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
Load-sharing in heterogeneous systems via weighted factoring
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Dynamic Scheduling Parallel Loops with Variable Iterate Execution Times
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
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
A Load Balancing Tool for Distributed Parallel Loops
CLADE '03 Proceedings of the 1st International Workshop on Challenges of Large Applications in Distributed Environments
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
Functional coefficient autoregressive models for vector time series
Computational Statistics & Data Analysis
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
Dynamic load balancing with adaptive factoring methods in scientific applications
The Journal of Supercomputing
Functional coefficient autoregressive models for vector time series
Computational Statistics & Data Analysis
Investigating asymptotic properties of vector nonlinear time series models
Journal of Computational and Applied Mathematics
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Vector functional coefficient autoregressive (VFCAR) models are powerful nonlinear times series models. Statistical estimation and inference involving VFCAR models are computationally intensive so that simulations to study statistical properties take weeks, or even months. As a consequence, empirical results are often based on a relatively small number of simulation replications, sacrificing precision, accuracy and reliability in the interest of "computational expediency." The simulations are amenable for parallelization; however, parallel computing technology has not been implemented in the VFCAR literature. This paper proposes an approach to the parallelization of statistical simulation codes to address long running times, without resorting to extensive code revisions. This approach takes advantage of recent advances in dynamic loop scheduling on workstation clusters to achieve high performance, even with the presence of unpredictable load imbalance factors. Preliminary results indicate that efficiencies in the range 95-99% on 32 processors of a Sun Solaris cluster are achievable.