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So we exec the FD to /dev/null, # effectively closing config.log, so it can be properly (re)opened and # appended to by config.status. When coming back to configure, we # need to make the FD available again. if test "$no_create" != yes; then ac_cs_success=: ac_config_status_args= test "$silent" = yes && ac_config_status_args="$ac_config_status_args --quiet" exec 5>/dev/null $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false exec 5>>config.log # Use ||, not &&, to avoid exiting from the if with $? = 1, which # would make configure fail if this is the last instruction. $ac_cs_success || { (exit 1); exit 1; } fi if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then { $as_echo "$as_me:$LINENO: WARNING: unrecognized options: $ac_unrecognized_opts" >&5 $as_echo "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;} fi echo \ "------------------------------------------------- ${PACKAGE_NAME} Version ${PACKAGE_VERSION} Prefix: ${prefix}. Compiler: ${CXX} ${CXXFLAGS} ${MPI_INC} ${MPI_LIBDIR} ${MPI_LIBS} Package features: OpenMP enabled: ${openmp_enabled} MPI enabled: ${mpi_enabled} CUDA enabled: ${cuda_enabled} Now type 'make []' where the optional is: all - build all binaries install - install everything --------------------------------------------------" somoclu-1.2/CHANGES0000644000175000001440000000157612253742256010766 00000000000000Version 1.2: 2013-12-17 - Massive improvements in OpenMP parallelization. - MPI libraries are no longer mandatory. - Best matching units are saved. - Option for specifying an initial codebook for the map. - ESOM .lrn input format added. - Parsing of white-space characters corrected. - Long-named command line switches for specifying SOM dimensions. - Fine-grained control of which interim files to save across epochs - Option in Makefile for building shared library. Version 1.1.2: 2013-11-28 - Toroid maps were added. - Initial radius is exposed as a parameter via the command line interface. - Formats of codebook and U-matrix export are compatible with Databionic ESOM Tools for advanced visualisation. - Bug fixes: codebook update with a compact support was removed, NaN entry no longer appears in U-matrices. Version 1.0: 2013-05-14 - Initial release. somoclu-1.2/README.md0000644000175000001440000001531112253742256011242 00000000000000Somoclu == Somoclu is a cluster-oriented implementation of self-organizing maps. It relies on MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Key features: * Fast execution by parallelization: OpenMP, MPI, and CUDA are supported. * Planar and toroid maps. * Both dense and sparse input data are supported. * Large maps of several hundred thousand neurons are feasible. * Integration with Databionic ESOM Tools. For more information, refer to the following paper: Peter Wittek (2013). Somoclu: An Efficient Distributed Library for Self-Organizing Maps. [arXiv:1305.1422](http://arxiv.org/abs/1305.1422). Usage == Somoclu takes a plain text input file -- either dense or sparse data. Example files are included. $ [mpirun -np NPROC] somoclu [OPTIONs] INPUT_FILE OUTPUT_PREFIX Arguments: -c FILENAME Specify an initial codebook for the map. -e NUMBER Maximum number of epochs -k NUMBER Kernel type 0: Dense CPU 1: Dense GPU 2: Sparse CPU -m NUMBER Map type 0: Planar 1: Toroid -r NUMBER Initial radius (default: half the number of columns) -s NUMBER Save interim files (default: 0): 0: Do not save interim files 1: Save U-matrix only 2: Also save codebook and best matching -x, --columns NUMBER Number of columns in map (size of SOM in direction x) -y, --rows NUMBER Number of rows in map (size of SOM in direction y) Examples: $ somoclu data/rgbs.txt data/rgbs $ mpirun -np 4 somoclu -k 0 --rows 20 --columns 20 data/rgbs.txt data/rgbs Input File Formats == One sparse and two dense data formats are supported. All of them are plain text files. The entries can be separated by any white-space character. One row represents one data instance across all formats. Comment lines starting with a hash mark are ignored. The sparse format follows the [libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm/) guidelines. The first feature is zero-indexed. For instance, the vector [ 1.2 0 0 3.4] is represented as the following line in the file: 0:1.2 3:3.4. The file is parsed twice: once to get the number of instances and features, and the second time to read the data in the individual threads. The basic dense format includes the coordinates of the data vectors, separated by a white-space. Just like the sparse format, this file is parsed twice to get the basic dimensions right. The .lrn file of [Databionic ESOM Tools](http://databionic-esom.sourceforge.net/) is also accepted and it is parsed only once. The format is described as follows: % n % m % s1 s2 .. sm % var_name1 var_name2 .. var_namem x11 x12 .. x1m x21 x22 .. x2m . . . . . . . . xn1 xn2 .. xnm Here n is the number of rows in the file, that is, the number of data instances. Parameter m defines the number of columns in the file. The next row defines the column mask: the value 1 for a column means the column should be used in the training. Note that the first column in this format is always a unique key, so this should have the value 9 in the column mask. The row with the variable names is ignore by Somoclu. The elements of the matrix follow -- from here, the file is identical to the basic dense format, with the addition of the first column as the unique key. If the input file is sparse, but a dense kernel is invoked, Somoclu will execute and results will be incorrect. Invoking a sparse kernel on a dense input file is likely to lead to a segmentation fault. Efficient Parallel Execution == The CPU kernels use OpenMP to load multicore processors. On a single node, this is more efficient than launching tasks with MPI to match the number of cores. The MPI tasks replicated the codebook, which is especially inefficient for large maps. For instance, given a single node with eight cores, the following execution will use 1/8th of the memory, and will run 10-20% faster: $ somoclu -x 200 -y 200 data/rgbs.txt data/rgbs Or, equivalently: $ OMP_NUM_THREADS=8 somoclu -x 200 -y 200 data/rgbs.txt data/rgbs Avoid the following on a single node: $ OMP_NUM_THREADS=1 mpirun -np 8 somoclu -x 200 -y 200 data/rgbs.txt data/rgbs The same caveats apply for the sparse CPU kernel. Visualisation == The primary purpose of generating a map is visualisation. Somoclu does not come with its own functions for visualisation, since there are numerous generic tools that are capable of plotting high-quality figures. The output formats of the U-matrix and the codebook are compatible with [Databionic ESOM Tools](http://databionic-esom.sourceforge.net/) for more advanced visualisation. Dependencies == The only dependency is GCC, albeit other compiler chains might also work. Distributed systems and single-machine multicore execution is supported through MPI. The package was tested with OpenMPI, versions 1.3.2 and 1.6.5 were tested. It should also work with other MPI flavours. CUDA support is optional. CUDA versions 4.1, 5.0 and 5.5 are known to work. Compilation & Installation == From GIT repository first run $ ./autogen.sh Then follow the standard POSIX procedure: $ ./configure [options] $ make $ make install Options for configure --prefix=PATH Set directory prefix for installation By default Somoclu is installed into /usr/local. If you prefer a different location, use this option to select an installation directory. --without-mpi Disregard any MPI installation found. --with-mpi=MPIROOT Use MPI root directory. --with-mpi-compilers=DIR or --with-mpi-compilers=yes use MPI compiler (mpicxx) found in directory DIR, or in your PATH if =yes --with-mpi-libs="LIBS" MPI libraries [default "-lmpi"] --with-mpi-incdir=DIR MPI include directory [default MPIROOT/include] --with-mpi-libdir=DIR MPI library directory [default MPIROOT/lib] The above flags allow the identification of the correct MPI library the user wishes to use. The flags are especially useful if MPI is installed in a non-standard location, or when multiple MPI libraries are available. --with-cuda=/path/to/cuda Set path for CUDA Somoclu looks for CUDA in /usr/local/cuda. If your installation is not there, then specify the path with this parameter. 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But first, please read . somoclu-1.2/TODO0000644000175000001440000000006512253742256010453 00000000000000 - Integration with R. - Integration with Python. somoclu-1.2/src/0000755000175000001440000000000012253742256010631 500000000000000somoclu-1.2/src/denseCpuKernels.cpp0000644000175000001440000001343312253742256014353 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include "somoclu.h" /** Distance b/w a feature vector and a weight vector * = Euclidean * @param som_y * @param som_x * @param r - row number in the input feature file */ float get_distance(float* codebook, float* data, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nDimensions, unsigned int r) { float distance = 0.0f; for (unsigned int d = 0; d < nDimensions; d++) distance += (codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d] - *(data + r*nDimensions + d)) * (codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d] - *(data + r*nDimensions + d)); return distance; } /** Get node coords for the best matching unit (BMU) * @param coords - BMU coords * @param n - row num in the input feature file */ void get_bmu_coord(float* codebook, float* data, unsigned int nSomY, unsigned int nSomX, unsigned int nDimensions, int* coords, unsigned int n) { float mindist = 9999.99; float dist = 0.0f; /// Check nSomX * nSomY nodes one by one and compute the distance /// D(W_K, Fvec) and get the mindist and get the coords for the BMU. /// for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { dist = get_distance(codebook, data, som_y, som_x, nSomX, nDimensions, n); if (dist < mindist) { mindist = dist; coords[0] = som_x; coords[1] = som_y; } } } } void trainOneEpochDenseCPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus) { int p1[2] = {0, 0}; int *bmus = new int[nVectorsPerRank*2]; #pragma omp parallel default(shared) private(p1) { #pragma omp for for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask*nVectorsPerRank+n. * */ #include #include #include #include "somoclu.h" float euclideanDistanceOnToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = std::min(x2-x1, x1+nSomX-x2); unsigned int ydist = std::min(y2-y1, y1+nSomY-y2); //unsigned int xdist = std::min(std::abs(x1-x2), std::abs(x1+nSomX-x2)); //unsigned int ydist = std::min(std::abs(y1-y2), std::abs(y1+nSomY-y2)); return sqrt(float(xdist*xdist+ydist*ydist)); } float euclideanDistanceOnPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = x2-x1; unsigned int ydist = y2-y1; return sqrt(float(xdist*xdist+ydist*ydist)); } somoclu-1.2/src/Makefile.in0000644000175000001440000000351112253742256012616 00000000000000# @configure_input@ # Package-related substitution variables package = @PACKAGE_NAME@ version = @PACKAGE_VERSION@ tarname = @PACKAGE_TARNAME@ distdir = $(tarname)-$(version) # Prefix-related substitution variables prefix = @prefix@ exec_prefix = @exec_prefix@ bindir = @bindir@ libdir = @libdir@ # Tool-related substitution variables CXX = @CXX@ CXXFLAGS = @CXXFLAGS@ LIBS = @LIBS@ DEFS = @DEFS@ INSTALL = @INSTALL@ INSTALL_DATA = @INSTALL_DATA@ INSTALL_PROGRAM= @INSTALL_PROGRAM@ INSTALL_SCRIPT = @INSTALL_SCRIPT@ CUDA_CFLAGS = @CUDA_CFLAGS@ CUDA_LIBS = @CUDA_LIBS@ CUDA_LDFLAGS = @CUDA_LDFLAGS@ NVCC = @NVCC@ MPI_INC = @MPI_INC@ MPI_LIBDIR = @MPI_LIBDIR@ MPI_LIBS = @MPI_LIBS@ # VPATH-related substitution variables srcdir = @srcdir@ VPATH = @srcdir@ LIBOBJS=sparseCpuKernels.o io.o denseCpuKernels.o mapDistanceFunctions.o training.o ifdef CUDA_LIBS LIBOBJS+=denseGpuKernels.cu.co endif OBJS=$(LIBOBJS) somoclu.o all: somoclu lib: $(LIBOBJS) $(CXX) $(DEFS) $(CXXFLAGS) $(CUDA_LDFLAGS) ${MPI_LIBDIR} -shared -o libsomoclu.so $^ $(LIBS) $(CUDA_LIBS) ${MPI_LIBS} somoclu: $(OBJS) $(CXX) $(DEFS) $(CXXFLAGS) $(CUDA_LDFLAGS) ${MPI_LIBDIR} -o $@ $^ $(LIBS) $(CUDA_LIBS) ${MPI_LIBS} %.o: %.cpp $(CXX) $(DEFS) $(CXXFLAGS) ${MPI_INC} -I$(srcdir) -I.. -o $@ -c $(srcdir)/$< %.cu.co: %.cu $(NVCC) $(DEFS) $(CUDA_CFLAGS) ${MPI_INC} -I$(srcdir) -I.. -o $@ -c $(srcdir)/$< clean: -rm somoclu libsomoclu.so $(OBJS) 1>/dev/null install: $(INSTALL) -d $(DESTDIR)$(bindir) $(INSTALL_PROGRAM) -m 0755 somoclu \ $(DESTDIR)$(bindir) uninstall: -rm $(DESTDIR)$(bindir)/somoclu &>/dev/null Makefile: Makefile.in ../config.status cd .. && ./config.status $@ ../config.status: ../configure cd .. && ./config.status --recheck .PHONY: all clean install uninstall somoclu-1.2/src/sparseCpuKernels.cpp0000644000175000001440000001400312253742256014544 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include "somoclu.h" /** Distance b/w a feature vector and a weight vector * = Euclidean * @param som_y * @param som_x * @param r - row number in the input feature file */ float get_distance(float* codebook, svm_node **sparseData, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nDimensions, unsigned int r) { float distance = 0.0f; unsigned int j=0; for ( unsigned int d=0; d < nDimensions; d++ ) { if ( (int) d == sparseData[r][j].index ) { distance += (codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]- sparseData[r][j].value) * (codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]- sparseData[r][j].value); ++j; } else { distance += codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]* codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]; } } return distance; } /** Get node coords for the best matching unit (BMU) * @param coords - BMU coords * @param n - row num in the input feature file */ void get_bmu_coord(float* codebook, svm_node **sparseData, unsigned int nSomY, unsigned int nSomX, unsigned int nDimensions, int* coords, unsigned int n) { float mindist = 9999.99; float dist = 0.0f; /// Check nSomX * nSomY nodes one by one and compute the distance /// D(W_K, Fvec) and get the mindist and get the coords for the BMU. /// for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { dist = get_distance(codebook, sparseData, som_y, som_x, nSomX, nDimensions, n); if (dist < mindist) { mindist = dist; coords[0] = som_x; coords[1] = som_y; } } } } void trainOneEpochSparseCPU(int itask, svm_node **sparseData, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus) { int p1[2] = {0, 0}; int *bmus = new int[nVectorsPerRank*2]; #pragma omp parallel default(shared) private(p1) { #pragma omp for for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask*nVectorsPerRank+n. * */ #include #include #include #include #include #include "somoclu.h" using namespace std; /// For synchronized timing #ifndef MPI_WTIME_IS_GLOBAL #define MPI_WTIME_IS_GLOBAL 1 #endif // Default parameters #define N_EPOCH 10 #define N_SOM_X 50 #define N_SOM_Y 50 #define KERNEL_TYPE 0 #define MAP_TYPE 0 #define SNAPSHOTS 0 void processCommandLine(int argc, char** argv, string *inFilename, string* outPrefix, unsigned int *nEpoch, unsigned int *radius, unsigned int *nSomX, unsigned int *nSomY, unsigned int *kernelType, unsigned int *mapType, unsigned int *snapshots, string *initialCodebookFilename); /* -------------------------------------------------------------------------- */ int main(int argc, char** argv) /* -------------------------------------------------------------------------- */ { int rank = 0; int nProcs = 1; #ifdef HAVE_MPI /// /// MPI init /// MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &nProcs); MPI_Barrier(MPI_COMM_WORLD); #endif unsigned int nEpoch = 0; unsigned int nSomX = 0; unsigned int nSomY = 0; unsigned int kernelType = 0; unsigned int mapType = 0; unsigned int radius = 0; unsigned int snapshots = 0; string inFilename; string initialCodebookFilename; string outPrefix; if (rank==0) { processCommandLine(argc, argv, &inFilename, &outPrefix, &nEpoch, &radius, &nSomX, &nSomY, &kernelType, &mapType, &snapshots, &initialCodebookFilename); #ifndef CUDA if (kernelType == DENSE_GPU) { cerr << "Somoclu was compile without GPU support!\n"; my_abort(1); } #endif } #ifdef HAVE_MPI MPI_Bcast(&nEpoch, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&radius, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&nSomX, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&nSomY, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&kernelType, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&mapType, 1, MPI_INT, 0, MPI_COMM_WORLD); char *inFilenameCStr = new char[255]; if (rank == 0) { strcpy(inFilenameCStr,inFilename.c_str()); } MPI_Bcast(inFilenameCStr, 255, MPI_CHAR, 0, MPI_COMM_WORLD); inFilename = inFilenameCStr; double profile_time = MPI_Wtime(); #endif float * dataRoot = NULL; unsigned int nDimensions = 0; unsigned int nVectors = 0; if(rank == 0 ) { if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { dataRoot = readMatrix(inFilename, nVectors, nDimensions); } else { readSparseMatrixDimensions(inFilename, nVectors, nDimensions); } } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); MPI_Bcast(&nVectors, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&nDimensions, 1, MPI_INT, 0, MPI_COMM_WORLD); #endif unsigned int nVectorsPerRank = ceil(nVectors / (1.0*nProcs)); // Allocate a buffer on each node float* data = NULL; svm_node **sparseData; sparseData = NULL; if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { #ifdef HAVE_MPI // Dispatch a portion of the input data to each node data = new float[nVectorsPerRank*nDimensions]; MPI_Scatter(dataRoot, nVectorsPerRank*nDimensions, MPI_FLOAT, data, nVectorsPerRank*nDimensions, MPI_FLOAT, 0, MPI_COMM_WORLD); #else data = dataRoot; #endif } else { int currentRankProcessed = 0; while (currentRankProcessed < nProcs) { if (rank == currentRankProcessed) { sparseData=readSparseMatrixChunk(inFilename, nVectors, nVectorsPerRank, rank*nVectorsPerRank); } currentRankProcessed++; #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif } } if(rank == 0) { // No need for root data any more if compiled with MPI #ifdef HAVE_MPI if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { delete [] dataRoot; } #endif cout << "nVectors: " << nVectors << " "; cout << "nVectorsPerRank: " << nVectorsPerRank << " "; cout << "nDimensions: " << nDimensions << " "; cout << endl; } #ifdef CUDA if (kernelType == DENSE_GPU) { setDevice(rank, nProcs); initializeGpu(data, nVectorsPerRank, nDimensions, nSomX, nSomY); } #endif #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif // TRAINING train(rank, data, sparseData, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, nEpoch, radius, outPrefix, snapshots, kernelType, mapType, initialCodebookFilename); if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { delete [] data; } else { delete [] sparseData; } #ifdef HAVE_MPI profile_time = MPI_Wtime() - profile_time; if (rank == 0) { cerr << "Total Execution Time: " << profile_time << endl; } #endif #ifdef CUDA if (kernelType == DENSE_GPU) { freeGpu(); } #endif #ifdef HAVE_MPI MPI_Finalize(); #endif return 0; } void printUsage() { cout << "Usage:\n" \ " [mpirun -np NPROC] somoclu [OPTIONs] INPUT_FILE OUTPUT_PREFIX\n" \ "Arguments:\n" \ " -c FILENAME Specify an initial codebook for the map." \ " -e NUMBER Maximum number of epochs (default: " << N_EPOCH << ")\n" \ " -k NUMBER Kernel type (default: " << KERNEL_TYPE << "): \n" \ " 0: Dense CPU\n" \ " 1: Dense GPU\n" \ " 2: Sparse CPU\n" \ " -m NUMBER Map type (default: " << MAP_TYPE << "): \n" \ " 0: Planar\n" \ " 1: Toroid\n" \ " -r NUMBER Initial radius (default: half of the map in direction x)\n" \ " -s NUMBER Save interim files (default: 0):\n" \ " 0: Do not save interim files\n" \ " 1: Save U-matrix only\n" \ " 2: Also save codebook and best matching neurons\n" \ " -x, --columns NUMBER Number of columns in map (size of SOM in direction x) (default: " << N_SOM_X << ")\n" \ " -y, --rows NUMBER Number of rows in map (size of SOM in direction y) (default: " << N_SOM_Y << ")\n" \ "Examples:\n" \ " somoclu data/rgbs.txt data/rgbs\n" " mpirun -np 4 somoclu -k 0 -x 20 -y 20 data/rgbs.txt data/rgbs\n"; } void processCommandLine(int argc, char** argv, string *inFilename, string* outPrefix, unsigned int *nEpoch, unsigned int *radius, unsigned int *nSomX, unsigned int *nSomY, unsigned int *kernelType, unsigned int *mapType, unsigned int *snapshots, string *initialCodebookFilename) { // Setting default values *nEpoch = N_EPOCH; *nSomX = N_SOM_X; *nSomY = N_SOM_Y; *kernelType = KERNEL_TYPE; *snapshots = SNAPSHOTS; *mapType = MAP_TYPE; *radius = 0; static struct option long_options[] = { {"rows", required_argument, 0, 'y'}, {"columns", required_argument, 0, 'x'}, {0, 0, 0, 0} }; int c; extern int optind, optopt; int option_index = 0; while ((c = getopt_long (argc, argv, "hx:y:e:k:m:r:s:c:", long_options, &option_index)) != -1) { switch (c) { case 'c': *initialCodebookFilename = optarg; break; case 'e': *nEpoch = atoi(optarg); if (*nEpoch<=0) { cerr << "The argument of option -e should be a positive integer.\n"; my_abort(1); } break; case 'h': printUsage(); my_abort(0); break; case 'k': *kernelType = atoi(optarg); if (*kernelTypeSPARSE_CPU) { cerr << "The argument of option -k should be a valid kernel.\n"; my_abort(1); } break; case 'm': *mapType = atoi(optarg); if (*mapTypeTOROID) { cerr << "The argument of option -m should be a valid map type.\n"; my_abort(1); } break; case 'r': *radius = atoi(optarg); if (*radius<=0) { cerr << "The argument of option -r should be a positive integer.\n"; my_abort(1); } break; case 's': *snapshots = atoi(optarg); if (*snapshots>2) { cerr << "The argument of option -s should be 0, 1, or 2.\n"; my_abort(1); } break; case 'x': *nSomX = atoi(optarg); if (*nSomX<=0) { cerr << "The argument of option -x should be a positive integer.\n"; my_abort(1); } break; case 'y': *nSomY = atoi(optarg); if (*nSomY<=0) { cerr << "The argument of option -y should be a positive integer.\n"; my_abort(1); } break; case '?': if (optopt == 'e' || optopt == 'k' || optopt == 's' || optopt == 'x' || optopt == 'y') { cerr << "Option -" << optopt << " requires an argument.\n"; printUsage(); my_abort(1); } else if (isprint (optopt)) { cerr << "Unknown option `-" << optopt << "'.\n"; printUsage(); my_abort(1); } else { cerr << "Unknown option character `\\x" << optopt << "'.\n"; printUsage(); my_abort(1); } default: abort (); } } if (argc-optind!=2) { cerr << "Incorrect number of mandatory parameters\n"; printUsage(); my_abort(1); } *inFilename = argv[optind++]; *outPrefix = argv[optind++]; } /// EOF somoclu-1.2/src/training.cpp0000644000175000001440000002057312253742256013077 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include #include #include "somoclu.h" using namespace std; /** Initialize SOM codebook with random values * @param seed - random seed * @param codebook - the codebook to fill in * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ void initializeCodebook(unsigned int seed, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions) { /// /// Fill initial random weights /// srand(seed); for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int d = 0; d < nDimensions; d++) { int w = 0xFFF & rand(); w -= 0x800; codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d] = (float)w / 4096.0f; } } } } /** Main training loop * @param itask - number of work items * @param kv * @param ptr */ void train(int itask, float *data, svm_node **sparseData, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, unsigned int nEpoch, unsigned int radius0, string outPrefix, unsigned int snapshots, unsigned int kernelType, unsigned int mapType, string initialCodebookFilename) { /// /// Codebook /// float *codebook = new float[nSomY*nSomX*nDimensions]; float *numerator; float *denominator; int *globalBmus = NULL; if (itask == 0) { numerator = new float[nSomY*nSomX*nDimensions]; denominator = new float[nSomY*nSomX]; globalBmus = new int[nVectorsPerRank*int(ceil(nVectors/(double)nVectorsPerRank))*2]; if (initialCodebookFilename.empty()){ initializeCodebook(0, codebook, nSomX, nSomY, nDimensions); } else { unsigned int nSomXY = 0; unsigned int tmpNDimensions = 0; delete [] codebook; codebook = readMatrix(initialCodebookFilename, nSomXY, tmpNDimensions); if (tmpNDimensions != nDimensions) { cerr << "Dimension of initial codebook does not match data!\n"; my_abort(5); } else if (nSomXY / nSomY != nSomX) { cerr << "Dimension of initial codebook does not match specified SOM grid!\n"; my_abort(6); } cout << "Read initial codebook: " << initialCodebookFilename << "\n"; } } /// /// Parameters for SOM /// float N = (float)nEpoch; /// iterations if (radius0 == 0) { radius0 = nSomX / 2.0f; /// init radius for updating neighbors } float radius = radius0; unsigned int x = 0; /// 0...N-1 /// /// Training /// #ifdef HAVE_MPI double training_time = MPI_Wtime(); #endif while (nEpoch && radius > 1.0) { #ifdef HAVE_MPI double epoch_time = MPI_Wtime(); #endif if (itask == 0) { radius = radius0 * exp(-10.0f * (x * x) / (N * N)); x++; cout << "Epoch: " << (nEpoch-1) << " Radius: " << radius << endl; } #ifdef HAVE_MPI MPI_Bcast(&radius, 1, MPI_FLOAT, 0, MPI_COMM_WORLD); MPI_Bcast(codebook, nSomY*nSomX*nDimensions, MPI_FLOAT, 0, MPI_COMM_WORLD); #endif if (itask == 0) { for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { denominator[som_y*nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) { numerator[som_y*nSomX*nDimensions + som_x*nDimensions + d] = 0.0; } } } } /// 1. Each task fills localNumerator and localDenominator /// 2. MPI_reduce sums up each tasks localNumerator and localDenominator to the root's /// numerator and denominator. switch (kernelType) { default: case DENSE_CPU: trainOneEpochDenseCPU(itask, data, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, mapType, globalBmus); break; #ifdef CUDA case DENSE_GPU: trainOneEpochDenseGPU(itask, data, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, mapType, globalBmus); break; #endif case SPARSE_CPU: trainOneEpochSparseCPU(itask, sparseData, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, mapType, globalBmus); break; } /// 3. Update codebook using numerator and denominator #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif if (itask == 0) { #pragma omp parallel for for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { float denom = denominator[som_y*nSomX + som_x]; for (unsigned int d = 0; d < nDimensions; d++) { float newWeight = numerator[som_y*nSomX*nDimensions + som_x*nDimensions + d] / denom; if (newWeight > 0.0) { codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d] = newWeight; } } } } } /// if (snapshots > 0 && itask == 0) { cout << "Saving interim U-Matrix..." << endl; stringstream sstm; sstm << outPrefix << "." << x; saveUMat(sstm.str() + string(".umx"), codebook, nSomX, nSomY, nDimensions, mapType); if (snapshots == 2){ saveBmus(sstm.str() + string(".bm"), globalBmus, nSomX, nSomY, nVectors); saveCodebook(sstm.str() + string(".wts"), codebook, nSomX, nSomY, nDimensions); } } nEpoch--; #ifdef HAVE_MPI if (itask == 0) { epoch_time = MPI_Wtime() - epoch_time; cerr << "Epoch Time: " << epoch_time << endl; } #endif } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); training_time = MPI_Wtime() - training_time; if (itask == 0) { cerr << "Total training Time: " << training_time << endl; } #endif /// /// Save SOM map and u-mat /// if (itask == 0) { /// /// Save U-mat /// int ret = saveUMat(outPrefix + string(".umx"), codebook, nSomX, nSomY, nDimensions, mapType); if (ret < 0) cout << " Failed to save u-matrix. !" << endl; else { cout << " Done!" << endl; } saveBmus(outPrefix + string(".bm"), globalBmus, nSomX, nSomY, nVectors); /// /// Save codebook /// saveCodebook(outPrefix + string(".wts"), codebook, nSomX, nSomY, nDimensions); } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif delete [] codebook; if (itask == 0) { delete [] numerator; delete [] denominator; } } somoclu-1.2/src/denseGpuKernels.cu0000644000175000001440000003516112253742256014206 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #undef _GLIBCXX_ATOMIC_BUILTINS #undef _GLIBCXX_USE_INT128 #include #include #include #include #include #include #include #include "somoclu.h" // Error handling macro #define CUDA_CHECK(call) \ if((call) != cudaSuccess) { \ cudaError_t err = cudaGetLastError(); \ cerr << "CUDA error calling \""#call"\", code is " << err << endl; \ my_abort(err); } //Globals cublasHandle_t handle; thrust::device_vector deviceData; thrust::device_vector deviceDataNorms; thrust::device_vector deviceCodebook; thrust::device_vector deviceCodebookNorms; // convert a linear index to a row index template struct linear_index_to_row_index : public thrust::unary_function { T C; // number of columns __host__ __device__ linear_index_to_row_index(T C) : C(C) {} __host__ __device__ T operator()(T i) { return i / C; } }; // note: functor inherits from unary_function template struct square : public thrust::unary_function { __host__ __device__ T operator()(T x) const { return x*x; } }; typedef thrust::tuple argMinType; struct argMin : public thrust::binary_function { __host__ __device__ argMinType operator()(const argMinType& a, const argMinType& b) const { if (thrust::get<1>(a) < thrust::get<1>(b)) { return a; } else { return b; } } }; template thrust::device_vector normsOfRowSpace(thrust::device_vector A, int nRows, int nColumns) { // allocate storage for row sums and indices thrust::device_vector row_sums(nRows); thrust::device_vector row_indices(nRows); // compute row sums by summing values with equal row indices thrust::reduce_by_key (thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)), thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)) + (nRows*nColumns), thrust::make_transform_iterator(A.begin(), square()), row_indices.begin(), row_sums.begin(), thrust::equal_to(), thrust::plus()); return row_sums; } thrust::device_vector minsOfRowSpace(thrust::device_vector A, int nRows, int nColumns) { // allocate storage for row sums and indices thrust::device_vector row_sums(nRows); thrust::device_vector row_indices(nRows); // compute row sums by summing values with equal row indices thrust::reduce_by_key (thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)), thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)) + (nRows*nColumns), thrust::make_zip_iterator(thrust::make_tuple(thrust::counting_iterator(0),A.begin())), row_indices.begin(), row_sums.begin(), thrust::equal_to(), argMin()); return row_sums; } template __global__ void euclidean(float *anorm2, float *bnorm2, float *M, int height, int width) { unsigned int xIndex = blockIdx.x * BLOCK_DIM + threadIdx.x; unsigned int yStartIndex = blockIdx.y * BLOCK_DIM; if (xIndex < width) { float bNormForX = bnorm2[xIndex]; unsigned int yEndIndex=(yStartIndex+BLOCK_DIM < height ? yStartIndex+BLOCK_DIM : height); for (unsigned int yIndex=yStartIndex; yIndex void printMatrix(thrust::device_vector A, int nRows, int nColumns) { for (size_t i = 0; i < nRows; i++) { for (size_t j = 0; j < nColumns; j++) { std::cout << A[i*nColumns+j] << " "; } std::cout << "\n"; } std::cout << "\n"; } /** Clear the device memory and shut down CUBLAS * */ void freeGpu() { deviceData.clear(); deviceDataNorms.clear(); deviceCodebook.clear(); deviceCodebookNorms.clear(); thrust::device_vector().swap(deviceData); thrust::device_vector().swap(deviceDataNorms); thrust::device_vector().swap(deviceCodebook); thrust::device_vector().swap(deviceCodebookNorms); cublasStatus_t status = cublasDestroy(handle); if (status != CUBLAS_STATUS_SUCCESS) { cerr << "!!!! shutdown error (A)\n"; my_abort(-1); } } /** Find the best matching units -- called from the map function * @param bmus - array of best matching units * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance * @param nVectorsPerRank - the number of data points assigned to this GPU */ void getBmusOnGpu(int *bmus, float *codebook, int nSomX, int nSomY, int nDimensions, int nVectorsPerRank) { deviceCodebook = thrust::device_vector(codebook, codebook+nSomX*nSomY*nDimensions); deviceCodebookNorms = normsOfRowSpace(deviceCodebook, nSomX*nSomY, nDimensions); thrust::device_vector deviceGramMatrix(nSomX*nSomY*nVectorsPerRank, 0); //Calculate the inner products of the data vectors and the weight vectors float alpha = 1.0f; float beta = 0.0f; cublasStatus_t status = cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, nSomX*nSomY, nVectorsPerRank, nDimensions, &alpha, thrust::raw_pointer_cast(&deviceCodebook[0]), nDimensions, thrust::raw_pointer_cast(&deviceData[0]), nDimensions, &beta, thrust::raw_pointer_cast(&deviceGramMatrix[0]), nSomX*nSomY); if (status != CUBLAS_STATUS_SUCCESS) { cerr << "!!!! kernel execution error.\n"; my_abort(-1); } //All components of the vectorized Euclidean distance are available // 32 is a magic number, this is the block size that works best on Tesla C2050 int BLOCK_DIM=32; dim3 grid((nSomX*nSomY+BLOCK_DIM-1)/BLOCK_DIM, (nVectorsPerRank+BLOCK_DIM-1)/BLOCK_DIM,1); dim3 threads(BLOCK_DIM,1,1); if (BLOCK_DIM==32) { euclidean<32><<>>(thrust::raw_pointer_cast(&deviceDataNorms[0]), thrust::raw_pointer_cast(&deviceCodebookNorms[0]), thrust::raw_pointer_cast(&deviceGramMatrix[0]), nVectorsPerRank, nSomX*nSomY); } //Finding minimums thrust::host_vector minsOfA=minsOfRowSpace(deviceGramMatrix, nVectorsPerRank, nSomX*nSomY); CUDA_CHECK(cudaDeviceSynchronize()); //Getting back SOM coordinates from minimums for (int i=0; i(tmp) % (nSomX*nSomY); bmus[i*2] = somCoordinate % nSomX; bmus[i*2+1] = somCoordinate / nSomX; } } /** Initialize CUBLAS and device data * @param hostData - the data in the main memory * @param height - number of data points assigned to this GPU * @param width - dimensions of a data instance */ void initializeGpu(float *hostData, int nVectorsPerRank, int nDimensions, int nSomX, int nSomY) { /* Initialize CUBLAS */ cublasStatus_t status = cublasCreate(&handle); if (status != CUBLAS_STATUS_SUCCESS) { cerr << "!!!! CUBLAS initialization error\n"; my_abort(-1); } deviceData = thrust::device_vector(hostData, hostData+nVectorsPerRank*nDimensions); deviceDataNorms = normsOfRowSpace(deviceData, nVectorsPerRank, nDimensions); deviceCodebook = thrust::device_vector(nSomX*nSomY*nDimensions,0); deviceCodebookNorms = thrust::device_vector(nSomX*nSomY,0); } /** Check and initialize a device attached to a node * @param commRank - the MPI rank of this process * @param commSize - the size of MPI comm world */ #ifdef HAVE_MPI /// Note that this function was lifted from http://code.google.com/p/gpmr/ void setDevice(int commRank, int commSize) { FILE * fp = popen("/bin/hostname", "r"); char buf[1024]; if (fgets(buf, 1023, fp) == NULL) strcpy(buf, "localhost"); pclose(fp); string host = buf; host = host.substr(0, host.size() - 1); strcpy(buf, host.c_str()); int devCount; int deviceNum=-1; CUDA_CHECK(cudaGetDeviceCount(&devCount)); if (commRank == 0) { map > hosts; map devCounts; MPI_Status stat; MPI_Request req; hosts[buf].push_back(0); devCounts[buf] = devCount; for (int i = 1; i < commSize; ++i) { MPI_Recv(buf, 1024, MPI_CHAR, i, 0, MPI_COMM_WORLD, &stat); MPI_Recv(&devCount, 1, MPI_INT, i, 0, MPI_COMM_WORLD, &stat); // check to make sure each process on each node reports the same number of devices. hosts[buf].push_back(i); if (devCounts.find(buf) != devCounts.end()) { if (devCounts[buf] != devCount) { printf("Error, device count mismatch %d != %d on %s\n", devCounts[buf], devCount, buf); fflush(stdout); } } else devCounts[buf] = devCount; } // check to make sure that we don't have more jobs on a node than we have GPUs. for (map >::iterator it = hosts.begin(); it != hosts.end(); ++it) { if (it->second.size() > static_cast(devCounts[it->first])) { printf("Error, more jobs running on '%s' than devices - %d jobs > %d devices.\n", it->first.c_str(), static_cast(it->second.size()), devCounts[it->first]); fflush(stdout); MPI_Abort(MPI_COMM_WORLD, 1); } } // send out the device number for each process to use. MPI_Irecv(&deviceNum, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, &req); for (map >::iterator it = hosts.begin(); it != hosts.end(); ++it) { for (unsigned int i = 0; i < it->second.size(); ++i) { int devID = i; MPI_Send(&devID, 1, MPI_INT, it->second[i], 0, MPI_COMM_WORLD); } } MPI_Wait(&req, &stat); } else { // send out the hostname and device count for your local node, then get back the device number you should use. MPI_Status stat; MPI_Send(buf, strlen(buf) + 1, MPI_CHAR, 0, 0, MPI_COMM_WORLD); MPI_Send(&devCount, 1, MPI_INT, 0, 0, MPI_COMM_WORLD); MPI_Recv(&deviceNum, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, &stat); } CUDA_CHECK(cudaSetDevice(deviceNum)); MPI_Barrier(MPI_COMM_WORLD); } #endif /** One epoch on the GPU, dense variant */ void trainOneEpochDenseGPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus) { int bmus[nVectorsPerRank*2]; getBmusOnGpu(bmus, codebook, nSomX, nSomY, nDimensions, nVectorsPerRank); float *localNumerator = new float[nSomY*nSomX*nDimensions]; float *localDenominator = new float[nSomY*nSomX]; #pragma omp parallel default(shared) { #pragma omp for for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { localDenominator[som_y*nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) localNumerator[som_y*nSomX*nDimensions + som_x*nDimensions + d] = 0.0; } } /// Accumulate denoms and numers #pragma omp for for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask*nVectorsPerRank+n. * */ #include using namespace std; #ifndef SOMOCLU_H #define SOMOCLU_H #if HAVE_CONFIG_H #include #endif #ifdef HAVE_MPI #include #endif #define DENSE_CPU 0 #define DENSE_GPU 1 #define SPARSE_CPU 2 #define PLANAR 0 #define TOROID 1 /// The neighbor_fuct value below which we consider /// the impact zero for a given node in the map #define NEIGHBOR_THRESHOLD 0.05 /// Sparse structures and routines struct svm_node { int index; float value; }; float euclideanDistanceOnToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY); float euclideanDistanceOnPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y); int saveCodebook(string cbFileName, float *codebook, unsigned int SOM_X, unsigned int SOM_Y, unsigned int NDIMEN); int saveUMat(string fname, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int mapType); int saveBmus(string filename, int *bmus, unsigned int nSomX, unsigned int nSomY, unsigned int nVectors); void printMatrix(float *A, int nRows, int nCols); float *readMatrix(const string inFilename, unsigned int &nRows, unsigned int &nCols); void readSparseMatrixDimensions(const string filename, unsigned int &nRows, unsigned int &nColumns); svm_node** readSparseMatrixChunk(const string filename, unsigned int nRows, unsigned int nRowsToRead, unsigned int rowOffset); void train(int itask, float *data, svm_node **sparseData, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, unsigned int nEpoch, unsigned int radius0, string outPrefix, unsigned int snapshots, unsigned int kernelType, unsigned int mapType, string initialCodebookFilename); void trainOneEpochDenseCPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus); void trainOneEpochSparseCPU(int itask, svm_node **sparseData, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus); extern "C" { #ifdef CUDA void setDevice(int commRank, int commSize); void freeGpu(); void initializeGpu(float *hostData, int nVectorsPerRank, int nDimensions, int nSomX, int nSomY); void trainOneEpochDenseGPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, unsigned int mapType, int *globalBmus); #endif void my_abort(int err); } #endif somoclu-1.2/src/io.cpp0000644000175000001440000003131512253742256011667 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include #include #include #include "somoclu.h" using namespace std; /** Save a SOM codebook * @param cbFileName - name of the file to save * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ int saveCodebook(string cbFilename, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions) { FILE* file = fopen(cbFilename.c_str(), "wt"); cout << " Saving Codebook " << cbFilename << endl; fprintf(file, "%%%d %d\n", nSomY, nSomX); fprintf(file, "%%%d\n", nDimensions); if (file!=0) { for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int d = 0; d < nDimensions; d++) { fprintf(file, "%0.10f ", codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]); } fprintf(file, "\n"); } } fclose(file); return 0; } else { return 1; } } /** Save best matching units * @param filename - name of the file to save * @param bmus - the best matching units to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nVectors - the number of vectors */ int saveBmus(string filename, int *bmus, unsigned int nSomX, unsigned int nSomY, unsigned int nVectors) { FILE* file = fopen(filename.c_str(), "wt"); cout << " Saving best matching units " << filename << endl; fprintf(file, "%%%d %d\n", nSomY, nSomX); fprintf(file, "%%%d\n", nVectors); if (file!=0) { for (unsigned int i = 0; i < nVectors; ++i) { fprintf(file, "%d %d %d\n", i, bmus[2*i], bmus[2*i+1]); } fclose(file); return 0; } else { return 1; } } /** Euclidean distance between vec1 and vec2 * @param vec1 * @param vec2 * @param nDimensions * @return distance */ float get_distance(const float* vec1, const float* vec2, unsigned int nDimensions) { float distance = 0.0f; float x1 = 0.0f; float x2 = 0.0f; for (unsigned int d = 0; d < nDimensions; d++) { x1 = std::min(vec1[d], vec2[d]); x2 = std::max(vec1[d], vec2[d]); distance += std::abs(x1-x2)*std::abs(x1-x2); } return sqrt(distance); } /** Get weight vector from a codebook using x, y index * @param codebook - the codebook to save * @param som_y - y coordinate of a node in the map * @param som_x - x coordinate of a node in the map * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance * @return the weight vector */ float* get_wvec(float *codebook, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions) { float* wvec = new float[nDimensions]; for (unsigned int d = 0; d < nDimensions; d++) wvec[d] = codebook[som_y*nSomX*nDimensions+som_x*nDimensions+d]; /// CAUTION: (y,x) order return wvec; } /** Save u-matrix * @param fname * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ int saveUMat(string fname, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int mapType) { float min_dist = 1.5f; FILE* fp = fopen(fname.c_str(), "wt"); fprintf(fp, "%%"); fprintf(fp, "%d %d", nSomY, nSomX); fprintf(fp, "\n"); if (fp != 0) { for (unsigned int som_y1 = 0; som_y1 < nSomY; som_y1++) { for (unsigned int som_x1 = 0; som_x1 < nSomX; som_x1++) { float dist = 0.0f; unsigned int nodes_number = 0; for (unsigned int som_y2 = 0; som_y2 < nSomY; som_y2++) { for (unsigned int som_x2 = 0; som_x2 < nSomX; som_x2++) { if (som_x1 == som_x2 && som_y1 == som_y2) continue; float tmp = 0.0f; if (mapType == PLANAR) { tmp = euclideanDistanceOnPlanarMap(som_x1, som_y1, som_x2, som_y2); } else if (mapType == TOROID) { tmp = euclideanDistanceOnToroidMap(som_x1, som_y1, som_x2, som_y2, nSomX, nSomY); } if (tmp <= min_dist) { nodes_number++; float* vec1 = get_wvec(codebook, som_y1, som_x1, nSomX, nSomY, nDimensions); float* vec2 = get_wvec(codebook, som_y2, som_x2, nSomX, nSomY, nDimensions); dist += get_distance(vec1, vec2, nDimensions); delete [] vec1; delete [] vec2; } } } dist /= (float)nodes_number; fprintf(fp, " %f", dist); } fprintf(fp, "\n"); } fclose(fp); return 0; } else return -2; } void getMatrixDimensions(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); float *data=NULL; if (file.is_open()) { string line; float tmp; while(getline(file,line)) { if (line.substr(0,1) == "#") { continue; } std::istringstream iss(line); if (nRows == 0) { while (iss >> tmp) { nColumns++; } } nRows++; } file.close(); } else { std::cerr << "Input file could not be opened!\n"; my_abort(-1); } } unsigned int *readLrnHeader(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); string line; unsigned int currentColumn = 0; unsigned int nAllColumns = 0; unsigned int *columnMap = NULL; while(getline(file,line)) { if (line.substr(0,1) == "#") { continue; } if (line.substr(0,1) == "%") { std::istringstream iss(line.substr(1,line.length())); if (nRows == 0) { iss >> nRows; } else if (nAllColumns == 0) { iss >> nAllColumns; } else if (columnMap == NULL) { columnMap = new unsigned int[nAllColumns]; unsigned int itmp = 0; currentColumn = 0; while (iss >> itmp) { columnMap[currentColumn++] = itmp; if (itmp == 1) { ++nColumns; } } } else { break; } } } file.close(); return columnMap; } unsigned int *readWtsHeader(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); string line; unsigned int currentColumn = 0; while(getline(file,line)) { if (line.substr(0,1) == "#") { continue; } if (line.substr(0,1) == "%") { std::istringstream iss(line.substr(1,line.length())); if (nRows == 0) { iss >> nRows; unsigned int nSomY = 0; iss >> nSomY; nRows = nRows*nSomY; } else if (nColumns == 0) { iss >> nColumns; } else { break; } } } file.close(); unsigned int *columnMap = new unsigned int[nColumns]; for (unsigned int i = 0; i < nColumns; ++i) { columnMap[i] = 1; } return columnMap; } /** Reads a matrix * @param inFilename * @param nRows - returns the number of rows * @param nColumns - returns the number of columns * @return the matrix */ float *readMatrix(string inFilename, unsigned int &nRows, unsigned int &nColumns) { float *data = NULL; unsigned int *columnMap = NULL; if (inFilename.compare(inFilename.size()-3, 3, "lrn") == 0) { columnMap = readLrnHeader(inFilename, nRows, nColumns); } else if (inFilename.compare(inFilename.size()-3, 3, "wts") == 0) { columnMap = readWtsHeader(inFilename, nRows, nColumns); } else { getMatrixDimensions(inFilename, nRows, nColumns); columnMap = new unsigned int[nColumns]; for (unsigned int i = 0; i < nColumns; ++i) { columnMap[i] = 1; } } ifstream file; file.open(inFilename.c_str()); string line; float tmp; unsigned int j = 0; unsigned int currentColumn = 0; while(getline(file,line)) { if (line.substr(0,1) == "#" | line.substr(0,1) == "%") { continue; } if (data == NULL) { data = new float[nRows*nColumns]; } std::istringstream iss(line); currentColumn = 0; while (iss >> tmp) { if (columnMap[currentColumn++] != 1) { continue; } data[j++] = tmp; } } file.close(); delete [] columnMap; return data; } void readSparseMatrixDimensions(string filename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(filename.c_str()); if (file.is_open()) { string line; int max_index=-1; while(getline(file,line)) { if (line.substr(0,1) == "#") { continue; } stringstream linestream(line); string value; int dummy_index; while(getline(linestream,value,' ')) { int separator=value.find(":"); istringstream myStream(value.substr(0,separator)); myStream >> dummy_index; if(dummy_index > max_index) { max_index = dummy_index; } } ++nRows; } nColumns=max_index+1; file.close(); } else { std::cerr << "Input file could not be opened!\n"; my_abort(-1); } } svm_node** readSparseMatrixChunk(string filename, unsigned int nRows, unsigned int nRowsToRead, unsigned int rowOffset) { ifstream file; file.open(filename.c_str()); string line; for (unsigned int i=0; i= nRows) { nRowsToRead = nRows-rowOffset; } svm_node **x_matrix = new svm_node *[nRowsToRead]; for(unsigned int i=0; i> x_matrix[i][j].index; istringstream myStream2(value.substr(separator+1)); myStream2 >> x_matrix[i][j].value; j++; } x_matrix[i][j].index = -1; } file.close(); return x_matrix; } /** Shut down MPI cleanly if something goes wrong * @param err - error code to print */ void my_abort(int err) { cerr << "Aborted\n"; #ifdef HAVE_MPI MPI_Abort(MPI_COMM_WORLD, err); #else exit(err); #endif } somoclu-1.2/install-sh0000755000175000001440000003246412253742256011777 00000000000000#!/bin/sh # install - install a program, script, or datafile scriptversion=2006-12-25.00 # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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[-T] SRCFILE DSTFILE or: $0 [OPTION]... SRCFILES... DIRECTORY or: $0 [OPTION]... -t DIRECTORY SRCFILES... or: $0 [OPTION]... -d DIRECTORIES... In the 1st form, copy SRCFILE to DSTFILE. In the 2nd and 3rd, copy all SRCFILES to DIRECTORY. In the 4th, create DIRECTORIES. Options: --help display this help and exit. --version display version info and exit. -c (ignored) -C install only if different (preserve the last data modification time) -d create directories instead of installing files. -g GROUP $chgrpprog installed files to GROUP. -m MODE $chmodprog installed files to MODE. -o USER $chownprog installed files to USER. -s $stripprog installed files. -t DIRECTORY install into DIRECTORY. -T report an error if DSTFILE is a directory. Environment variables override the default commands: CHGRPPROG CHMODPROG CHOWNPROG CMPPROG CPPROG MKDIRPROG MVPROG RMPROG STRIPPROG " while test $# -ne 0; do case $1 in -c) ;; -C) copy_on_change=true;; -d) dir_arg=true;; -g) chgrpcmd="$chgrpprog $2" shift;; --help) echo "$usage"; exit $?;; -m) mode=$2 case $mode in *' '* | *' '* | *' '* | *'*'* | *'?'* | *'['*) echo "$0: invalid mode: $mode" >&2 exit 1;; esac shift;; -o) chowncmd="$chownprog $2" shift;; -s) stripcmd=$stripprog;; -t) dst_arg=$2 shift;; -T) no_target_directory=true;; --version) echo "$0 $scriptversion"; exit $?;; --) shift break;; -*) echo "$0: invalid option: $1" >&2 exit 1;; *) break;; esac shift done if test $# -ne 0 && test -z "$dir_arg$dst_arg"; then # When -d is used, all remaining arguments are directories to create. # When -t is used, the destination is already specified. # Otherwise, the last argument is the destination. 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