The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Proceedings of the 6th international workshop on Hardware/software codesign
An open graph visualization system and its applications to software engineering
Software—Practice & Experience - Special issue on discrete algorithm engineering
The boost graph library: user guide and reference manual
The boost graph library: user guide and reference manual
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Scheduling Algorithms
GNU Scientific Library Reference Manual - Third Edition
GNU Scientific Library Reference Manual - Third Edition
WSCOM: Online Task Scheduling with Data Transfers
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Scalable test data generation from multidimensional models
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Generating synthetic task graphs for simulating stream computing systems
Journal of Parallel and Distributed Computing
A synthetic task model for HPC-grade optical network performance evaluation
IA^3 '13 Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms
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In parallel and distributed systems, validation of scheduling heuristics is usually done by simulation on randomly generated synthetic workloads, typically represented by task graphs. Since there is no single generation method that models all possible workloads for scheduling problems, researchers often re-implement the classical generation algorithms or even implement ad hoc ones. A bad choice of generation method can mislead the validation of the algorithm due to biases it can induce. Moreover, different implementations of the same randomized generation method may produce slightly different graphs. These problems can harm the experimental comparison of scheduling algorithms. In order to provide a comparison basis we propose GGen -- a unified and standard implementation of classical task graph generation methods used in the scheduling domain. We also provide an in-depth analysis of each generation method, emphasizing important graph properties that may influence scheduling algorithms.