Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen

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Mervin Stephens
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1 Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen Frank Graeber Application Engineering MathWorks Germany 2013 The MathWorks, Inc. 1

2 Speed up the serial code within core MATLAB Use the MATLAB Profiler to identify performance bottlenecks Techniques for addressing performance Vectorization Preallocation Consider readability and maintainability Looping vs. matrix operations Subscripted vs. linear vs. logical and more Public Webinar: 2

The Quest for Performance Once you improved the

If so, do you have access to a Multi-core or

3 The Quest for Performance Once you improved the performance of your serial MATLAB code Do you need to reduce your run-time further? Do you need to solve larger problems? If so, do you have access to a Multi-core or multi-processor computer? Computer cluster or cloud? Graphics processing unit (GPU)? 3

4 Utilizing Additional Processing Power Built-in multithreading Automatically enabled in MATLAB since R2008a Multiple threads in a single MATLAB computation engine Parallel Computing using explicit techniques Multiple computation engines on CPU controlled by a single session Parallel programming constructs for interactive and batch processing GPU programming for massively parallel problems 4

5 Parallel Computing for the Desktop Desktop Computer Local Implement & test parallel applications Gain speed on multicore systems Up to 12 local workers (computation engines) Take advantage of GPUs MATLAB Desktop (Client) Parallel Computing Toolbox Prototype code for your cluster 5

6 Scheduler Scaling Up to Clusters and Clouds Desktop Computer Computer Cluster Local Cluster MATLAB Desktop (Client) Parallel Computing Toolbox MATLAB Distributed Computing Server 6

7 Going Beyond Serial MATLAB Applications MATLAB Desktop (Client) Worker Worker Worker Worker Worker Worker Pool of Workers 7

8 Ease of Use Programming Parallel Applications (CPU) Built-in support with Toolboxes Simple programming constructs: parfor, batch, distributed, Advanced programming constructs: createjob, labsend, spmd, Greater Control 8

Processing Toolbox and more BLOCKSETS Directly leverage functions in Parallel

9 Tools Providing Parallel Computing Support Optimization Toolbox, Global Optimization Toolbox Statistics Toolbox Signal Processing Toolbox Neural Network Toolbox Image Processing Toolbox and more BLOCKSETS Directly leverage functions in Parallel Computing Toolbox 9

peak value for each simulation 1.2 1 0.8 0.6 0.4 0.2 m = 5, b = 2, k = 2 k b x m 5 m x b x k x 0 1,2,.

10 Displacement (x) Example: Parameter Sweep of ODEs Parallel for-loops Simulink Parameter sweep of ODE system Damped spring oscllator (also in Simulink) Sweep through different values of damping and stiffness Record peak value for each simulation m = 5, b = 2, k = 2 k b x m 5 m x b x k x 0 1,2,... 1,2,... Convert for to parfor m = 5, b = 5, k = Time (s) Use pool of MATLAB workers 10

81 325 1292 16 0.6 2 6 25 98 32 0.4 1 3 12 47 64 0.5 0.6 1.7 6 23 96 0.5 0.7 1.

11 Benchmark: Parameter Sweep of ODEs Scaling case study Computation time (minutes) Workers Number of combinations Processor: Intel Xeon E cores per node 11

GPU Programming with MATLAB One computation engine (client or worker) per GPU Massively parallel: Calculations can be broken into hundreds or thousands of

12 GPU Programming with MATLAB One computation engine (client or worker) per GPU Massively parallel: Calculations can be broken into hundreds or thousands of independent units of work Problem size takes advantage of many GPU cores Computationally intensive: Computation time significantly exceeds CPU/GPU data transfer time 12

13 Ease of Use GPU Programming Options Built-in support with Toolboxes Simple programming constructs: gpuarray, gather Advanced programming constructs: arrayfun, bsxfun, spmd Interface for experts: CUDAKernel, MEX support Greater Control 13

14 Example: Solving 2D Wave Equation GPU Computing Solve 2 nd order wave equation using spectral methods: 2 u t 2 = 2 u x u y 2 Run both on CPU and GPU Using gpuarray and overloaded functions (e.g., fft and ifft) 14

15 Comparison of Code 15

Benchmark: Solving 2D Wave Equation CPU vs GPU Grid Size CPU (s) GPU (s) Speedup 64 x 64 0.05 0.15 0.32 128 x 128 0.13 0.15 0.88 256 x 256 0.47 0.

16 Benchmark: Solving 2D Wave Equation CPU vs GPU Grid Size CPU (s) GPU (s) Speedup 64 x x x x x x Intel Xeon Processor W3690 (3.47GHz), NVIDIA Tesla K20 GPU 16

17 Learn More

Key Takeaways Analyze your code for bottlenecks using the MATLAB profiler Consider performance benefit of vector and matrix operations in MATLAB Leverage parallel computing tools on

18 Key Takeaways Analyze your code for bottlenecks using the MATLAB profiler Consider performance benefit of vector and matrix operations in MATLAB Leverage parallel computing tools on Multicore CPUs Compute clusters or clouds Graphics processing units (GPUs) On the use of multiple GPUs: 18

Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen

Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen Michael Glaßer Application Engineering MathWorks Germany 2014 The MathWorks, Inc. 1 Key Takeaways 1. Speed up your serial