On Wed, Aug 17, 2016 at 2:50 AM, Karl Rupp <r...@iue.tuwien.ac.at> wrote:
> Hi Andy and Dmitriy, > > We could (and probably should?) add such a convenience header file at the > expense of increased compilation times (and reduced encapsulation of source > code against compiler issues). +1 on single header! :) Ultimately, this all boils down to fighting limitations of the current > header-only source code distribution model. > FWIW, if our opinion matters, actually, header-only is one of the things we like very much. It means we don't have to redistribute any executables, everything already is included in our jars, everything that we use and need (and only it) is already generated for us by javacpp. This is one of the most valuable features about ViennaCL in my opinion. It is very hard to get customers to install yet-another libX.so on their clusters. But header-only, template-based code solves (1) we include everything we need in jar (no extra infra requirement) (2) we include only that we actually support/use (lightweight, slim application size requirement) these are very valuable for flink/spark type of applications. Which is what we are. I know that you have plans to generate a .so lib with apparently non-object API, but for apache mahout the OAA api with header-only requirement is super optimal. (at least I have a high hope you won't _force_ us to redistribute an .so(s) in the future releases :) ) -Dmitriy > Best regards, > Karli > > > > >> >> On Tue, Aug 16, 2016 at 11:16 AM, Dmitriy Lyubimov <dlie...@gmail.com >> <mailto:dlie...@gmail.com>> wrote: >> >> Karl, >> >> i can independently confirm the problem with prod_impl instantiation >> over expression of compressed times base_matrix into matrix type. >> >> I understand there are tests examples but something goes wrong with >> the straightforward code. >> >> We are compiling for open cl and open mp at the same time. >> >> >> On Mon, Aug 8, 2016 at 11:03 AM, Andrew Palumbo <ap....@outlook.com >> <mailto:ap....@outlook.com>> wrote: >> >> Hi Karli, >> >> >> I've mocked up in C++ the method that I'm trying to use from >> java. Aside from adding some values, it looks very similar to >> the code that you have below. >> >> >> I'm getting the same compiler error hat I was getting through >> javacpp/JNI: >> >> >> >> sparseDenseMmul.cpp:85:103: required from here >> /usr/include/viennacl/matrix.hpp:2247:36: error: no matching >> function for call to >> ‘prod_impl(const viennacl::compressed_matrix<double>&, const >> viennacl::matrix_base<double, long unsigned int, long int>&, >> viennacl::matrix_base<double, long unsigned int, long int>&)’ >> viennacl::linalg::prod_impl(proxy.lhs(), proxy.rhs(), >> lhs); >> >> ^ >> In file included from /usr/include/viennacl/matrix.hpp:28:0, >> from >> /usr/include/viennacl/linalg/sparse_matrix_operations.hpp:28, >> from >> /usr/include/viennacl/compressed_matrix.hpp:31, >> from sparseDenseMmul.cpp:7: >> /usr/include/viennacl/linalg/matrix_operations.hpp:438:10: >> note: candidate: >> template<class NumericT> void viennacl::linalg::prod_impl(co >> nst >> viennacl::matrix_base<T>&, const viennacl::vector_base<T>&, >> viennacl::vector_base<T>&) >> void prod_impl(const matrix_base<NumericT> & mat, >> >> >> The code is below, and I've attached both the >> "sparseDenseMmul.cpp" file and the full compilation error output >> (very long, probably not useful) >> >> >> Thanks very much, >> >> >> Andy >> >> >> >> >> >> Attached as "sparseDenseMmul.cpp": >> >> >> #include <iostream> >> // not using openMP for this mockup >> // #define VIENNACL_WITH_OPENMP 1 >> // ViennaCL includes >> #include "viennacl/forwards.h" >> #include "viennacl/compressed_matrix.hpp" >> #include "viennacl/linalg/prod.hpp" >> #include "viennacl/backend/memory.hpp" >> #include "viennacl/matrix.hpp" >> #include "viennacl/detail/matrix_def.hpp" >> #include "viennacl/tools/random.hpp" >> #include "viennacl/context.hpp" >> #include "viennacl/linalg/host_based/sp >> arse_matrix_operations.hpp" >> >> >> // C_dense_matrix = A_compressed_matrix %*% B_dense_matrix. >> >> // compile line w/o OpenMP: g++ sparseDenseMmul.cpp >> -I/usr/include/viennacl/ -o sparseDenseMmul >> >> >> >> int main() >> { >> // trying to recreate javacpp wrapper functionalliy as closely >> as possible >> // so not using typedef, unsigned ints, etc, and defining >> templates as doubles >> // creating buffers as int/double arrays and then setting >> pointers to them. >> // (not 100% sure that this is how javacpp passes pointers but >> should be close.) >> >> >> //typedef double ScalarType; >> >> // in acuallity, we cast `int`s from jni/javacpp. >> unsigned int m = 10; >> unsigned int n = 10; >> unsigned long s = 5; >> >> unsigned int NNz_A = 12; >> >> >> // allocate buffers and set pointers (similarly to javacpp) >> // using ints (not unsigned ints) here from jni/javacpp. >> int A_row_jumpers[m + 1] = {0, 0, 1, 2, 4, 5, 6, 7, 9, 11, 12}; >> int *A_row_ptr = A_row_jumpers; >> >> // using ints (not unsigned ints) here from jni/javacpp. >> int A_col_idxs[NNz_A] = {4, 0, 2, 3, 2, 4, 0, 4, 3, 0, 3, 0}; >> int *A_col_ptr = A_col_idxs; >> >> double A_values[NNz_A] = {0.4065367203992265, >> 0.04957158909682802, 0.3708618354358446, >> 0.5205586068847993, 0.6963900565931678, >> 0.8330915529787706, 0.32839112750638844, >> 0.4265801782090245, 0.7856168903297948, >> 0.14733066454561583, 0.9501663495824946, >> 0.9710498974366047}; >> double* A_values_ptr = A_values; >> >> >> // using double values in Mahout setting template directlyfor >> our compressed_matrix, A >> viennacl::compressed_matrix<double> A_compressed_matrix(m, s); >> >> // set the ptrs for A >> A_compressed_matrix.set(A_row_ptr, A_col_ptr, A_values_ptr, m, >> s, NNz_A); >> >> // B is dense s so we only need s x n values. >> double B_values[s * n] = {0}; >> >> // add some random data to B: >> viennacl::tools::uniform_random_numbers<double> randomNumber; >> for (int i = 0; i< s * n; i++) { >> B_values[i] = randomNumber(); >> } >> >> double* B_values_ptr = B_values; >> >> >> // for our row_major dense_matrix, B can set the double values >> in the construcor >> // this is currently the constructor that we're using through >> scala/javacpp. >> const viennacl::matrix<double,viennacl::row_major> >> B_dense_matrix(B_values_ptr, >> viennacl::MAIN_MEMORY, s, n); >> >> >> // perform multiplication and inside of a compressed_matrix >> constructor >> viennacl::matrix<double> >> C_dense_matrix(viennacl::linalg::prod(A_compressed_matrix , >> B_dense_matrix)); >> >> >> // print out matrix >> std::cout << "ViennaCL: " << C_dense_matrix << std::endl; >> >> >> // just exit with success for now if there are no runtime >> errors. >> >> return EXIT_SUCCESS; >> } >> >> >> ------------------------------------------------------------ >> ------------ >> *From:* Karl Rupp <r...@iue.tuwien.ac.at >> <mailto:r...@iue.tuwien.ac.at>> >> *Sent:* Sunday, August 7, 2016 2:20:26 PM >> *To:* Andrew Palumbo; viennacl-devel@lists.sourceforge.net >> <mailto:viennacl-devel@lists.sourceforge.net> >> *Subject:* Re: [ViennaCL-devel] compressed_matrix %*% matrix_Base >> >> >> Hi Andy, >> >> the relevant tests for sparse matrices times dense matrices are in >> tests/spmdm.cpp. In particular, I recreated a test case based on >> your >> description and couldn't find any issues: >> >> viennacl::compressed_matrix<NumericT> compressed_A; >> viennacl::matrix<NumericT, FactorLayoutT> B1(std_A.size(), >> cols_rhs); >> viennacl::matrix_base<NumericT> B1_ref(B1); >> viennacl::matrix_base<NumericT> >> C2(viennacl::linalg::prod(compressed_A, B1_ref)); >> >> compiles cleanly. Could you please provide a code snippet >> demonstrating >> the problem you are encountering? >> >> Thanks and best regards, >> Karli >> >> >> >> On 08/05/2016 09:04 PM, Andrew Palumbo wrote: >> > Hi Karl, >> > >> > >> > I've been trying to implement tests for: >> > >> > >> > matrix_base<double> C = compressed_matrix<double> A %*% >> > >> > matrix_base<double,row_major> B. >> > >> > >> > I cant find in the code or the documentation any constructor for >> > matrix_base<T>( >> > >> > matrix_expression<const viennacl::compressed_matrix<T>, const >> > viennacl::matrix_base<T>, viennacl::op_prod>) >> > >> > ie. a mixed expression of compressed_matrix and matrix_base >> > >> > and get a compilation error when I try to instantiate a: >> > >> > matrix_base<double>(matrix_expression<const >> > viennacl::compressed_matrix<double>, const >> > viennacl::matrix_base<double>, >> > viennacl::op_prod>) >> > >> > Is there a transformation that I need to do from this >> > >> > matrix_expression<compressed_matrix<double>, >> matrix_base<double>, >> > op_prod> >> > >> > to something else so that I may be able to initialize a >> matrix_base (or >> > possibly even a compressed_matrix) from it? >> > >> > The compilation error that i get is below. >> > >> > Thanks, >> > >> > Andy >> > >> >> >> ------------------------------------------------------------ >> ------------------ >> What NetFlow Analyzer can do for you? Monitors network bandwidth >> and traffic >> patterns at an interface-level. Reveals which users, apps, and >> protocols are >> consuming the most bandwidth. Provides multi-vendor support for >> NetFlow, >> J-Flow, sFlow and other flows. Make informed decisions using >> capacity >> planning reports. http://sdm.link/zohodev2dev >> _______________________________________________ >> ViennaCL-devel mailing list >> ViennaCL-devel@lists.sourceforge.net >> <mailto:ViennaCL-devel@lists.sourceforge.net> >> https://lists.sourceforge.net/lists/listinfo/viennacl-devel >> <https://lists.sourceforge.net/lists/listinfo/viennacl-devel> >> >> >> >> >
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