PROGRAM DEVELOPMENT FOR COMPUTATIONAL GRIDS USING SKELETONS AND PERFORMANCE PREDICTION

2002 ◽  
Vol 12 (02) ◽  
pp. 157-174 ◽  
Author(s):  
MARTIN ALT ◽  
HOLGER BISCHOF ◽  
SERGEI GORLATCH
Keyword(s):  
Performance Prediction ◽  
Computational Grid ◽  
Computational Grids ◽  
Programming System ◽  
Challenging Problem ◽  
Predicting Performance ◽  
Grid Applications ◽  
And Performance ◽  
High Level ◽  
Computational Resources

We address the challenging problem of algorithm and program design for the Computational Grid by providing the application user with a set of high-level, parameterised components called skeletons. We descrile a Java-based Grid programming system in which algorithmns are composed of skeletons and the computational resources for executing individual skeletons are chosen using performance prediction. The advantage of our approach is that skeletons are reusable for different applications and that skeletons' implementation can be tuned to particular machines. The focus of this paper is on predicting performance for Grid applications constructed using skeletons.

scholarly journals AUTOMATIC MAPPING OF ASSIST APPLICATIONS USING PROCESS ALGEBRA

2008 ◽  
Vol 18 (01) ◽  
pp. 175-188
Author(s):  
MARCO ALDINUCCI ◽  
ANNE BENOIT
Keyword(s):  
Large Scale ◽  
Process Algebra ◽  
Dynamic Nature ◽  
Programming Environments ◽  
Automatic Mapping ◽  
Grid Applications ◽  
Grid Application ◽  
High Level ◽  
Computational Resources ◽  
Grid Resources

Grid technologies aim to harness the computational capabilities of widely distributed collections of computers. Due to the heterogeneous and dynamic nature of the set of grid resources, the programming and optimisation burden of a low level approach to grid computing is clearly unacceptable for large scale, complex applications. The development of grid applications can be simplified by using high-level programming environments. In the present work, we address the problem of the mapping of a high-level grid application onto the computational resources. In order to optimise the mapping of the application, we propose to automatically generate performance models from the application using the process algebra PEPA. We target applications written with the high-level environment ASSIST, since the use of such a structured environment allows us to automate the study of the application more effectively.

scholarly journals A-56 Predicting Performance on the Raven’s Matrices from the Findings of the Boston Longitudinal MIDUS Study

2019 ◽  
Vol 34 (6) ◽  
pp. 916-916
Author(s):  
C Cabrera
Keyword(s):  
Working Memory ◽  
Fluid Intelligence ◽  
Digit Span ◽  
P Value ◽  
Digit Symbol Substitution Test ◽  
Predicting Performance ◽  
Visuospatial Skills ◽  
Shared Component ◽  
And Performance ◽  
High Level

Abstract Objective To examine the relationship between different cognitive measures and Raven’s Progressive Matrices (RPM) during midlife. Methods Data was derived from a de-identified MIDUS-II database (n = 328, 57.8% male, Mage = 48.14, Meducation = 15.8, 93% Caucasian). All participants were administered cognitive tests consisting of several measures of cognitive ability (Trails-B, Vocabulary, Forward & Backward-Digit-Span, and Digit-Symbol-Substitution-Test (DSST)) and RPM. Results Pearson correlations were conducted between cognitive performance on various measures and performance on RPM. Using a Bonferroni correction across all correlations, p-value was set at .001. Better performance on RPM was negatively associated with Trails-B (r(139) = -.446,p = .001) and positively associated with Vocabulary (r(168) = .424,p = .001), Forward-Digit-Span (r(168) = .318,p = .001), Backward-Digit-Span (r(166) = .257,p = .001), DSST (r(166) = .516,p = .001). Conclusions Results suggest that DSST is the strongest predictor of RPM performance, followed by Trails-B. Reasons for the strong DSST correlation suggest the shared component of sustained attention, processing speed, working memory and set shifting. A moderate association with Trails-B implies the shared components of visual-conceptualization, visual motor tracking, and working memory. Both DSST and Trails-B not only require working memory, but also visuospatial skills, suggesting the use of high-level processes may be required for successful performance on the RPM. Moreover, visuospatial skills may be more strongly related to fluid intelligence than other abilities. This suggests that visualization skills to perceive and mentally reproduce patterns, mental rotation, and flexibility of closure to detect a stimulus hidden within a complex figure are all essential in RPM performance. Future studies should focus on gender and age differences in relation to visuospatial skills, specifically the age at which these differences occur.

Scale Model on Performance Prediction in Recreational and Elite Endurance Runners

2014 ◽  
Vol 9 (4) ◽  
pp. 650-655 ◽  
Author(s):  
Marcus P. Tartaruga ◽  
Carlos B. Mota ◽  
Leonardo A. Peyré-Tartaruga ◽  
Jeanick Brisswalter
Keyword(s):  
Body Mass ◽  
Performance Prediction ◽  
Allometric Scaling ◽  
Endurance Performance ◽  
Scale Model ◽  
Distance Runners ◽  
Recreational Runners ◽  
And Performance ◽  
High Level ◽  
The Relationship

Purpose:To identify the effect of allometric scaling on the relationship between running efficiency (REff) and middle-distancerunning performance according to performance level.Methods:Thirteen male recreational middle-distance runners (mean ± SD age 33.3 ± 8.4 y, body mass 76.4 ± 8.6 kg, maximal oxygen uptake [VO2max] 52.8 ± 4.6 mL · kg−1 · min−1; G1) and 13 male high-level middle-distance runners (age 25.5 ± 4.2 y, body mass 62.8 ± 2.7 kg, VO2max 70.4 ± 1.9 mL · kg−1 · min−1; G2) performed a continuous incremental test to volitional exhaustion to determine VO2max and a 6-min submaximal running test at 70% of VO2max to assess REff.Results:Significant correlation between REff and performance were found for both groups; however, the strongest correlations were observed in recreational runners, especially when using the allometric exponent (respectively for G1, nonallometric vs allometric scaling: r = .80 vs r = .86; and for G2, nonallometric vs allometric scaling: r = .55 vs r = .50).Conclusion:These results indicate that an allometric normalization may improve endurance-performance prediction from REff values in recreational, but not in elite, runners.

scholarly journals Deep Recurrent Model for Server Load and Performance Prediction in Data Center

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zheng Huang ◽  
Jiajun Peng ◽  
Huijuan Lian ◽  
Jie Guo ◽  
Weidong Qiu
Keyword(s):  
Performance Prediction ◽  
Data Center ◽  
Short Term Memory ◽  
Workload Prediction ◽  
Data Set ◽  
Request Sequence ◽  
Predicting Performance ◽  
User Request ◽  
And Performance ◽  
Natural Language Process

Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM) units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests), which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.

https://imsciences.edu.pk/files/journals/vol12_2/New%201%20MA.864.pdf

2020 ◽  
Vol 12 (2) ◽  
pp. 19-50 ◽  
Author(s):  
Muhammad Siddique ◽  
Shandana Shoaib ◽  
Zahoor Jan
Keyword(s):  
Organizational Performance ◽  
Structural Equation ◽  
High Performance ◽  
Service Sector ◽  
Performance Outcomes ◽  
Theory And Practice ◽  
Relational Coordination ◽  
Multiple Sources ◽  
And Performance ◽  
High Level

A key aspect of work processes in service sector firms is the interconnection between tasks and performance. Relational coordination can play an important role in addressing the issues of coordinating organizational activities due to high level of interdependence complexity in service sector firms. Research has primarily supported the aspect that well devised high performance work systems (HPWS) can intensify organizational performance. There is a growing debate, however, with regard to understanding the “mechanism” linking HPWS and performance outcomes. Using relational coordination theory, this study examines a model that examine the effects of subsets of HPWS, such as motivation, skills and opportunity enhancing HR practices on relational coordination among employees working in reciprocal interdependent job settings. Data were gathered from multiple sources including managers and employees at individual, functional and unit levels to know their understanding in relation to HPWS and relational coordination (RC) in 218 bank branches in Pakistan. Data analysis via structural equation modelling, results suggest that HPWS predicted RC among officers at the unit level. The findings of the study have contributions to both, theory and practice.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

2021 ◽  
Vol 31 (2) ◽  
pp. 1-28
Author(s):  
Gopinath Chennupati ◽  
Nandakishore Santhi ◽  
Phill Romero ◽  
Stephan Eidenbenz
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Prediction Models ◽  
Radiation Transport ◽  
Discrete Event ◽  
Basic Block ◽  
Distribution Models ◽  
Scientific Application ◽  
High Level ◽  
Access Patterns

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

scholarly journals Towards a contemporary player learning in development framework for sports practitioners

2021 ◽  
pp. 174795412110023
Author(s):  
Mark O Sullivan ◽  
Carl T Woods ◽  
James Vaughan ◽  
Keith Davids
Keyword(s):  
Linear Process ◽  
Task Environment ◽  
Coach Education ◽  
Development Framework ◽  
Novel Approach ◽  
Non Linear ◽  
And Performance ◽  
Learning Development ◽  
High Level ◽  
User Friendly

As it is appreciated that learning is a non-linear process – implying that coaching methodologies in sport should be accommodative – it is reasonable to suggest that player development pathways should also account for this non-linearity. A constraints-led approach (CLA), predicated on the theory of ecological dynamics, has been suggested as a viable framework for capturing the non-linearity of learning, development and performance in sport. The CLA articulates how skills emerge through the interaction of different constraints (task-environment-performer). However, despite its well-established theoretical roots, there are challenges to implementing it in practice. Accordingly, to help practitioners navigate such challenges, this paper proposes a user-friendly framework that demonstrates the benefits of a CLA. Specifically, to conceptualize the non-linear and individualized nature of learning, and how it can inform player development, we apply Adolph’s notion of learning IN development to explain the fundamental ideas of a CLA. We then exemplify a learning IN development framework, based on a CLA, brought to life in a high-level youth football organization. We contend that this framework can provide a novel approach for presenting the key ideas of a CLA and its powerful pedagogic concepts to practitioners at all levels, informing coach education programs, player development frameworks and learning environment designs in sport.

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