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Commit 63079513 authored by hhakim's avatar hhakim
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Add faust_prox_gpu.h/hpp (GPU2 light impl. -- using CPU prox) and include them... 

Add faust_prox_gpu.h/hpp (GPU2 light impl. -- using CPU prox) and include them in Constraint* files.
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src/algorithm/constraint/faust_ConstraintFPP.h
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......@@ -52,6 +52,10 @@
#include "faust_MatDense.h"
#include "faust_prox.h"
#endif
#if USE_GPU_MOD
#include "faust_prox_gpu.h"
#include "faust_MatDense_gpu.h"
#endif
......
src/algorithm/constraint/faust_ConstraintInt.h
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......@@ -53,6 +53,10 @@
#include "faust_MatDense.h"
#include "faust_prox.h"
#endif
#if USE_GPU_MOD
#include "faust_prox_gpu.h"
#include "faust_MatDense_gpu.h"
#endif
template<typename FPP,FDevice DEVICE> class MatDense;
......
src/algorithm/constraint/faust_ConstraintMat.h
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......@@ -51,6 +51,11 @@
#include "faust_MatDense.h"
#include "faust_prox.h"
#endif
#if USE_GPU_MOD
#include "faust_prox_gpu.h"
#include "faust_MatDense_gpu.h"
#endif
//! \class Faust::ConstraintMat
//! \brief Contains the matrix dense constraint parameters for the hierarchical factorization. <br>
//! See the parent class Faust::ConstraintGeneric for more detail. <br>
......
src/faust_linear_operator/GPU2/faust_prox_gpu.h 0 → 100644
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/****************************************************************************/
/* Description: */
/* For more information on the FAuST Project, please visit the website */
/* of the project : <http://faust.inria.fr> */
/* */
/* License: */
/* Copyright (2019): Hakim Hadj-Djilani, Nicolas Bellot, Adrien Leman, Thomas Gautrais, */
/* Luc Le Magoarou, Remi Gribonval */
/* INRIA Rennes, FRANCE */
/* http://www.inria.fr/ */
/* */
/* The FAuST Toolbox is distributed under the terms of the GNU Affero */
/* General Public License. */
/* This program is free software: you can redistribute it and/or modify */
/* it under the terms of the GNU Affero General Public License as */
/* published by the Free Software Foundation. */
/* */
/* 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 Affero General Public License for more details. */
/* */
/* You should have received a copy of the GNU Affero General Public */
/* License along with this program. */
/* If not, see <http://www.gnu.org/licenses/>. */
/* */
/* Contacts: */
/* Hakim H. hakim.hadj-djilani@inria.fr */
/* Nicolas Bellot : nicolas.bellot@inria.fr */
/* Adrien Leman : adrien.leman@inria.fr */
/* Thomas Gautrais : thomas.gautrais@inria.fr */
/* Luc Le Magoarou : luc.le-magoarou@inria.fr */
/* Remi Gribonval : remi.gribonval@inria.fr */
/* */
/* References: */
/* [1] Le Magoarou L. and Gribonval R., "Flexible multi-layer sparse */
/* approximations of matrices and applications", Journal of Selected */
/* Topics in Signal Processing, 2016. */
/* <https://hal.archives-ouvertes.fr/hal-01167948v1> */
/****************************************************************************/
#ifndef __PROX_GPU_H__
#define __PROX_GPU_H__
#include <vector>
#include <iostream>
#include <algorithm>
#include "faust_prox.h"
#include "faust_MatDense_gpu.h"
#include "faust_constant.h"
#include "faust_exception.h"
namespace Faust
{
template<typename FPP>
void prox_sp(MatDense<FPP,GPU2> & M,faust_unsigned_int k, const bool normalized=true, const bool pos=false);
template<typename FPP>
void prox_sp_pos(MatDense<FPP, GPU2> & M,faust_unsigned_int k, const bool normalized=true, const bool pos=false );
template<typename FPP>
void prox_spcol(MatDense<FPP,GPU2> & M,faust_unsigned_int k, const bool normalized=true, const bool pos=false);
template<typename FPP>
void prox_splin(MatDense<FPP,GPU2> & M,faust_unsigned_int k, const bool normalized=true, const bool pos=false);
template<typename FPP>
void prox_splincol(MatDense<FPP,GPU2> &M,faust_unsigned_int k, const bool normalized=true, const bool pos=false);
template<typename FPP>
void prox_supp(MatDense<FPP,GPU2> & M, const MatDense<FPP,GPU2> & supp, const bool normalized=true, const bool pos=false);
template<typename FPP>
void prox_const(MatDense<FPP,GPU2> & M, const MatDense<FPP,GPU2> & supp, const bool normalized=true, const bool pos=false);
template<typename FPP, typename FPP2>
void prox_normcol(MatDense<FPP,GPU2> & M,FPP2 s, const bool normalized=false, const bool pos=false);
template<typename FPP, typename FPP2>
void prox_normlin(MatDense<FPP,GPU2> & M,FPP2 s, const bool normalized=false, const bool pos=false);
// template<typename FPP>
// void prox_blkdiag(MatDense<FPP,GPU2> & M,faust_unsigned_int k);
template<typename FPP>
void prox_blockdiag(MatDense<FPP,GPU2> & M, std::vector<faust_unsigned_int>& m_vec, std::vector<faust_unsigned_int>& n_vec, const bool normalized = false, const bool pos = false);
template<typename FPP>
void prox_blockdiag(MatDense<FPP,GPU2> & M, MatDense<FPP,GPU2> mn_vec, const bool normalized=true, const bool pos=false);
template<typename FPP> void pre_prox_pos(MatDense<FPP,GPU2> & M);
template<typename FPP> void prox_hankel(MatDense<FPP, GPU2> & M, const bool normalized = true, const bool pos = false);
template<typename FPP> void prox_toeplitz(MatDense<FPP, GPU2> & M, const bool normalized = true, const bool pos = false);
template<typename FPP> void prox_circ(MatDense<FPP, GPU2> & M, const bool normalized = true, const bool pos = false);
template<typename FPP> void prox_skperm(MatDense<FPP, GPU2> & M,const unsigned int k, const bool normalized=true, const bool pos=false);
}
#include "faust_prox_gpu.hpp"
#endif
src/faust_linear_operator/GPU2/faust_prox_gpu.hpp 0 → 100644
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/****************************************************************************/
/* Description: */
/* For more information on the FAuST Project, please visit the website */
/* of the project : <http://faust.inria.fr> */
/* */
/* License: */
/* Copyright (2019): Hakim Hadj-Djilani, Nicolas Bellot, Adrien Leman, Thomas Gautrais, */
/* Luc Le Magoarou, Remi Gribonval */
/* INRIA Rennes, FRANCE */
/* http://www.inria.fr/ */
/* */
/* The FAuST Toolbox is distributed under the terms of the GNU Affero */
/* General Public License. */
/* This program is free software: you can redistribute it and/or modify */
/* it under the terms of the GNU Affero General Public License as */
/* published by the Free Software Foundation. */
/* */
/* 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 Affero General Public License for more details. */
/* */
/* You should have received a copy of the GNU Affero General Public */
/* License along with this program. */
/* If not, see <http://www.gnu.org/licenses/>. */
/* */
/* Contacts: */
/* Hakim H. hakim.hadj-djilani@inria.fr */
/* Nicolas Bellot : nicolas.bellot@inria.fr */
/* Adrien Leman : adrien.leman@inria.fr */
/* Thomas Gautrais : thomas.gautrais@inria.fr */
/* Luc Le Magoarou : luc.le-magoarou@inria.fr */
/* Remi Gribonval : remi.gribonval@inria.fr */
/* */
/* References: */
/* [1] Le Magoarou L. and Gribonval R., "Flexible multi-layer sparse */
/* approximations of matrices and applications", Journal of Selected */
/* Topics in Signal Processing, 2016. */
/* <https://hal.archives-ouvertes.fr/hal-01167948v1> */
/****************************************************************************/
#ifndef __FAUST_PROX_CU_HPP__
#define __FAUST_PROX_CU_HPP__
// const char * interface_prox_name="prox : ";
namespace Faust
{
template<typename FPP>
void prox_sp(MatDense<FPP,GPU2> & M, faust_unsigned_int k, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_sp(cpuM, k, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_sp_pos(MatDense<FPP, GPU2> & M, faust_unsigned_int k, const bool normalized/*=true*/, const bool pos/*=false */)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_sp_pos(cpuM, k, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_spcol(MatDense<FPP,GPU2> & M, faust_unsigned_int k, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_spcol(cpuM, k, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_splin(MatDense<FPP,GPU2> & M,faust_unsigned_int k, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_splin(cpuM, k, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_splincol(MatDense<FPP,GPU2> &M,faust_unsigned_int k, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_splincol(cpuM, k, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_supp(MatDense<FPP,GPU2> & M, const MatDense<FPP,GPU2> & supp, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
MatDense<FPP,Cpu> cpu_supp = supp.tocpu();
prox_supp(cpuM, cpu_supp, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_const(MatDense<FPP,GPU2> & M, const MatDense<FPP,GPU2> & supp, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
MatDense<FPP,Cpu> cpu_supp = supp.tocpu();
prox_const(cpuM, cpu_supp, normalized, pos);
M = cpuM;
}
template<typename FPP, typename FPP2>
void prox_normcol(MatDense<FPP,GPU2> & M,FPP2 s, const bool normalized/*=false*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_normcol(cpuM, s, normalized, pos);
M = cpuM;
}
template<typename FPP, typename FPP2>
void prox_normlin(MatDense<FPP,GPU2> & M,FPP2 s, const bool normalized/*=false*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_normlin(cpuM, s, normalized, pos);
M = cpuM;
}
// template<typename FPP>
// void prox_blkdiag(MatDense<FPP,GPU2> & M,faust_unsigned_int k)
// {
// MatDense<FPP,Cpu> cpuM = M.tocpu();
// prox_gpu(cpuM, k);
// M = cpuM;
// }
template<typename FPP>
void prox_blockdiag(MatDense<FPP,GPU2> & M, std::vector<faust_unsigned_int>& m_vec, std::vector<faust_unsigned_int>& n_vec, const bool normalized /*= false*/, const bool pos /*= false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_blockdiag(cpuM, m_vec, n_vec, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_blockdiag(MatDense<FPP,GPU2> & M, MatDense<FPP,GPU2> mn_vec, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
MatDense<FPP,Cpu> cpu_mn_vec = mn_vec.tocpu();
prox_blockdiag(cpuM, cpu_mn_vec, normalized, pos);
M = cpuM;
}
template<typename FPP> void pre_prox_pos(MatDense<FPP,GPU2> & M)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
pre_prox_pos(cpuM);
M = cpuM;
}
template<typename FPP> void prox_hankel(MatDense<FPP, GPU2> & M, const bool normalized /*= true*/, const bool pos /*= false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_hankel(cpuM, normalized, pos);
M = cpuM;
}
template<typename FPP> void prox_toeplitz(MatDense<FPP, GPU2> & M, const bool normalized /*= true*/, const bool pos /*= false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_toeplitz(cpuM, normalized, pos);
M = cpuM;
}
template<typename FPP> void prox_circ(MatDense<FPP, GPU2> & M, const bool normalized /*= true*/, const bool pos /*= false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_circ(cpuM, normalized, pos);
M = cpuM;
}
template<typename FPP>
void prox_skperm(MatDense<FPP, GPU2> & M,const unsigned int k, const bool normalized/*=true*/, const bool pos/*=false*/)
{
MatDense<FPP,Cpu> cpuM = M.tocpu();
prox_skperm(cpuM, k, normalized, pos);
M = cpuM;
}
}
#endif
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