Parallel Programming on the ANDC cluster
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The document discusses parallel programming and message passing as a parallel programming model. It provides examples of using MPI (Message Passing Interface) and MapReduce frameworks for parallel programming. Some key applications discussed are financial risk assessment, molecular dynamics simulations, rendering animation, and web indexing. Challenges with parallel programming include potential slowdown due to overhead and limitations of parallel speedup based on sequential fractions of programs.
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This document describes using MATLAB to simulate packet traffic using an M/M/1 queue with Erlang C distribution. The simulation finds:
1) The probability of packets being delayed is 0.6402.
2) The average delay for all packets is 382.33 microseconds.
3) The probability of delay exceeding 10 microseconds is 0.6296.
4) The probability of delay exceeding 4 milliseconds is 0.0007895.
The results show the system can acceptably handle packets with over 99% experiencing delays under 4 milliseconds.
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Hoje em dia é fácil juntar quantidades absurdamente grandes de dados. Mas, uma vez de posse deles, como fazer para extrair informações dessas montanhas amorfas de dados? Nesse minicurso vamos apresentar o modelo de programação MapReduce: entender como ele funciona, para que serve e como construir aplicações usando-o. Vamos ver também como usar o Elastic MapReduce, o serviço da Amazon que cria clusters MapReduce sob-demanda, para que você não se preocupe em administrar e conseguir acesso a um cluster de máquinas, mas em como fazer seu código digerir de forma distribuída os dados que você possui. Veremos exemplos práticos em ação e codificaremos juntos alguns desafios.
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Parallel computing(2)
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- 33. Questions?