TY - GEN
T1 - Predictive performance analysis of a parallel pipelined synchronous wavefront application for commodity processor cluster systems
AU - Mudalige, Gihan R.
AU - Jarvis, Stephan A.
AU - Spooner, Daniel P.
AU - Nudd, Graham R.
PY - 2006
Y1 - 2006
N2 - This paper details the development and application of a model for predictive performance analysis of a pipelined synchronous wavefront application running on commodity processor cluster systems. The performance model builds on existing work [1] by including extensions for modern commodity processor architectures. These extensions, including coarser hardware benchmarking, prove to be essential in countering the effects of modern superscalar processors (e.g. multiple operation pipelines and on-the-fly optimisations), complex memory hierarchies, and the impact of applying modern optimising compilers. The process of application modelling is also extended, combining static source code analysis with run-time profiling results for increased accuracy. The model is validated on several high performance SMP systems and the results show a high predictive accuracy (≤ 10% error). Additionally, the use of the performance model to speculate on the performance and scalability of this application on a hypothetical cluster with two different problem sizes is demonstrated. It is shown that such speculative techniques can be used to support system procurement, run-time verification and system maintenance and upgrading.
AB - This paper details the development and application of a model for predictive performance analysis of a pipelined synchronous wavefront application running on commodity processor cluster systems. The performance model builds on existing work [1] by including extensions for modern commodity processor architectures. These extensions, including coarser hardware benchmarking, prove to be essential in countering the effects of modern superscalar processors (e.g. multiple operation pipelines and on-the-fly optimisations), complex memory hierarchies, and the impact of applying modern optimising compilers. The process of application modelling is also extended, combining static source code analysis with run-time profiling results for increased accuracy. The model is validated on several high performance SMP systems and the results show a high predictive accuracy (≤ 10% error). Additionally, the use of the performance model to speculate on the performance and scalability of this application on a hypothetical cluster with two different problem sizes is demonstrated. It is shown that such speculative techniques can be used to support system procurement, run-time verification and system maintenance and upgrading.
UR - http://www.scopus.com/inward/record.url?scp=46049084595&partnerID=8YFLogxK
U2 - 10.1109/CLUSTR.2006.311888
DO - 10.1109/CLUSTR.2006.311888
M3 - Conference contribution
AN - SCOPUS:46049084595
SN - 1424403286
SN - 9781424403288
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
BT - 2006 IEEE International Conference on Cluster Computing, Cluster 2006
T2 - 2006 IEEE International Conference on Cluster Computing, Cluster 2006
Y2 - 25 September 2006 through 28 September 2006
ER -