added AVX for linear basis function

.gitignore

@@ -1,9 +1,3 @@
-NN.kdev4
-.kdev4
-*.o
-*.a
-*.so
-*.nm
 /doc/html/*
 !/doc/html/doxy-boot.js
 !/doc/html/jquery.powertip.min.js

CMakeLists.txt

@@ -2,22 +2,27 @@ cmake_minimum_required(VERSION 3.2)
 project(NeuralNetwork CXX)
 
 OPTION(BUILD_SHARED_LIBS "Build also shared library." ON)
+OPTION(USE_AVX "IF avx should be used." ON)
 OPTION(USE_SSE "IF sse should be used." ON)
 OPTION(USE_SSE2 "IF only sse2 should be used." OFF)
+
 OPTION(ENABLE_TESTS "enables tests" ON)
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -pedantic -Weffc++ -Wshadow -Wstrict-aliasing -ansi -Woverloaded-virtual -Wdelete-non-virtual-dtor -Wno-unused-function")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --std=c++14")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g")
 #set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread")
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native -mtune=native -O3")
 
-if(USE_SSE)
+if(USE_AVX)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_AVX")
+elseif(USE_SSE)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse4.2 -DUSE_SSE")
     if(USE_SSE2)
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_SSE2")
     endif(USE_SSE2)
-endif(USE_SSE)
+endif(USE_AVX)
 
 include_directories(./include/)
 

include/NeuralNetwork/BasisFunction/Linear.h

@@ -5,6 +5,7 @@
 #include <emmintrin.h>
 #include <xmmintrin.h>
 #include <pmmintrin.h>
+#include <immintrin.h>
 
 #include "./StreamingBasisFunction.h"
 
@@ -18,8 +19,37 @@ namespace BasisFunction {
 			Linear() {}
 
 			inline virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const override {
+#ifdef USE_AVX
+				//TODO: check sizes!!!
+				std::size_t inputSize=input.size();
+				size_t alignedPrev=inputSize-inputSize%8;
 
-#ifdef USE_SSE
+				const float* weightsData=weights.data();
+				const float* inputData=input.data();
+
+				union {
+					__m256 avx;
+					float f[8];
+				} partialSolution;
+
+				partialSolution.avx=_mm256_setzero_ps();
+
+				for(size_t k=0;k<alignedPrev;k+=8) {
+					//TODO: asignement!! -- possible speedup
+					partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_loadu_ps(weightsData+k),_mm256_loadu_ps(inputData+k)));
+				}
+
+				for(size_t k=alignedPrev;k<inputSize;k++) {
+					partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_set_ps(weightsData[k],0,0,0,0,0,0,0),_mm256_set_ps(inputData[k],0,0,0,0,0,0,0)));
+				}
+
+				partialSolution.avx = _mm256_add_ps(partialSolution.avx,  _mm256_permute2f128_ps(partialSolution.avx , partialSolution.avx , 1));
+				partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
+				partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
+
+				return partialSolution.f[0];
+#else
+	#ifdef USE_SSE
 				size_t inputSize=input.size();
 				size_t alignedPrev=inputSize-inputSize%4;
 
@@ -45,7 +75,7 @@ namespace BasisFunction {
 				partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
 		#endif
 				return partialSolution.f[0];
-#else
+	#else
 
 				register float tmp = 0;
 				size_t inputSize=input.size();
@@ -53,6 +83,7 @@ namespace BasisFunction {
 					tmp+=input[k]*weights[k];
 				}
 				return tmp;
+	#endif
 #endif
 			}