrid, here we present an easy-to-install, high-performance, and open-source convolutional gridding framework, HCGrid, in CPU-GPU heterogeneous platforms. It optimises data search by employing multi-threading on CPU, and accelerates the convolution process by utilising massive parallelisation of GPU. In order to make HCGrid a more adaptive solution, we also propose the strategies of thread organisation and coarsening, as well as optimal parameter settings under various GPU architectures. A thorough analysis of computing time and performance gain with several GPU parallel optimisation strategies show that it can lead to excellent performance in hybrid computing environments.

scholarly journals Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train

2021 ◽  
Vol 11 (24) ◽  
pp. 11952
Author(s):  
Xu Zhou ◽  
Tao Wen ◽  
Zhiqiang Long
Keyword(s):  
Real Time ◽  
Performance Optimization ◽  
High Performance ◽  
Suspension System ◽  
Control Performance ◽  
Real Time Monitoring ◽  
Maglev Train ◽  
Dynamic Compensator ◽  
And Performance ◽  
Residual Generator

With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness.

scholarly journals A Study of Performance Management System as a Strategic Tool of HRM

2020 ◽  
Vol 3 (10) ◽  
pp. 155-157
Author(s):  
Ashwini Walhekar ◽  
Anita Khatke
Keyword(s):  
Performance Management ◽  
Human Resource ◽  
Management System ◽  
High Performance ◽  
Performance Management System ◽  
Traditional Role ◽  
High Performer ◽  
Effective Performance ◽  
And Performance ◽  
New System

In present hard-hitting competition, one of the strategies to be a successful organization is to get right candidates for every available position in the organization and retain the good employees to have better and highly motivated workforce. So what actually needed for an organization and managers is to attract, retain and motivate a talented workforce? It is proven fact that all high performance organizations whether public or private are and must be focus on developing and adopting effective performance measurement and performance management system; because it is only with the help of these systems organization can remain high performer. Now-a-days, in any industry whether small or big, human resource management not just plays traditional role but they are using various strategical tools of HRM to evaluate its employees’ performance and manage it accurately with a new system in the field of HRM known as Performance Management System (PMS). PMS helps the organization in aligning individual’s goal and objectives with organizational objectives. This paper deals with how PMS can be utilized for taking various strategic HR decisions and the effectiveness of PMS. The result of the study shows that a performance management system acts as a strategic tool and a powerful foundation for the employees to achieve their ambitions and organizations to achieve their key financial goals.

scholarly journals Novel submission modes for tightly coupled jobs across distributed resources for reduced time-to-solution

2009 ◽  
Vol 367 (1897) ◽  
pp. 2545-2556 ◽  
Author(s):  
Promita Chakraborty ◽  
Shantenu Jha ◽  
Daniel S. Katz
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Wait Time ◽  
Peak Performance ◽  
Distributed Resources ◽  
Vast Number ◽  
Multiple Input ◽  
Tightly Coupled ◽  
And Performance ◽  
Multiple Machines

The problems of scheduling a single parallel job across a large-scale distributed system are well known and surprisingly difficult to solve. In addition, because of the issues involved in distributed submission, such as co-reserving resources, and managing accounts and certificates simultaneously on multiple machines, etc., the vast number of high-performance computing (HPC) application users have been happy to remain restricted to submitting jobs to single machines. Meanwhile, the need to simulate larger and more complex physical systems continues to grow, with a concomitant increase in the number of cores required to solve the resulting scientific problems. One might reduce the demand on load per machine, and eventually the wait-time in queue, by decomposing the problem to use two resources in such circumstances, even though there might be a reduction in the peak performance. This motivates a question. Can otherwise monolithic jobs running on single resources be distributed over more than one machine such that there is an overall reduction in the time-to-solution? In this paper, we briefly discuss the development and performance of a parallel molecular dynamics code and its generalization to work on multiple distributed machines (using MPICH-G2). We benchmark and validate the performance of our simulations over multiple input datasets of varying sizes. The primary aim of this work, however, is to show that the time-to-solution can be reduced by sacrificing some peak performance and distributing over multiple machines.

Power and Performance Management of GPUs Based Cluster

2012 ◽  
Vol 2 (4) ◽  
pp. 16-31 ◽  
Author(s):  
Yaser Jararweh ◽  
Salim Hariri
Keyword(s):  
Power Consumption ◽  
Performance Management ◽  
High Performance ◽  
Peak Performance ◽  
Gpu Cluster ◽  
Management Framework ◽  
High Productivity ◽  
Computing Industry ◽  
And Performance ◽  
Performance Computing

Power consumption in GPUs based cluster became the major obstacle in the adoption of high productivity GPU accelerators in the high performance computing industry. The power consumed by GPU chips represent about 75% of the total GPU based cluster power consumption. This is due to the fact that the GPU cards are often configured at peak performance, and consequently, they will be active all the time. In this paper, the authors present a holistic power and performance management framework that reduces power consumption of the GPU based cluster and maintains the system performance within an acceptable predefined threshold. The framework dynamically scales the GPU cluster to adapt to the variation of incoming workload’s requirements and increase the idleness of the of GPU devices, allowing them to transition to low-power state. The proposed power and performance management framework in GPU cluster demonstrated 46.3% power savings for GPU workload while maintaining the cluster performance. The overhead of the proposed framework is insignificant on the normal application\system operations and services.

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