Presentations text content in Parallel computing with MATLAB
Slide1
Parallel computing with MATLAB
Slide2Going Beyond Serial MATLAB Applications
MATLAB
Desktop (Client)
Worker
Worker
Worker
Worker
Worker
Worker
Slide3Programming Parallel Applications (CPU)
Built-in support with toolboxes
Ease of Use
Greater Control
Slide4Example: Optimizing Cell Tower PositionBuilt-in parallel support
With Parallel Computing Toolbox use built-in parallel algorithms in Optimization Toolbox Run optimization in parallelUse pool of MATLAB workers
Slide5Tools Providing Parallel Computing Support
Optimization Toolbox Global Optimization ToolboxStatistics ToolboxSignal Processing ToolboxNeural Network ToolboxImage Processing Toolbox…
BLOCKSETS
Directly leverage functions in Parallel Computing Toolbox
www.mathworks.com/builtin-parallel-support
Slide6Agenda
Task parallel applicationsGPU accelerationData parallel applicationsUsing clusters and grids
Slide7Ideal problem for parallel computingNo dependencies or communications between tasksExamples: parameter sweeps, Monte Carlo simulations
Independent Tasks or Iterations
Time
Time
blogs.mathworks.com/
loren
/2009/10/02/using-
parfor
-loops-getting-up-and-running/
Slide8Example: Parameter Sweep of ODEsParallel for-loops
Parameter sweep of ODE systemDamped spring oscillatorSweep through different values of damping and stiffnessRecord peak value for eachsimulationConvert for to parforUse pool of MATLAB workers
Slide9The Mechanics
of
parfor
Loops
Pool of MATLAB Workers
a = zeros(10, 1)
parfor
i = 1:10
a(i) = i;
end
a
a(i) = i;
a(i) = i;
a(i) = i;
a(i) = i;
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Slide10Agenda
Task parallel applicationsGPU accelerationData parallel applicationsUsing clusters and grids
Slide11What is a Graphics Processing Unit (GPU)
Originally for graphics acceleration, now also used for scientific calculations
Massively parallel array of integer andfloating point processorsTypically hundreds of processors per cardGPU cores complement CPU coresDedicated high-speed memory
* Parallel Computing Toolbox requires NVIDIA GPUs with Compute Capability 1.3 or higher, including NVIDIA Tesla 20-series products. See a complete listing at www.nvidia.com/object/cuda_gpus.html
Slide12Core 1
Core 3
Core 4
Core 2
Cache
Performance Gain with More Hardware
Using More Cores (CPUs)
Using GPUs
Device Memory
GPU cores
Device Memory
Slide13Example: Mandelbrot set
The color of each pixel is the result of hundreds or thousands or iterationsEach pixel is independent of the other pixelsHundres of thousands of pixels
Slide14Real-world performance increaseSolving a wave equation
Intel Xeon Processor X5650, NVIDIA Tesla C2050 GPU
Grid SizeCPU (s)GPU(s)Speedup64 x 640.10040.35530.28128 x 1280.19310.33680.57256 x 2560.58880.42171.4512 x 5122.81630.82433.41024 x 102413.47972.49795.42048 x 204874.99049.95677.5
Slide15Programming Parallel Applications (GPU)
Built-in support with toolboxesSimple programming constructs:gpuArray, gather Advanced programming constructs:arrayfun, spmdInterface for experts: CUDAKernel, MEX support
Ease of Use
Greater Control
www.mathworks.com/help/distcomp/run-cuda-or-ptx-code-on-gpu
www.mathworks.com/help/distcomp/run-mex-functions-containing-cuda-code
Slide16Agenda
Task parallel applicationsGPU accelerationData parallel applicationsUsing clusters and grids
Slide17Big data: Distributed Arrays
T
OOLBOXES
B
LOCKSETS
Distributed Array
Lives on the Cluster
Remotely Manipulate Array
from Desktop
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Slide18Big Data: Distributed Arrays
Pool of MATLAB Workers
y = distributed(rand(10));
Column 1:3 of y
Worker
Worker
Worker
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Column 7:8 of y
Column 9:10 of y
Column 4:6 of y
Slide19Demo: Approximation of π
Slide20
Programming Parallel Applications (CPU)
Built-in support with toolboxesSimple programming constructs:parfor, batch, distributedAdvanced programming constructs:createJob, labSend, spmd
Ease of Use
Greater Control
Slide21Agenda
Task parallel applicationsGPU accelerationData parallel applicationsUsing clusters and grids
Slide22Working on C3SE
Slide23Apply for a project with SNIC
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Parallel computing with MATLAB
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