TY - GEN
T1 - Resource planning for parallel processing in the cloud
AU - Shi, Justin Y.
AU - Taifi, Moussa
AU - Khreishah, Abdallah
PY - 2011
Y1 - 2011
N2 - Before the emergence of commercial cloud computing, interests in parallel algorithm analysis have been mostly academic. When computing and communication resources are charged by hours, cost effective parallel processing would become a required skill. This paper reports a resource planning study using a method derived from classical program time complexity analysis, we call Timing Models. Unlike existing qualitative performance analysis methods, a Timing Model uses application instrumented capacity measures to capture the quantitative dependencies between a computer program (sequential or parallel) and its processing environments. For applications planning to use commercial clouds, this tool is ideally suited for choosing the most cost-effective configuration. The contribution of the proposed tool is its ability to explore multiple dimensions of a program quantitatively to gain non-trivial insights. This paper uses a simple matrix multiplication application to illustrate the modeling, program instrumentation and performance prediction processes. Since cloud vender do offer HPC hardware resources, we use Amazon EC2 as the target processing environments. The computing and communication models are not only useful in choosing the processing platform but also for understanding the resource usage bills. Comparisons between predicted and actual resource usages show that poor processing granularity wastes resources. Prediction errors are minimized near the optimal number of processors.
AB - Before the emergence of commercial cloud computing, interests in parallel algorithm analysis have been mostly academic. When computing and communication resources are charged by hours, cost effective parallel processing would become a required skill. This paper reports a resource planning study using a method derived from classical program time complexity analysis, we call Timing Models. Unlike existing qualitative performance analysis methods, a Timing Model uses application instrumented capacity measures to capture the quantitative dependencies between a computer program (sequential or parallel) and its processing environments. For applications planning to use commercial clouds, this tool is ideally suited for choosing the most cost-effective configuration. The contribution of the proposed tool is its ability to explore multiple dimensions of a program quantitatively to gain non-trivial insights. This paper uses a simple matrix multiplication application to illustrate the modeling, program instrumentation and performance prediction processes. Since cloud vender do offer HPC hardware resources, we use Amazon EC2 as the target processing environments. The computing and communication models are not only useful in choosing the processing platform but also for understanding the resource usage bills. Comparisons between predicted and actual resource usages show that poor processing granularity wastes resources. Prediction errors are minimized near the optimal number of processors.
KW - Parallel program scalability analysis
KW - resource analysis for HPC cloud computing
UR - http://www.scopus.com/inward/record.url?scp=81555218592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81555218592&partnerID=8YFLogxK
U2 - 10.1109/HPCC.2011.117
DO - 10.1109/HPCC.2011.117
M3 - Conference contribution
AN - SCOPUS:81555218592
SN - 9780769545387
T3 - Proc.- 2011 IEEE International Conference on HPCC 2011 - 2011 IEEE International Workshop on FTDCS 2011 -Workshops of the 2011 Int. Conf. on UIC 2011- Workshops of the 2011 Int. Conf. ATC 2011
SP - 828
EP - 833
BT - Proc.- 2011 IEEE International Conference on HPCC 2011 - 2011 IEEE International Workshop on FTDCS 2011 - Workshops of the 2011 Int. Conf. on UIC 2011- Workshops of the 2011 Int. Conf. ATC 2011
T2 - 13th IEEE International Workshop on FTDCS 2011, the 8th International Conference on ATC 2011, the 8th International Conference on UIC 2011 and the 13th IEEE International Conference on HPCC 2011
Y2 - 2 September 2011 through 4 September 2011
ER -