Evaluating the performance of skeleton-based high level parallel - PowerPoint PPT Presentation

Evaluating the performance of skeleton-based high level parallel programs (funded by the EPSRC, grant number GR/S21717/01) Enhancing the Performance Predictability of Grid Applications with Patterns and Process Algebras A. Benoit, M. Cole, S. Gilmore, J. Hillston http://www.inf.ed.ac.uk abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 1

� Motivations Grid technologies: widely distributed collections of computers difficult issues of resource allocation and scheduling Skeleton-based programming: commonly used patterns models with Process Algebra Enhance the performance of Grid applications (performance results “good” scheduling decisions) abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 2

Structure of the talk Introduction The Pipeline skeleton Principle of the skeleton Process Algebra Model AMoGeT: The Automatic Model Generation Tool Overview and input files Different functionalities Numerical results Conclusions and Perspectives abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 3

Introduction - Grid and skeletons Grid Applications unpredictability of resource availability and performance scheduling issues rescheduling techniques may be useful Skeleton based programming library of skeletons many real applications can use these skeletons modularity, configurability Edinburgh Skeleton Library eSkel (MPI-based) abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 4

Introduction - Performance evaluation Use of a particular skeleton: information about implied scheduling dependencies Model with stochastic process algebras PEPA include aspects of uncertainty inherent to Grids automated modelling process dynamic monitoring of resource performance allow better scheduling decisions, and adap- tive rescheduling of applications abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 5

Introduction - Related projects The Network Weather Service – R. Wolski & al benchmarking and monitoring techniques for the Grid no skeletons and no performance models ICENI project – N. Furmento & al performance models to improve the scheduling decisions no skeletons, models = graphs which approximate data Use of skeleton programs within grid nodes – M. Alt&al each server provides a function capturing the cost of its implementation of each skeleton each skeleton runs only on one server scheduling = select the most appropriate servers abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 6

Introduction - Main contribution Single skeletons which span the Grid Skeletons modelled in a generic way using stochastic process algebras Performance results: allow a dynamic rescheduling of the Grid application to enhance its performance Significant results obtained on a first case study based on the Pipeline skeleton abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 7

Structure of the talk Introduction The Pipeline skeleton Principle of the skeleton Process Algebra Model AMoGeT: The Automatic Model Generation Tool Overview and input files Different functionalities Numerical results Conclusions and Perspectives abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 8

✂ ✁ Pipeline - Principle of the skeleton inputs outputs ✄✆☎ Stage Stage Stage ... stages process a sequence of inputs to produce a ✝✟✞ sequence of outputs All input passes through each stage in the same order The internal activity of a stage may be parallel, but this is transparent to our model abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 9

Pipeline - Model Model expressed in Performance Evaluation Process Algebra PEPA [Hillston] Mapping of the application onto the computing resources: the network and the processors abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 10

✘ ✞ ✣ ✩ ★ ✍ ✎ ✛ ✏ ✒ ✡ ✧ ✓ ✪ ✙ ✓ ✖ ✌ ✞ ✦ ✦ ✓ ✥ ✘ ✣✤ ✓ ✳ ✸✹✺ ✍ ✢ ✢ ✳ ✶ ✰✱ ✯ ✮ ✭ ✮✯✰✱ ✒ ✷ ✳ ✓ ✲ ✮✯✰✱ ✭ ✡ ✓ ✒ ✏ ✎ ✍ ✔ ☎ ☎ ✞ ✭ ✻ ✘✥ ✡ ✢ ✜ ✛ ✚ ✞ ✓ ✘✙ ✸✹✺ ✔ ✞ ✦ ✒✓ ✌ ✍ ☎ ☎ ✌ ☛ ✧ ✠ ✞ ✡ ✓✦ ✞ ✓ ✜ ✙ ✘ ✖ ✻ ✞ ✓ ✒ ✏ ✎ ✍ ✢ ✛ ✚ ✚ ✡ ✧ ✞ ✓ ✘✙ ✖ ✕ ✢ ✜ ✛ ✢ Pipeline - Application model Application model: independent of the resources 1 PEPA component per stage of the pipeline ( ) ✡☞☛ def ✕✗✖ ✕✗✣✤ ✎✑✏ Sequential component: gets data ( ), processes it ( ), moves the data to the next stage ( ) Unspecified rates ( ): depends on the resources Pipeline application = cooperation of the stages def ✩✬✫ ✎✑✏ ✓✵✴ : arrival of an input in the application : transfer of the final output out of the Pipeline abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 11

✢ ✢ ✘✙ ✖ ✕ ✾ ✢ ✡ ✾ ✌ ❀ ✤ ✘ ✿ ✎ ❉ ✓ ☎ ✜ ✢ ❀ ✤ ✘ ✿ ✎ ❃ ✜ ✧ ✡ ✧ ☎ ✌ ✼ ✚ ✡ ✢ ✡ ✞ ✞ ✢ ✢ ✽ ✔ ✦ ✌ ✓ ✞ ✘✙ ✖ ☎ ✞ ✼ ✧ ✡ ✝ ✾ ✼ ✔ ✚ ✡ ✓ ✙ ✘ ❃ ✞ ✽ ❀ ✤ ✘ ❆ ✓ ✝ ❃ ❂ Pipeline - Network model Network model: information about the efficiency of the link connection between pairs of processors Assign rates to the activities ( ) def ✕✗✖ ✝❁✓ ✎✑✿ ✝❁✓ represents the connection between the processor ✞❅❄ hosting stage and the processor hosting stage ❆❈❇ Special cases: is the processor providing inputs to the Pipeline ❃❋❊ is where we want the outputs to be delivered abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 12

✔ ✡ ✕ ✾ ✡ ✘✥ ✤ ★ ✢ ✜ ✡ ■ ✚ ✦ ✤ ✓✦ ✥ ✘ ★ ✤ ✘ ✡ ✥ ✘ ✤ ★ ✥ ✣ ✘✥ ✘✥ ★ ✤ ★ ✔ ❑ ✦ ✤ ✘ ✦ ✓✦ ✥ ✘ ✤ ❑ ✓✦ ✡ ✥ ✘ ✤ ★ ✢ ✜ ✴ ■ ✚ ✴ ✦ ✞ ✤ ✡ ❑ ● ✝ ✢ ✢ ✢ ✞ ✝ ✢ ✢ ✽ ✔ ✦ ✞ ✝ ✦ ✓✦ ✥ ✘ ✤ ✣ ❑ ✞ ■ ★ ✤ ✘✥ ✔ ✞ ★ ❑ ✢ ✜ ✞ ■ ✚ ✞ ✦ ✓✦ ✥ ✘ ✴ ✔ ✞ ❏ ✥ ✘ ✤ ★ ❑ ✞ ✝ ✢ ✢ ✽ ✔ ✦ ✘✥ Pipeline - Processors model Processors model: Application mapped on a set of ✝❍● processors Rate of the activities ( ): load of the processor, and other performance information One stage per processor ( ): def ✕✗✣✤ Several stages per processor: def ✕✗✣✤ Set of processors: parallel composition def ✌▼▲ abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 13

✐ ❲ ❞ ❜ ❝ ❲ ❘ ❜ ❭ ❘ ❝ ❵ ❴ ❪ ❪ ❭ ❬ ❲ ❲ ❩ ♥ ❚ ❯ ❦ ❥ ❲ P ♣ ❞ ❜ ❜ ❘ ❘ ♠ ❧ ❚ ❯ ❨ ❨ ♦ ❳ ❪ ❳ ❬ ❨ ❩ ❨ ❱❲❳ ❚❯ ❚❯ ❯ P ❖ ◆ ❚ ❳ ❱❲❳ ❜ ❲ ❯ ❯❤ ❲ P ❢ ◆ ❳ ❚ ❳ ❘ ❱❲❳ ❚❯ ♦ ❲ q ❝ ❲ ❧ Pipeline - Overall model The overall model is the mapping of the stages onto the processors and the network by using the cooperation combinator ◗❙❘ synchronize ❜❡❞ ❴❛❵ ❭❫❪ and ◗❙❣ synchronize and ❥✬❦ def ❜❡❞ abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 14

Structure of the talk Introduction The Pipeline skeleton Principle of the skeleton Process Algebra Model AMoGeT: The Automatic Model Generation Tool Overview and input files Different functionalities Numerical results Conclusions and Perspectives abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 15

AMoGeT - Overview AMoGeT Generate PEPA Solve Compare results models models models results performance description information information files from NWS AMoGeT: Automatic Model Generation Tool Standalone prototype Ultimate role: integrated component of a run-time scheduler and re-scheduler abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 16

✩ ✩ ✽ � AMoGeT - Description files (1) Specify the names of the processors file hosts.txt : list of IP addresses rank in the list processor processor is the reference processor wellogy.inf.ed.ac.uk bw240n01.inf.ed.ac.uk bw240n02.inf.ed.ac.uk france.imag.fr abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 17

✔ ✞ ✢ ✢ ✝ ✞ ✽ ✦ ✎ ✤ AMoGeT - Description files (2) Describe the modelled application mymodel file mymodel.des stages of the Pipeline: number of stages and time (sec) required to compute one output for each stage on the reference processor ✝❍✞ ✝❍✞ nbstage= ; tr1=10; tr2=2; ... mappings of stages to processors: location of the input data, the processor where each stage is processed, and where the output data must be left. mappings=[1,(1,2,3),1],[1,(1,1,1),1]; abenoit1@inf.ed.ac.uk – Enhance Meeting – 18 February 2004 – 18