2011 aerodef conf - parallel computing on the desktop

7/30/2019 2011 AeroDef Conf - Parallel Computing on the Desktop

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2011 The MathWorks, Inc.

Parallel Computing on the Desktop

Dave Forstot

Senior Application Engineer


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A Question to Consider

Do you want to speed up your algorithms?

If so

Do you have a multi-core or

multi-processor computer?

Do you have a high-end

graphics processing unit (GPU)?


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Introduction to the Parallel Computing Tools

Using Multi-core/Multi-processor Machines

Using Graphics Processing Units (GPUs)

Agenda


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Utilizing Additional Processing Power

Built-in multithreading

In core MATLAB

For specific matrix operations

Automatically enabled since R2008a

Parallel Computing Tools

Controlled by the MATLAB user

For a variety of applications Leverage CPUs and GPUs to speed applications further


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Parallel Computing on the Desktop

Use Parallel Computing Toolbox

Speed up parallel applications

on local computer

Take full advantage of desktop

power by using CPUs and GPUs

Separate computer cluster

not required

Desktop Computer

Parallel Computing Toolbox


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Scale Up to Clusters, Grids and Clouds

Desktop Computer


Computer Cluster

MATLAB Distributed Computing Server

Scheduler


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Parallel Computing enables you to

Speed up computations Work with large data

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Task-Parallel Data-Parallel

Task 1 Task 2 Task 3 Task 4Task 4Task 3Task 2Task 1


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Agenda


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Worker Worker

Worker

Worker

WorkerWorker

Worker

WorkerTOOLBOXES

BLOCKSETS

Going Beyond Serial MATLAB Applications


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Programming Parallel Applications

EaseofUse

G

reaterContro

l


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Using Additional Cores/Processors (CPUs)

Support built into Toolboxes

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aseofUse

G

reaterControl


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Example:Built-in Support for Parallelism in Other Tools

Use built-in support for


in Optimization Toolbox

Run optimization in parallel

Use pool of MATLAB workers


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Other Tools ProvidingParallel Computing Support

Optimization Toolbox

Global Optimization Toolbox

Statistics Toolbox SystemTest

Simulink Design Optimization

Bioinformatics Toolbox

Model-Based Calibration Toolbox

Worker

Worker

Worker

WorkerWorker

Worker

WorkerTOOLBOXES

BLOCKSETS

Directly leverage functions in Parallel Computing Toolbox


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Simple programming constructs:

parfor

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aseofUse

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reaterContro

l


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Task 1 Task 2 Task 3 Task 4

Task Parallel Applications

Time Time

TOOLBOXES

BLOCKSETS

Worker

Worker

Worker

Worker

Task 1 Task 2 Task 3 Task 4


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Example:Multiple Independent Simulations

Parameter sweep of ODE

system

Use pool of MATLAB workers

Convert for to parfor

Interleave serial and

parallel code

0 5 10 15 20 25-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Time (s)

Displacement(x)

m = 5, b = 2, k = 2

m = 5, b = 5, k = 5

0

,...2,1,...2,1

5

xkxbxm


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Simple programming constructs:

parfor

Full control of parallelization:

jobs and tasks

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aseofUse

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reaterContro

l


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Agenda


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Core 1

Core 3 Core 4

Core 2

Cache

Gaining Performance with More Hardware

Using More Cores (CPUs) Using GPUs

Device Memory


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What is a Graphics Processing Unit (GPU)

Originally used for graphics

acceleration, now available

for scientific calculations

Parallel array of integer and

floating point processors

Dozens to hundreds of processors per card

Less flexible than regular CPU cores

Separate memory from system

* Parallel Computing Toolbox requires NVIDIA GPU with Compute Capability 1.3 or greater


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Summary of Options for Targeting GPUs

Use GPU array interface with

MATLAB built-in functions

Execute custom functions on

elements of the GPU array

Create kernels from existing CUDA

code and PTX files

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aseofUse

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reaterContro

l


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Performance: A\b with Double Precision


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Performance Acceleration Options in theParallel Computing Toolbox

Technology ExampleMATLABWorkers

Execution Target

matlabpool parfor Required CPU cores

user-defined tasks createTask Required CPU cores

GPU-based

parallelismGPUArray No

NVIDIA GPU with

Compute Capability

1.3 or greater


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Summary

Speed up parallel applications on desktop by using


Take full advantage of CPU and GPU hardware

If required, use MATLAB Distributed Computing Serverto

Scale up to clusters, grids and clouds

Work with data that is too large to fit on to desktop computers


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For more information

Visit

www.mathworks.com/products/parallel-computing
http://www.mathworks.com/products/parallel-computinghttp://www.mathworks.com/products/parallel-computinghttp://www.mathworks.com/products/parallel-computinghttp://www.mathworks.com/products/parallel-computinghttp://www.mathworks.com/products/parallel-computing

2011 aerodef conf - parallel computing on the desktop

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