Allocating Independent Subtasks on Parallel Processors
IEEE Transactions on Software Engineering
Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Factoring: a method for scheduling parallel loops
Communications of the ACM
Adaptive cubature over a collection of triangles using the d-transformation
ICCAM'92 Proceedings of the fifth international conference on Computational and applied mathematics
Balancing processor loads and exploiting data locality in N-body simulations
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Load-sharing in heterogeneous systems via weighted factoring
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
The optimal effectiveness metric for parallel application analysis
Information Processing Letters - Special issue on parallel models
Comments on the Nature of Automatic Quadrature Routines
ACM Transactions on Mathematical Software (TOMS)
Trapezoid Self-Scheduling: A Practical Scheduling Scheme for Parallel Compilers
IEEE Transactions on Parallel and Distributed Systems
Load Balancing Highly Irregular Computations with the Adaptive Factoring
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Performance of Scheduling Scientific Applications with Adaptive Weighted Factoring
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Parallel Adaptive Quantum Trajectory Method for Wavepacket Simulations
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Message-passing parallel adaptive quantum trajectory method
High performance scientific and engineering computing
A Novel Dynamic Load Balancing Library for Cluster Computing
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
Simulation of Vector Nonlinear Time Series Models on Clusters
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 13 - Volume 14
A Load Balancing Tool for Distributed Parallel Loops
Cluster Computing
A Dynamic Load Balancing Tool for One and Two Dimensional Parallel Loops
ISPDC '06 Proceedings of the Proceedings of The Fifth International Symposium on Parallel and Distributed Computing
Vector nonlinear time-series analysis of gamma-ray burst datasets on heterogeneous clusters
Scientific Programming - International Symposium of Parallel and Distributed Computing & International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogenous Networks
Functional coefficient autoregressive models for vector time series
Computational Statistics & Data Analysis
Simulation of a hybrid model for image denoising
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Computational challenges in vector functional coefficient autoregressive models
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Adaptive statistical scheduling of divisible workloads in heterogeneous systems
Journal of Scheduling
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
The Journal of Supercomputing
Hi-index | 0.00 |
To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed. Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular, methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing.The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently introduced adaptive factoring method.