Characterizing heterogeneous computing environments using singular value decomposition

We consider a heterogeneous computing environment that consists of a collection of machines and task types. The machines vary in capabilities and different task types are better suited to specific machine architectures. We describe some of the difficulties with the current measures that are used to characterize heterogeneous computing environments and propose two new measures. These measures relate to the aggregate machine performance (relative to the given task types) and the degree of affinity that specific task types have to different machines. The latter measure of task-machine affinity is quantified using singular value decomposition. One motivation for using these new measures is to be able to represent a wider range of heterogeneous environments than is possible with previous techniques. An important application of studying the heterogeneity of heterogeneous systems is predicting the performance of different computing hardware for a given task type mix.

[1]  Arnold L. Rosenberg,et al.  Statistical predictors of computing power in heterogeneous clusters , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[2]  Klara Nahrstedt,et al.  QoS and Contention-Aware Multi-Resource Reservation , 2004, Cluster Computing.

[3]  Ishfaq Ahmad,et al.  Optimal task assignment in heterogeneous distributed computing systems , 1998, IEEE Concurr..

[4]  Imtiaz Ahmad,et al.  An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems , 2002, J. Parallel Distributed Comput..

[5]  Anthony A. Maciejewski,et al.  Robust sequential resource allocation in heterogeneous distributed systems with random compute node failures , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[6]  Emmanuel Jeannot,et al.  Precise Evaluation of the Efficiency and the Robustness of Stochastic DAG Schedules , 2009 .

[7]  Behdis Eslamnour,et al.  Measuring robustness of computing systems , 2009, Simul. Model. Pract. Theory.

[8]  Decai Huang,et al.  Research on Tasks Scheduling Algorithms for Dynamic and Uncertain Computing Grid Based on a+bi Connection Number of SPA , 2009, J. Softw..

[9]  Viktor K. Prasanna,et al.  Heterogeneous computing: challenges and opportunities , 1993, Computer.

[10]  Sadiq M. Sait,et al.  Task matching and scheduling in heterogeneous systems using simulated evolution , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[11]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[12]  Arif Ghafoor,et al.  A distributed heterogeneous supercomputing management system , 1993, Computer.

[13]  Ishfaq Ahmad,et al.  Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[14]  Howard Jay Siegel,et al.  Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems , 2000 .

[15]  Anthony A. Maciejewski,et al.  Task and Machine Heterogeneities: Higher Momenets Matter , 2009, PDPTA.

[16]  Gene H. Golub,et al.  Matrix computations , 1983 .

[17]  Emmanuel Jeannot,et al.  Evaluation and Optimization of the Robustness of DAG Schedules in Heterogeneous Environments , 2010, IEEE Transactions on Parallel and Distributed Systems.

[18]  Ladislau Bölöni,et al.  Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems , 2005, Adv. Comput..