Compiler techniques for data partitioning of sequentially iterated parallel loops

1990 ◽  
Author(s):  
David E. Hudak ◽  
Santosh G. Abraham
Keyword(s):  
Data Partitioning ◽  
Parallel Loops ◽  
Compiler Techniques

Compiler techniques for data synchronization in nested parallel loops

1990 ◽  
Vol 18 (3b) ◽  
pp. 177-186 ◽  
Author(s):  
Peiyi Tang ◽  
Pen-Chung Yew ◽  
Chuan-Qi Zhu
Keyword(s):  
Data Synchronization ◽  
Parallel Loops ◽  
Compiler Techniques

A Layered Mapping of Ada 202X to OpenMP

2021 ◽  
Vol 40 (2) ◽  
pp. 55-58
Author(s):  
S. Tucker Taft
Keyword(s):  
Operating System ◽  
Application Programming Interface ◽  
Light Weight ◽  
Main Interest ◽  
Thread Scheduling ◽  
Environment Variables ◽  
Application Programming ◽  
Parallel Loops ◽  
Programming Interface

The OpenMP specification defines a set of compiler directives, library routines, and environment variables that together represent the OpenMP Application Programming Interface, and is currently defined for C, C++, and Fortran. The forthcoming version of Ada, currently dubbed Ada 202X, includes lightweight parallelism features, in particular parallel blocks and parallel loops. All versions of Ada, since its inception in 1983, have included "tasking," which corresponds to what are traditionally considered "heavyweight" parallelism features, or simply "concurrency" features. Ada "tasks" typically map to what are called "kernel threads," in that the operating system manages them and schedules them. However, one of the goals of lightweight parallelism is to reduce overhead by doing more of the management outside the kernel of the operating system, using a light-weight-thread (LWT) scheduler. The OpenMP library routines support both levels of threading, but for Ada 202X, the main interest is in making use of OpenMP for its lightweight thread scheduling capabilities.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

scholarly journals Optimal data partitioning, multispecies coalescent and Bayesian concordance analyses resolve early divergences of the grape family (Vitaceae)

Cladistics ◽  
2017 ◽  
Vol 34 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Limin Lu ◽  
Cymon J. Cox ◽  
Sarah Mathews ◽  
Wei Wang ◽  
Jun Wen ◽  
...  
Keyword(s):  
Data Partitioning ◽  
Multispecies Coalescent

Run-time parallelization for partially parallel loops

2002 ◽  
Author(s):  
Chao-Tung Yang ◽  
Shian-Shyong Tseng ◽  
Shih Hung Kao ◽  
Ming-Hui Hsieh ◽  
Mon-Fong Jiang
Keyword(s):  
Parallel Loops ◽  
Run Time ◽  
Time Parallelization
Sign in / Sign up

Export Citation Format

Share Document