# Introducción a la computación de altas prestaciones

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• Francisco Almeida y Francisco de Sande
• Departamento de Estadística, I.O. y Computación
• Universidad de La Laguna
• La Laguna, 12 de febrero de 2004
• Questions
• Why Parallel Computers?
• How Can the Quality of the Algorithms be Analyzed?
• How Should Parallel Computers Be Programmed?
• Why the Message Passing Programming Paradigm?
• Why de Shared Memory Programming Paradigm?

## Introduction to Parallel Computing

• OUTLINE
• Introduction to Parallel Computing
• Performance Metrics
• Models of Parallel Computers
• The MPI Message Passing Library
• Examples
• The OpenMP Shared Memory Library
• Examples

## Applications Demanding more Computational Power:

• Why Parallel Computers ?
• Applications Demanding more Computational Power:
• Artificial Intelligence
• Weather Prediction
• Biosphere Modeling
• Processing of Large Amounts of Data (from sources such as satellites)
• Combinatorial Optimization
• Image Processing
• Neural Network
• Speech Recognition
• Natural Language Understanding
• etc..
• Cost
• Performance
• 1960 s
• 1970 s
• 1980 s
• 1990 s
• SUPERCOMPUTERS

## Top500

• www.top500.org

## Speed-up

• Ts = Sequential Run Time: Time elapsed between the begining and the end of its execution on a sequential computer.
• Tp = Parallel Run Time: Time that elapses from the moment that a parallel computation starts to the moment that the last processor finishes the execution.
• Speed-up: T*s / Tp ? p
• T*s = Time of the best sequential algorithm to solve the problem.

## Efficiency

• In practice, ideal behavior of an speed-up equal to p is not achieved because while executing a parallel algorithm, the processing elements cannot devote 100% of their time to the computations of the algorithm.
• Efficiency: Measure of the fraction of time for which a processing element is usefully employed.
• E = (Speed-up / p) x 100 %

## Amdahl`s Law

• Amdahl`s law attempt to give a maximum bound for speed-up from the nature of the algorithm chosen for the parallel implementation.
• Seq = Proportion of time the algorithm needs to be spent in purely sequential parts.
• Par = Proportion of time that might be done in parallel
• Seq + Par = 1 (where 1 is for algebraic simplicity)
• Maximum Speed-up = (Seq + Par) / (Seq + Par / p) = 1 / (Seq + Par / p)
 % Seq Par Maximum Speed-up 0,1 0,001 0,999 500,25 0,5 0,005 0,995 166,81 1 0,01 0,99 90,99 10 0,1 0,9 9,91
• p = 1000

## Example

• A problem to be solved many times over several different inputs.
• Evaluate F(x,y,z)
• x in {1 , ..., 20}; y in {1 , ..., 10}; z in {1 , ..., 3};
• The total number of evaluations is 20*10*3 = 600.
• The cost to evaluate F in one point (x, y, z) is t.
• The total running time is t * 600.
• If t is equal to 3 hours.
• The total running time for the 600 evaluations is 1800 hours  75 days

## The Sequential Model

• RAM
• Von Neumann
• The RAM model express computations on von Neumann architectures.
• The von Neumann architecture is universally accepted for sequential computations.

## The Parallel Model

• PRAM
• BSP, LogP
• PVM, MPI, HPF, Threads, OPenMP
• Parallel Architectures
• Computational Models
• Programming Models
• Architectural Models

## Digital AlphaServer 8400

• Hardware
• C4-CEPBA
• 10 Alpha processors21164
• 2 Gb Memory
• 8,8 Gflop/s
• Shared Memory
• BusTopology

## SGI Origin 2000

• Hardware
• C4-CEPBA
• 64 R1000 processos
• 8 Gb memory
• 32 Gflop/s

## The SGI Origin 3000 Architecture (1/2)

• jen50.ciemat.es
• 160 processors MIPS R14000 / 600MHz
• On 40 nodes with 4 processors each
• Data and instruction cache on-chip
• Irix Operating System
• Hypercubic Network

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