MATLAB CENTER FOR INTEGRATED RESEARCH COMPUTING http www
MATLAB CENTER FOR INTEGRATED RESEARCH COMPUTING http: //www. circ. rochester. edu/wiki/index. php/Matlab. Workshop
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Running Matlab Interactively �To use Matlab's GUI you must connect through suitable environment �Why NX? Faster than using X 11 forwarding (compresses data) Has clients for all major operating systems Saves your session when you are disconnected �You don’t have to restart Matlab if your network connection drops. �Instructions for obtaining/installing/connecting through NX can be found at: http: //www. circ. rochester. edu/wiki/index. php/NX_Cluster
Running Matlab Interactively �To use GUI you must connect through suitable environment �Why NX? Faster than using X 11 forwarding (compresses data) Has clients for all major operating systems Saves your session when you are disconnected �You don’t have to restart Matlab if your network connection drops. http: //www. circ. rochester. edu/wiki/index. php/NX_Cluster �The link to Matlab on the NX desktop menu bar actually launches a script that submits a job to the blue hive cluster. It does not run Matlab locally, but instead uses X 11 forwarding between compute nodes and the NX server.
Running Matlab Interactively �We could also launch a terminal on the NX desktop and submit an interactive job from there.
Running Matlab Interactively �We could also launch a terminal on the NX desktop and submit an interactive job from there. qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1 module load matlab-R 2013 a-local matlab -singlecompthread
Running Matlab Interactively �We could also launch a terminal on the NX desktop and submit an interactive job from there. qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1 module load matlab -singlecompthread �Occasionally the Matlab Desktop will respond slowly to commands which can be VERY frustrating. One work around is to use the terminal window as the "desktop" – while still retaining the ability to plot windows / access help etc. . . matlab -nodesktop -nosplash
Running Matlab Interactively �We could also launch a terminal on the NX desktop and submit an interactive job from there. qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1 module load matlab -singlecompthread �Occasionally the Matlab Desktop will respond slowly to commands which can be VERY frustrating. One work around is to use the terminal window as the Desktop – while still retaining the ability to plot windows / access help etc. . . matlab -nodesktop -nosplash �And finally you may not need to plot anything on the screen – or use any of the GUI features. In that case you can. . . matlab -nodisplay
Running Matlab Interactively � If you are running Matlab without a connected display you can still make plots directly to a file in Matlab H=hilb(1000); Z=fft 2(H); f=figure('Visible', 'off'); imagesc(log(abs(Z))); print('-dpdf', '-r 300', 'fig 1. pdf') � You may also find it useful to enter many commands into a script file and then execute the script – so you can do something else while Matlab creates several figures etc. . . This is also a good way to develop a script for batch jobs.
Running Matlab Interactively �If you are running a machine that has an X-server – you can bypass NX and just use X 11 Forwarding. Though if your connection drops – your Matlab session (and your interactive job) will terminate ssh -X jcarrol 5@bluehive. circ. rochester. edu qsub -I -X -q interactive –l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1 �Also if you do use NX and you finish using Matlab – please terminate your session instead of just disconnecting. This will cleanup any jobs you have running and free up resources for other users.
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Running Matlab in Batch Mode �To submit a job in batch mode we need to create a batch script #PBS -N Matlab sample_script. pbs #PBS -q standard #PBS -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab -nodisplay -r "sample_script" �And a Matlab script containing the commands to run H=hilb(1000); Z=fft 2(H); imagesc(log(abs(Z))); print('-dpdf', '-r 300', 'fig 1 -batch. pdf'); sample_script. m �And we should place both files in a folder on /scratch where we will submit the job from. qsub sample_script. pbs
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Using Job Arrays in Batch Mode �To use a job array we need to use the “-t” PBS option #PBS -t 0 -3 sample_script. pbs #PBS -N Matlab #PBS -q standard #PBS -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab -nodisplay -r "sample_function($PBS_ARRAYID)" �And turn our Matlab script into a function that takes arguments. (sample_function. m) sample_function. m function sample_function(n) H=hilb(n); Z=fft 2(H); imagesc(log(abs(Z))); print('-dpdf', '-r 300', sprintf('%s%03 d%s', 'fig 1 -batch_', n, '. pdf'));
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Symmetric Multi-Processing �By default Matlab uses all cores on a given node for operations that can be threaded – regardless of the submission script. Arrays and matrices • Basic information: ISFINITE, ISINF, ISNAN, MAX, MIN • Operators: +, -, . *, . /, . , . ^, *, ^, (MLDIVIDE), /
Symmetric Multi-Processing �To be sure you only use the resources you request, you should either request an entire node and all of the CPU’s. . . qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=8, vmem=16 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab �Or request a single cpu and specify that Matlab should only use a single thread qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=1, vmem=4 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab -single. Comp. Thread
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Using GPUs with Matlab �Matlab can use GPUs to do calculations, provided a GPU is available on the node Matlab is running on. qsub -I -X -q blugpu -l walltime=1: 00, nodes=1: ppn=1: gpus=1, vmem=16 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab module load cuda matlab �We can query the connected GPUs from within Matlab using gpu. Device. Count() gpu. Device()
Using GPUs with Matlab �Matlab can use GPUs to do calculations, provided a GPU is available on the node Matlab is running on. qsub -I -X -q blugpu -l walltime=1: 00, nodes=1: ppn=1: gpus=1, vmem=16 gb, pvmem=-1 . /usr/local/modules/init/bash module load matlab module load cuda matlab �We can query the connected GPUs from within Matlab using gpu. Device. Count() gpu. Device() �And obtain a list of GPU supported functions using methods('gpu. Array')
Using GPUs with Matlab �So there is a 2 D FFT – but no Hilbert function. . . H=hilb(1000); H_=gpu. Array(H); Z_=fft 2(H_); Z=gather(Z_); imagesc(log(abs(Z))); Distribute data to GPU FFT performed on GPU Gather data from GPU onto CPU �We could do the log and abs functions on the GPU as well. H=hilb(1000); H_=gpu. Array(H); Z_=fft 2(H_); imagesc(gather(log(abs(Z_)));
Using GPUs with Matlab �For our example, doing the FFT on the GPU is 7 x faster. (4 x if you include moving the data to the GPU and back) >> H=hilb(5000); >> tic; A=gather(gpu. Array(H)); toc Elapsed time is 0. 161166 seconds. >> tic; A=gather(fft 2(gpu. Array(H))); toc Elapsed time is 0. 348159 seconds. >> tic; A=fft 2(H); toc Elapsed time is 1. 210464 seconds.
Using GPUs with Matlab �Matlab has no built in hilb() function that can run on the GPU – but we can write our own function(kernel) in cuda – and save it to hilbert. cu __global__ void Hilbert. Kernel( double * const out, size_t const num. Rows, size_t const num. Cols) { const int row. Idx = block. Idx. x * block. Dim. x + thread. Idx. x; const int col. Idx = block. Idx. y * block. Dim. y + thread. Idx. y; if ( row. Idx >= num. Rows ) return; if ( col. Idx >= num. Cols ) return; size_t linear. Idx = row. Idx + col. Idx*num. Rows; out[linear. Idx] = 1. 0 / (double)(1+row. Idx+col. Idx) ; } �And compile it with nvcc to generate a Parallel Thread e. Xecution file nvcc -ptx hilbert. cu
Using GPUs with Matlab �We have to initialize the kernel and specify the grid size before executing the kernel. H_=gpu. Array. zeros(1000); hilbert_kernel=parallel. gpu. CUDAKernel('hilbert. ptx', 'hilbert. cu'); hilbert_kernel. Grid. Size=size(H_); H_=feval(hilbert_kernel, H_, 1000); Z_=fft 2(H_); imagesc(gather(log(abs(Z_)))); �The default for matlab kernel’s is 1 thread per block, but we could create fewer blocks that were each 10 x 10 threads. hilbert_kernel. Thread. Block. Size=[10, 1]; hilbert_kernel. Grid. Size=[100, 100]; �In any event, our speedup is a factor of 50 compared to 1 CPU.
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Parallel Computing Toolbox �As an alternative you can also use the Parallel Computing Toolbox. This supports parallelism via MPI qsub -I -X -q interactive -l walltime=1: 00, nodes=1: ppn=8, vmem=16 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab -single. Comp. Thread �You can enable a pool of matlab workers using matlabpool(8) �You should create a pool that is the same size as the number of processors you requested in your job submission. Matlab also sells licenses for using a Distributed Computing Server which allows for matlabpools that use more than just the local node.
Parallel Computing Toolbox �You can achieve parallelism in several ways: parfor loops – execute for loops in parallel smpd – execute instructions in parallel (using ‘labindex’ or ‘drange’) pmode – interactive version of smpd distributed arrays – very similar to gpu. Arrays.
Parallel Computing Toolbox �You can achieve parallelism in several ways: parfor loops – execute for loops in parallel smpd – execute instructions in parallel (using ‘labindex’ or ‘drange’) pmode – interactive version of smpd distributed arrays – very similar to gpu. Arrays. matlabpool(4) parfor n=1: 100 H=hilb(n); Z=fft 2(H); f=figure('Visible', 'off'); imagesc(log(abs(Z))); print('-dpdf', '-r 300', sprintf('%s%03 d%s', 'fig 1 -batch_', n, '. pdf')); end matlabpool close
Parallel Computing Toolbox �You can achieve parallelism in several ways: parfor loops – execute for loops in parallel smpd – execute instructions in parallel (using ‘labindex’ or ‘drange’) pmode – interactive version of smpd distributed arrays – very similar to gpu. Arrays. matlabpool(4) spmd for n=drange(1: 100) H=hilb(n); Z=fft 2(H); f=figure('Visible', 'off'); imagesc(log(abs(Z))); end matlabpool close matlabpool(4) spmd for n=labindex: numlabs: 100 H=hilb(n); Z=fft 2(H); f=figure('Visible', 'off'); imagesc(log(abs(Z))); end matlabpool close
Parallel Computing Toolbox �You can achieve parallelism in several ways: parfor loops – execute for loops in parallel smpd – execute instructions in parallel (using ‘labindex’ or ‘drange’) pmode – interactive version of smpd distributed arrays – very similar to gpu. Arrays. pmode start 4 n=labindex; H=hilb(n); Z=fft 2(H); f=figure('Visible', 'off'); imagesc(log(abs(Z))); print('-dpdf', '-r 300', sprintf('%s%03 d%s', 'fig 1 -batch_', n, '. pdf')); pmode lab 2 client H 3 H 3 pmode close
Parallel Computing Toolbox �You can achieve parallelism in several ways: parfor loops – execute for loops in parallel smpd – execute instructions in parallel (using ‘labindex’ or ‘drange’) pmode – interactive version of smpd distributed arrays – very similar to gpu. Arrays Example using distributed arrays Example using gpu. Array H=hilb(1000); H_=gpu. Array(H); Z_=fft 2(H_); Z=gather(Z_); imagesc(log(abs(Z))); matlabpool(8) H=hilb(1000); H_=distributed(H); Z_=fft(H_, [], 1), [], 2); Z=gather(Z_); imagesc(log(abs(Z))); matlabpool close
Parallel Computing Toolbox �What about building hilbert matrix in parallel? matlabpool(4) spmd Define partition codist=codistributor 1 d(1, [250, 250], [1000, 1000]); [i_lo, i_hi]=codist. global. Indices(1); Get local indices in x-direction Allocate space for local part H_local=zeros(250, 1000); for i=i_lo: i_hi for j=1: 1000 Initialize local array with H_local(i-i_lo+1, j)=1/(i+j-1); Hilbert values. end H_ = codistributed. build(H_local, codist); Assemble codistributed array end Now it's distributed like before! Z_=fft(H_, [], 1), [], 2); Z=gather(Z_); imagesc(log(abs(Z))); matlabpool close
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Using the Matlab Distributed Compute Engine �To get started, first cd into an empty directory and run mdce_init �This will generate 4 files: mdce_job. pbs – pbs submission script mdce_script. m – sample matlab script that uses parallel computing toolbox mdce_profile. m – matlab function that uses your environment variables to locate the matlab compute cluster for your job mdce_cleanup is an epilogue script that cleans up the matlab distributed compute server when your job terminates �Then you can submit the sample job with qsub mdce_job. pbs
Using the Matlab Distributed Compute Engine �Here is the job submission script #!/bin/bash #PBS -N Matlab_mdce #PBS -j oe #PBS -l nodes=2: ppn=8, pvmem=2000 mb #PBS -l walltime=1: 00 #PBS -l epilogue=mdce_cleanup #PBS -q standard #PBS -o matlab. log. /usr/local/modules/init/bash module load matlab-R 2013 a-local cd $PBS_O_WORKDIR pbs_mdce_start matlab -nodisplay -r "mdce_script" mdce_job. pbs This epilogue script is important to ensure that the cluster is taken down when your job terminates Note that other versions of matlab could take hours to start the matlab cluster!!! �This script loads the matlab module, starts the cluster with pbs_mdce_start, and runs the matlab script "mdce_script. m"
Using the Matlab Distributed Compute Engine �And here is the sample matlab script profile=mdce_profile() mdce_script. m matlabpool('open', profile) parfor n=1: matlabpool('size') H=hilb(n); Z=fft 2(H); imagesc(log(abs(Z))); print('-dpdf', '-r 300', sprintf('%s%03 d%s', 'fig 1 -batch', n, '. pdf')); end matlabpool('close') �The mdce_profile() function returns a profile that can be used to connect to the mdce cluster for your job. You can then use matlabpool or pmode, or spmd etc. . . to startup parallel computations across the matlab cluster.
Using the Matlab Distributed Compute Engine �For interactive mode, you can use the q. Matlab_mdce script. This script will inherit your matlab path from your environment, so be sure to load the matlab-R 2013 a-local module to speed up the initilization of the cluster. mkdir /scratch/jcarrol 5/matlab_mdce cd /scratch/jcarrol 5/matlab_mdce module load matlab-R 2013 a-local q. Matlab_mdce 4 8 16 �This will create a matlab cluster which in this case consists of 4 nodes each with 8 workers and 16 GB of memory per. To use the matab cluster, load the profile using the mdce_profile() function and then open the pool of workers with matlabpool – or pmode etc. . . profile=mdce_profile() matlabpool('open', profile)
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Using p. Matlab �p. Matlab is an alternative method to get distributed matlab functionality without relying on Matlab’s Distributed Computing Server. �It is built on top of Map. MPI (an MPI implementation for matlab – written in matlab - that uses file I/O for communication) �It supports various operations on distributed arrays (up to 4 D) Remapping, aggregating, finding non-zero entries, transposing, ghosting Elementary math functions (trig, exponential, complex, remainder/rounding) 2 D Convolutions, FFTs, Discrete Cosine Transform � FFT's need to be properly mapped (cannot be distributed along transform dimension). �It does not have as much functionality as the parallel computing toolbox – but it does support ghosting and more flexible partitioning!
Using p. Matlab �Since p. Matlab works by launching other Matlab instances – we need them to startup with p. Matlab functionality. To do so we need to add a few lines to our startup. m file in our matlab path. addpath('/usr/local/p. Matlab/Matlab. MPI/src'); addpath('/usr/local/p. Matlab/src'); rehash; p. Matlab. Globals. Init;
Running p. Matlab in Batch Mode �To submit a job in batch mode we need to create a batch script #PBS -N Matlab sample_script. pbs #PBS -q standard #PBS -l walltime=1: 00, nodes=2: ppn=8, vmem=32 gb, pvmem=-1. /usr/local/modules/init/bash module load matlab -nodisplay -r "pmatlab_launcher" �And a Matlab script to launch the p. Matlab script [sreturn, n. Procs]=system('cat $PBS_NODEFILE | wc -l'); pmatlab_launcher. m n. Procs=str 2 num(n. Procs); [sreturn, machines]=system('cat $PBS_NODEFILE | uniq'); machines=regexp(machines, 'n', 'split'); machines=machines(1: size(machines, 2)-1); eval(p. RUN('pmatlab_script', n. Procs, machines));
Running p. Matlab in Batch Mode �And finally we have our pmatlab script. Xmap=map([Np 1], {}, 0: Np-1); H_=zeros(1000, Xmap); [I 1, I 2]=global_block_range(H_); H_local=zeros(I 1(2)-I 1(1)+1, I 2(2)-I 2(1)+1); for i=I 1(1): I 1(2) for j=I 2(1): I 2(2) H_local(i-I 1(1)+1, j-I 2(1)+1)=1/(i+j-1); end H_=put_local(H_, H_local); Z_=fft(H_, [], 2), [], 1); Z=agg(Z_); if (p. MATLAB. my_rank == p. MATLAB. leader) f=figure('Visible', 'off'); imagesc(log(abs(Z))); print('-dpdf', '-r 300', 'fig 1. pdf'); end map for distributing array Distributed matrix constructor Indices for local portion of array Allocate and populate local portion of array with Hilbert matrix values X = put_local(X, fft(local(X), [], 2)); Copy local values into distributed array Z= transpose_grid(X); Do y-fft and do x-fft. Z_ has different map Z = put_local(Z, fft(local(Z), [], 1)); Collect resulting matrix onto 'leader' Plot result from 'leader' matlab process pmatlab_script. m
Using p. Matlab �PBS is unaware of matlab sessions launched from 'p. RUN' and therefore cannot properly clean up if something goes wrong (job runs out of walltime etc. . . ) To avoid leaving orphaned Matlab processes on other machines, modify your PBS script #PBS -l epilogue=epilogue_script. sh to run this epilogue script which must have user-execute permissions #!/bin/bash cd $PBS_O_WORKDIR/Mat. MPIa echo "running prologue" pwd; for i in `ls pid. *`; do machine=`echo $i | awk -F '. ' '{print $2}'`; pid=`echo $i | awk -F '. ' '{print $3}'`; ssh $machine "(kill -9 $pid)" && rm -rf $i; done epilogue_script. sh
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Compiling Mex Code �There is a configuration file for mex that you can place in your ~/. matlab/R 2012 b/ folder – or whatever version of matlab you are using. The file can be downloaded from the CIRC wiki http: //www. circ. rochester. edu/wiki/index. php/Mexopts. sh
Compiling Mex Code �C, C++, or Fortran routines can be called from within Matlab. #include "fintrf. h" subroutine mexfunction(nlhs, plhs, nrhs, prhs) mw. Pointer : : plhs(*), prhs(*) integer : : nlhs, nrhs mw. Pointer : : mx. Get. Pr mw. Pointer : : mx. Create. Double. Matrix real(8) : : mx. Get. Scalar mw. Pointer : : pr_out integer : : n n = nint(mx. Get. Scalar(prhs(1))) plhs(1) = mx. Create. Double. Matrix(n, n, 0) pr_out = mx. Get. Pr(plhs(1)) call compute(%VAL(pr_out), n) end subroutine mexfunction subroutine compute(h, n) integer : : n real(8) : : h(n, n) integer : : i, j do i=1, n do j=1, n h(i, j)=1 d 0/(i+j-1 d 0) end do end subroutine compute mex hilbert. f 90 >> H=hilbert(10)
Outline Part I – Interacting with Matlab �Running Matlab interactively Accessing the GUI Using the terminal for command entry Using just the terminal �Running Matlab in batch mode �Using PBS job arrays to do embarrassingly parallel computations Part II – Speeding up Matlab Computations �Symmetric Multi-Processing with Matlab �Accelerating Matlab computations with GPUs �Running Matlab in distributed memory environments Using the Parallel Computing Toolbox Using the Matlab Distributed Compute Engine Server Using p. Matlab Part III – Mixing Matlab and Fortran/C code �Compiling MEX code from C/Fortran �Turning Matlab routines into C code
Turning Matlab code into C �First we create a log_abs_fft_hilb. m function result = log_abs_fft_hilb(n) result=log(abs(fft 2(hilb(n)))); �And then we run >> codegen log_abs_fft_hilb. m –args {uint 32(0)} �This will produce a mex file that we can test. >> A=log_abs_fft_hilb_mex(uint 32(16)); >> B=log_abs_fft_hilb(16); >> max(abs(A-B))) ans = 8. 8818 e-16 �We could have specified the type of 'n' in our matlab function result = log_abs_fft_hilb(n) assert(isa(n, 'uint 32')); result=log(abs(fft 2(hilb(n))));
Turning Matlab code into C �Now we can also export a static library that we can link to: >> codegen log_abs_fft_hilb. m -config coder. config('lib') -args {'uint 32(0)'} �This will create a subdirectory codegen/lib/log_abs_fft_hilb that will have the source files '. c and. h' as well as a compiled object files '. o' and a library 'log_abs_fft_hilb. a' �The source files are portable to any platform with a 'C' compiler (ie Blue. Streak). We can rebuild the library on Blue. Streak by running mpixlc –c *. c ar rcs log_abs_fft_hilb. a *. o
Turning Matlab code into C �To use the function, we still need to write a main subroutine that links to it. This requires working with matlab's variable types (which include dynamically resizable arrays) #include "stdio. h" #include "rtwtypes. h" Matlab type definitions #include "log_abs_fft_hilb_types. h" void main() { uint 32_T n=64; Argument to Matlab function emx. Array_real_T *result; Return value of Matlab function int 32_T i, j; emx. Init_real_T(&result, 2); Initialize Matlab array to have rank 2 log_abs_fft_hilb(n, result); Call matlab function for(i=0; i<result->size[0]; i++) { for(j=0; j<result->size[1]; j++) { printf("%f ", result->data[i+result->size[0]*j]); Output result in } column major order printf("n"); } Free up memory associated with return array emx. Free_real_T(&result); } Exported code was 2 x slower.
Turning Matlab code into C �And here is another example of calling 2 D fft's on real data void main() { int 32_T q 0; int 32_T i; int 32_T n=8; emx. Array_creal_T *result; emx. Array_real_T *input; emx. Init_creal_T(&result, 2); emx. Init_real_T(&input, 2); q 0 = input->size[0] * input->size[1]; input->size[0]=n; input->size[1]=n; emx. Ensure. Capacity((emx. Array__common *)input, q 0, (int 32_T)sizeof(real_T)); for(j=0; j<input->size[1]; j++ { for(i=0; i<input->size[0]; i++) { input->data[i+input->size[0]*j]=1. 0 / (real_T)(i+j+1); } } my_fft(input, result); for(i=0; i<result->size[0]; i++) { for(j=0; j<result->size[1]; j++) { printf("[% 10. 4 f, % 10. 4 f] ", result->data[i+result->size[0]*j]. re, result->data[i+result->size[0]*j]. im); } printf("n"); } emx. Free_creal_T(&result); emx. Free_real_T(&input); }
Turning Matlab code into C �Exported FFT's only work on vectors of length 2 N �Error checking is disabled in exported C code �Mex code will have the same functionality as exported C code, but will also have error checking. It will warn about doing FFT's on arbitrary length vectors, etc. . . �Always test your mex code!
Matlab code is not that different from C code #include <stdio. h> #include <math. h> #include <complex. h> #include <fftw 3. h> void main() { int n=4096; int i, j; double complex temp[n][n], input[n][n]; double result[n][n]; fftw_plan p; p=fftw_plan_dft_2 d(n, n, &input[0][0], &temp[0][0], FFTW_FORWARD, FFTW_ESTIMATE); for (i=0; i<n; i++){ for(j=0; j<n; j++) { input[i][j]=(double complex)(1. 0/(double)(i+j+1)); } } fftw_execute(p); for (i=0; i<n; i++){ for(j=0; j<n; j++) { result[i][j]=log(cabs(temp[i][j])); } } for (i=0; i<n; i++){ for(j=0; j<n; j++) { printf("%f ", result[i][j]); } } fftw_destroy_plan(p); } Or you can write your own 'C' code that uses open source mathematical libraries (ie fftw).
- Slides: 64
