Pushing everything again, accidentally deleted my remote repository

This commit is contained in:
jakob.schratter 2025-12-09 22:06:13 +01:00
commit 1bee3e8e5b
101 changed files with 9428 additions and 0 deletions

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# Basic Defintions for using GNU-compiler suite sequentially
# requires setting of COMPILER=CLANG_
#CLANGPATH=//usr/lib/llvm-10/bin/
CC = ${CLANGPATH}clang
CXX = ${CLANGPATH}clang++
#CXX = ${CLANGPATH}clang++ -lomptarget -fopenmp-targets=nvptx64-nvidia-cuda --cuda-path=/opt/pgi/linux86-64/2017/cuda/8.0
#F77 = gfortran
LINKER = ${CXX}
#http://clang.llvm.org/docs/UsersManual.html#options-to-control-error-and-warning-messages
WARNINGS += -Weverything -Wno-c++98-compat -Wno-sign-conversion -Wno-date-time -Wno-shorten-64-to-32 -Wno-padded -ferror-limit=1
WARNINGS += -Wdocumentation -Wconversion -Wshadow -Wfloat-conversion -pedantic
#-fsyntax-only -Wdocumentation -Wconversion -Wshadow -Wfloat-conversion -pedantic
CXXFLAGS += -O3 -std=c++17 -ferror-limit=1 ${WARNINGS}
# don't use -Ofast
# -ftrapv
LINKFLAGS += -O3
# different libraries in Ubuntu or manajaró
ifndef UBUNTU
UBUNTU=1
endif
# BLAS, LAPACK
LINKFLAGS += -llapack -lblas
# -lopenblas
ifeq ($(UBUNTU),1)
# ubuntu
else
# on archlinux
LINKFLAGS += -lcblas
endif
# interprocedural optimization
CXXFLAGS += -flto
LINKFLAGS += -flto
# very good check
# http://clang.llvm.org/extra/clang-tidy/
# good check, see: http://llvm.org/docs/CodingStandards.html#include-style
SWITCH_OFF=,-readability-magic-numbers,-readability-redundant-control-flow,-readability-redundant-member-init
SWITCH_OFF+=,-readability-redundant-member-init,-readability-isolate-declaration
#READABILITY=,readability*${SWITCH_OFF}
#TIDYFLAGS = -checks=llvm-*,-llvm-header-guard -header-filter=.* -enable-check-profile -extra-arg="-std=c++17" -extra-arg="-fopenmp"
TIDYFLAGS = -checks=llvm-*,-llvm-header-guard${READABILITY} -header-filter=.* -enable-check-profile -extra-arg="-std=c++17" -extra-arg="-fopenmp"
#TIDYFLAGS += -checks='modernize*
# ???
#TIDYFLAGS = -checks='cert*' -header-filter=.*
# MPI checks ??
#TIDYFLAGS = -checks='mpi*'
# ??
#TIDYFLAGS = -checks='performance*' -header-filter=.*
#TIDYFLAGS = -checks='portability-*' -header-filter=.*
#TIDYFLAGS = -checks='readability-*' -header-filter=.*
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
@rm -f ${PROGRAM} ${OBJECTS}
clean_all:: clean
@rm -f *_ *~ *.bak *.log *.out *.tar
codecheck: tidy_check
tidy_check:
clang-tidy ${SOURCES} ${TIDYFLAGS} -- ${SOURCES}
# see also http://clang-developers.42468.n3.nabble.com/Error-while-trying-to-load-a-compilation-database-td4049722.html
run: clean ${PROGRAM}
# time ./${PROGRAM} ${PARAMS}
./${PROGRAM} ${PARAMS}
# tar the current directory
MY_DIR = `basename ${PWD}`
tar: clean_all
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
doc:
doxygen Doxyfile
#########################################################################
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# some tools
# Cache behaviour (CXXFLAGS += -g tracks down to source lines; no -pg in linkflags)
cache: ${PROGRAM}
valgrind --tool=callgrind --simulate-cache=yes ./$^ ${PARAMS}
# kcachegrind callgrind.out.<pid> &
kcachegrind `ls -1tr callgrind.out.* |tail -1`
# Check for wrong memory accesses, memory leaks, ...
# use smaller data sets
mem: ${PROGRAM}
valgrind -v --leak-check=yes --tool=memcheck --undef-value-errors=yes --track-origins=yes --log-file=$^.addr.out --show-reachable=yes ./$^ ${PARAMS}
# Simple run time profiling of your code
# CXXFLAGS += -g -pg
# LINKFLAGS += -pg
prof: ${PROGRAM}
perf record ./$^ ${PARAMS}
perf report
# gprof -b ./$^ > gp.out
# kprof -f gp.out -p gprof &
codecheck: tidy_check

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# Basic Defintions for using GNU-compiler suite sequentially
# requires setting of COMPILER=GCC_
CC = gcc
CXX = g++
F77 = gfortran
LINKER = ${CXX}
# on mephisto:
#CXXFLAGS += -I/share/apps/atlas/include
#LINKFLAGS += -L/share/apps/atlas/lib
#LINKFLAGS += -lcblas -latlas
#LINKFLAGS += -lblas
# Der <cblas.h> Header muss mit extern "C" versehen werden, damit g++ alles findet.
#WARNINGS = -pedantic -pedantic-errors -Wall -Wextra -Werror -Wconversion -Weffc++ -Woverloaded-virtual -Wfloat-equal -Wshadow
WARNINGS = -pedantic -Wall -Wextra -Wconversion -Weffc++ -Woverloaded-virtual -Wfloat-equal -Wshadow \
-Wredundant-decls -Winline -fmax-errors=1
# -Wunreachable-code
# -Wunreachable-code
CXXFLAGS += -ffast-math -O3 -march=native -std=c++17 ${WARNINGS}
#CXXFLAGS += -Ofast -funroll-all-loops -std=c++17 ${WARNINGS}
#-msse3
# -ftree-vectorizer-verbose=2 -DNDEBUG
# -ftree-vectorizer-verbose=5
# -ftree-vectorize -fdump-tree-vect-blocks=foo.dump -fdump-tree-pre=stderr
# CFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp -fdump-tree-vect-details
# CFLAGS = -ffast-math -O3 -funroll-loops -DNDEBUG -msse3 -fopenmp -ftree-vectorizer-verbose=2
# #CFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
# FFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
# LFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
LINKFLAGS += -O3
# BLAS, LAPACK
OPENBLAS_DIR = /opt/openblas_GCCseq
#OPENBLAS_DIR = /opt/openblas_GCC
OPENBLAS_LIBDIR = ${OPENBLAS_DIR}/lib
OPENBLAS_INCDIR = ${OPENBLAS_DIR}/include
CXXFLAGS += -I${OPENBLAS_INCDIR}
LINKFLAGS += -L${OPENBLAS_LIBDIR} -lopenblas
# interprocedural optimization
CXXFLAGS += -flto
LINKFLAGS += -flto
# profiling tools
#CXXFLAGS += -pg
#LINKFLAGS += -pg
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
@rm -f ${PROGRAM} ${OBJECTS}
clean_all:: clean
-@rm -f *_ *~ *.bak *.log *.out *.tar *.orig
-@rm -r html
run: clean ${PROGRAM}
# time ./${PROGRAM}
# ./${PROGRAM}
( export LD_LIBRARY_PATH=${OPENBLAS_LIBDIR}:${LD_LIBRARY_PATH} ; ./${PROGRAM} )
# or 'export LD_LIBRARY_PATH=/opt/openblas_gcc/lib:${LD_LIBRARY_PATH}' in your ~/.bashrc
# tar the current directory
MY_DIR = `basename ${PWD}`
tar:
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
doc:
doxygen Doxyfile
#########################################################################
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# some tools
# Cache behaviour (CXXFLAGS += -g tracks down to source lines; no -pg in linkflags)
cache: ${PROGRAM}
valgrind --tool=callgrind --simulate-cache=yes ./$^
# kcachegrind callgrind.out.<pid> &
kcachegrind `ls -1tr callgrind.out.* |tail -1`
# Check for wrong memory accesses, memory leaks, ...
# use smaller data sets
# no "-pg" in compile/link options
mem: ${PROGRAM}
valgrind -v --leak-check=yes --tool=memcheck --undef-value-errors=yes --track-origins=yes --log-file=$^.addr.out --show-reachable=yes ./$^
# Simple run time profiling of your code
# CXXFLAGS += -g -pg
# LINKFLAGS += -pg
prof: ${PROGRAM}
./$^
gprof -b ./$^ > gp.out
# kprof -f gp.out -p gprof &
#Trace your heap:
#> heaptrack ./main.GCC_
#> heaptrack_gui heaptrack.main.GCC_.<pid>.gz
heap: ${PROGRAM}
heaptrack ./$^ 11
heaptrack_gui `ls -1tr heaptrack.$^.* |tail -1` &
########################################################################
# get the detailed status of all optimization flags
info:
echo "detailed status of all optimization flags"
$(CXX) --version
$(CXX) -Q $(CXXFLAGS) --help=optimizers

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# Basic Defintions for using GNU-compiler suite sequentially
# requires setting of COMPILER=GCC_
CC = gcc
CXX = g++
F77 = gfortran
LINKER = ${CXX}
WARNINGS = -Wall -pedantic -Wextra -Weffc++ -Woverloaded-virtual -Wfloat-equal -Wshadow \
-Wredundant-decls -Winline -fmax-errors=1
# -Wunreachable-code
CXXFLAGS += -ffast-math -O3 -march=native -std=c++17 ${WARNINGS}
#CXXFLAGS += -Ofast -funroll-all-loops -std=c++17 ${WARNINGS}
#-msse3
# -ftree-vectorizer-verbose=2 -DNDEBUG
# -ftree-vectorizer-verbose=5
# -ftree-vectorize -fdump-tree-vect-blocks=foo.dump -fdump-tree-pre=stderr
# CFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp -fdump-tree-vect-details
# CFLAGS = -ffast-math -O3 -funroll-loops -DNDEBUG -msse3 -fopenmp -ftree-vectorizer-verbose=2
# #CFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
# FFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
# LFLAGS = -ffast-math -O3 -DNDEBUG -msse3 -fopenmp
LINKFLAGS += -O3
#architecture
#CPU = -march=znver2
CXXFLAGS += ${CPU}
LINKFLAGS += ${CPU}
# different libraries in Ubuntu or manajaró
ifndef UBUNTU
UBUNTU=1
endif
# BLAS, LAPACK
ifeq ($(UBUNTU),1)
LINKFLAGS += -llapack -lblas
# -lopenblas
else
# on archlinux
LINKFLAGS += -llapack -lopenblas -lcblas
endif
# interprocedural optimization
CXXFLAGS += -flto
LINKFLAGS += -flto
# for debugging purpose (save code)
# -fsanitize=leak # only one out the three can be used
# -fsanitize=address
# -fsanitize=thread
SANITARY = -fsanitize=address -fsanitize=undefined -fsanitize=null -fsanitize=return \
-fsanitize=bounds -fsanitize=alignment -fsanitize=float-divide-by-zero -fsanitize=float-cast-overflow \
-fsanitize=bool -fsanitize=enum -fsanitize=vptr
#CXXFLAGS += ${SANITARY}
#LINKFLAGS += ${SANITARY}
# profiling tools
#CXXFLAGS += -pg
#LINKFLAGS += -pg
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
@rm -f ${PROGRAM} ${OBJECTS}
clean_all:: clean
-@rm -f *_ *~ *.bak *.log *.out *.tar *.orig *.optrpt
-@rm -rf html
run: clean ${PROGRAM}
#run: ${PROGRAM}
# time ./${PROGRAM} ${PARAMS}
./${PROGRAM} ${PARAMS}
# tar the current directory
MY_DIR = `basename ${PWD}`
tar: clean_all
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
#find . -size +10M > large_files
#--exclude-from ${MY_DIR}/large_files
zip: clean
@echo "Zip the directory: " ${MY_DIR}
@cd .. ;\
zip -r ${MY_DIR}.zip ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
doc:
doxygen Doxyfile
#########################################################################
.SUFFIXES: .f90
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
# $(CXX) -c $(CXXFLAGS) -o $@ $< 2>&1 | tee -a $<.log
# $(CXX) -c $(CXXFLAGS) -o $@ $< 2>&1 | tee -a $(<:.cpp=.log)
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
.f90.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# some tools
# Cache behaviour (CXXFLAGS += -g tracks down to source lines; no -pg in linkflags)
cache: ${PROGRAM}
valgrind --tool=callgrind --simulate-cache=yes ./$^ ${PARAMS}
# kcachegrind callgrind.out.<pid> &
kcachegrind `ls -1tr callgrind.out.* |tail -1`
# Check for wrong memory accesses, memory leaks, ...
# use smaller data sets
# no "-pg" in compile/link options
mem: ${PROGRAM}
valgrind -v --leak-check=yes --tool=memcheck --undef-value-errors=yes --track-origins=yes --log-file=$^.addr.out --show-reachable=yes ./$^ ${PARAMS}
# Graphical interface
# valkyrie
# Simple run time profiling of your code
# CXXFLAGS += -g -pg
# LINKFLAGS += -pg
prof: ${PROGRAM}
perf record ./$^ ${PARAMS}
perf report
# gprof -b ./$^ > gp.out
# kprof -f gp.out -p gprof &
# perf in Ubuntu 20.04: https://www.howtoforge.com/how-to-install-perf-performance-analysis-tool-on-ubuntu-20-04/
# * install
# * sudo vi /etc/sysctl.conf
# add kernel.perf_event_paranoid = 0
#Trace your heap:
#> heaptrack ./main.GCC_
#> heaptrack_gui heaptrack.main.GCC_.<pid>.gz
heap: ${PROGRAM}
heaptrack ./$^ ${PARAMS}
heaptrack_gui `ls -1tr heaptrack.$^.* |tail -1` &
codecheck: $(SOURCES)
cppcheck --enable=all --inconclusive --std=c++17 --suppress=missingIncludeSystem $^
########################################################################
# get the detailed status of all optimization flags
info:
echo "detailed status of all optimization flags"
$(CXX) --version
$(CXX) -Q $(CXXFLAGS) --help=optimizers
lscpu
inxi -C
lstopo
# Excellent hardware info
# hardinfo
# Life monitoring of CPU frequency etc.
# sudo i7z
# Memory consumption
# vmstat -at -SM 3
# xfce4-taskmanager
# https://www.tecmint.com/check-linux-cpu-information/
#https://www.tecmint.com/monitor-cpu-and-gpu-temperature-in-ubuntu/
# Debugging:
# https://wiki.archlinux.org/index.php/Debugging

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# Basic Defintions for using INTEL compiler suite sequentially
# requires setting of COMPILER=ICC_
#BINDIR = /opt/intel/bin/
# special on my sony [GH]
#BINDIR = /opt/save.intel/bin/
# very special on my sony [GH]
# FIND_LIBS = -L /opt/save.intel/composer_xe_2013.1.117/mkl/lib/intel64/libmkl_intel_lp64.so
# Error with g++-4.8 using icpc14.0,x:
# find directory wherein bits/c++config.h is located
# 'locate bits/c++config.h'
#FOUND_CONFIG = -I/usr/include/x86_64-linux-gnu/c++/4.8
CC = ${BINDIR}icc
CXX = ${BINDIR}icpc
F77 = ${BINDIR}ifort
LINKER = ${CXX}
WARNINGS = -Wall -Weffc++ -Woverloaded-virtual -Wfloat-equal -Wshadow -wd2015,2012 -wn3
# -Winline -Wredundant-decls -Wunreachable-code
CXXFLAGS += -O3 -fargument-noalias -std=c++17 -DNDEBUG ${WARNINGS} -mkl ${FOUND_CONFIG}
# profiling tools
#CXXFLAGS += -pg
#LINKFLAGS += -pg
# -vec-report=3
# -qopt-report=5 -qopt-report-phase=vec
# -guide -parallel
# -guide-opts=string -guide-par[=n] -guide-vec[=n]
# -auto-p32 -simd
CXXFLAGS += -align
# use MKL by INTEL
# https://software.intel.com/content/www/us/en/develop/documentation/mkl-linux-developer-guide/top/linking-your-application-with-the-intel-math-kernel-library/linking-quick-start/using-the-mkl-compiler-option.html
# https://software.intel.com/content/www/us/en/develop/articles/intel-mkl-link-line-advisor.html
# LINKFLAGS += -L${BINDIR}../composer_xe_2013.1.117/mkl/lib/intel64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lpthread
#LINKFLAGS += -O3 -L/opt/intel/mkl/lib -mkl
LINKFLAGS += -O3 -mkl=sequential
# interprocedural optimization
CXXFLAGS += -ipo
LINKFLAGS += -ipo
# annotated assembler file
ANNOTED = -fsource-asm -S
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
rm -f ${PROGRAM} ${OBJECTS}
clean_all:: clean
@rm -f *_ *~ *.bak *.log *.out *.tar
run: clean ${PROGRAM}
./${PROGRAM}
# tar the current directory
MY_DIR = `basename ${PWD}`
tar: clean_all
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
doc:
doxygen Doxyfile
#########################################################################
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# # some tools
# # Cache behaviour (CXXFLAGS += -g tracks down to source lines)
# cache: ${PROGRAM}
# valgrind --tool=callgrind --simulate-cache=yes ./$^
# # kcachegrind callgrind.out.<pid> &
#
# # Check for wrong memory accesses, memory leaks, ...
# # use smaller data sets
# mem: ${PROGRAM}
# valgrind -v --leak-check=yes --tool=memcheck --undef-value-errors=yes --track-origins=yes --log-file=$^.addr.out --show-reachable=yes ./$^
#
# # Simple run time profiling of your code
# # CXXFLAGS += -g -pg
# # LINKFLAGS += -pg
# prof: ${PROGRAM}
# ./$^
# gprof -b ./$^ > gp.out
# # kprof -f gp.out -p gprof &
#
mem: inspector
prof: amplifier
cache: amplifier
gap_par_report:
${CXX} -c -guide -parallel $(SOURCES) 2> gap.txt
# GUI for performance report
amplifier: ${PROGRAM}
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
# alternatively to the solution above:
#edit file /etc/sysctl.d/10-ptrace.conf and set variable kernel.yama.ptrace_scope variable to 0 .
amplxe-gui &
# GUI for Memory and Thread analyzer (race condition)
inspector: ${PROGRAM}
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
inspxe-gui &
advisor:
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
advixe-gui &
icc-info:
icpc -# main.cpp

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# Basic Defintions for using INTEL compiler suite sequentially
# requires setting of COMPILER=ONEAPI_
# https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onemkl/link-line-advisor.html
# requires
# source /opt/intel/oneapi/setvars.sh
# on AMD: export MKL_DEBUG_CPU_TYPE=5
#BINDIR = /opt/intel/oneapi/compiler/latest/linux/bin/
#MKL_ROOT = /opt/intel/oneapi/mkl/latest/
#export KMP_AFFINITY=verbose,compact
CC = ${BINDIR}icc
CXX = ${BINDIR}dpcpp
F77 = ${BINDIR}ifort
LINKER = ${CXX}
## Compiler flags
WARNINGS = -Wall -Weffc++ -Woverloaded-virtual -Wfloat-equal -Wshadow -pedantic
WARNINGS += -Wpessimizing-move -Wredundant-move
#-wd2015,2012,2014 -wn3
# -Winline -Wredundant-decls -Wunreachable-code
# -qopt-subscript-in-range
# -vec-threshold0
CXXFLAGS += -O3 -std=c++17 ${WARNINGS}
#CXXFLAGS += -DMKL_ILP64 -I"${MKLROOT}/include"
#CXXFLAGS += -DMKL_ILP32 -I"${MKLROOT}/include"
LINKFLAGS += -O3
# interprocedural optimization
CXXFLAGS += -ipo
LINKFLAGS += -ipo
LINKFLAGS += -flto
# annotated Assembler file
ANNOTED = -fsource-asm -S
#architecture
CPU = -march=core-avx2
#CPU += -mtp=zen
# -xCORE-AVX2
# -axcode COMMON-AVX512 -axcode MIC-AVX512 -axcode CORE-AVX512 -axcode CORE-AVX2
CXXFLAGS += ${CPU}
LINKFLAGS += ${CPU}
# use MKL by INTEL
# https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onemkl/link-line-advisor.html
# sequential MKL
# use the 32 bit interface (LP64) instead of 64 bit interface (ILP64)
CXXFLAGS += -qmkl=sequential -UMKL_ILP64
LINKFLAGS += -O3 -qmkl=sequential -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lpthread
#LINKFLAGS += -O3 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lpthread
# shared libs: https://aur.archlinux.org/packages/intel-oneapi-compiler-static
# install intel-oneapi-compiler-static
# or
LINKFLAGS += -shared-intel
OPENMP = -qopenmp
CXXFLAGS += ${OPENMP}
LINKFLAGS += ${OPENMP}
# profiling tools
#CXXFLAGS += -pg
#LINKFLAGS += -pg
# -vec-report=3
# -qopt-report=5 -qopt-report-phase=vec -qopt-report-phase=openmp
# -guide -parallel
# -guide-opts=string -guide-par[=n] -guide-vec[=n]
# -auto-p32 -simd
# Reports: https://software.intel.com/en-us/articles/getting-the-most-out-of-your-intel-compiler-with-the-new-optimization-reports
#CXXFLAGS += -qopt-report=5 -qopt-report-phase=vec,par
#CXXFLAGS += -qopt-report=5 -qopt-report-phase=cg
# Redirect report from *.optrpt to stderr
# -qopt-report-file=stderr
# Guided paralellization
# -guide -parallel
# -guide-opts=string -guide-par[=n] -guide-vec[=n]
# -auto-p32 -simd
## run time checks
# https://www.intel.com/content/www/us/en/develop/documentation/fortran-compiler-oneapi-dev-guide-and-reference/top/compiler-reference/compiler-options/offload-openmp-and-parallel-processing-options/par-runtime-control-qpar-runtime-control.html
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
rm -f ${PROGRAM} ${OBJECTS} *.optrpt
clean_all:: clean
@rm -f *_ *~ *.bak *.log *.out *.tar
run: clean ${PROGRAM}
./${PROGRAM}
# tar the current directory
MY_DIR = `basename ${PWD}`
tar: clean_all
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
doc:
doxygen Doxyfile
#########################################################################
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# some tools
# Cache behaviour (CXXFLAGS += -g tracks down to source lines)
# https://software.intel.com/content/www/us/en/develop/documentation/vtune-help/top/analyze-performance/microarchitecture-analysis-group/memory-access-analysis.html
mem: inspector
prof: vtune
cache: inspector
gap_par_report:
${CXX} -c -guide -parallel $(SOURCES) 2> gap.txt
# GUI for performance report
amplifier: ${PROGRAM}
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
echo 0 | sudo tee /proc/sys/kernel/perf_event_paranoid
amplxe-gui &
# GUI for Memory and Thread analyzer (race condition)
inspector: ${PROGRAM}
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
# inspxe-gui &
vtune-gui ./${PROGRAM} &
advisor:
source /opt/intel/oneapi/advisor/2021.2.0/advixe-vars.sh
# /opt/intel/oneapi/advisor/latest/bin64/advixe-gui &
advisor --collect=survey ./${PROGRAM}
# advisor --collect=roofline ./${PROGRAM}
advisor --report=survey --project-dir=./ src:r=./ --format=csv --report-output=./out/survey.csv
vtune:
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope
# https://software.intel.com/en-us/articles/intel-advisor-2017-update-1-what-s-new
export ADVIXE_EXPERIMENTAL=roofline
vtune -collect hotspots ./${PROGRAM}
vtune -report hotspots -r r000hs > vtune.out
# vtune-gui ./${PROGRAM} &
icc-info:
icpc -# main.cpp
# MKL on AMD
# https://www.computerbase.de/2019-11/mkl-workaround-erhoeht-leistung-auf-amd-ryzen/
#
# https://sites.google.com/a/uci.edu/mingru-yang/programming/mkl-has-bad-performance-on-an-amd-cpu
# export MKL_DEBUG_CPU_TYPE=5
# export MKL_NUM_THRAEDS=1
# export MKL_DYNAMIC=false
# on Intel compiler
# http://publicclu2.blogspot.com/2013/05/intel-complier-suite-reference-card.html

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ex1/PGI_default.mk Normal file
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# Basic Defintions for using PGI-compiler suite sequentially
# requires setting of COMPILER=PGI_
# OPTIRUN = optirun
CC = pgcc
CXX = pgc++
F77 = pgfortran
LINKER = ${CXX}
# on mephisto:
#CXXFLAGS += -I/share/apps/atlas/include
#LINKFLAGS += -L/share/apps/atlas/lib
#LINKFLAGS += -lcblas -latlas
#LINKFLAGS += -lblas
# Der <cblas.h> Header muss mit extern "C" versehen werden, damit g++ alles findet.
WARNINGS = -Minform=warn
# -Wall -pedantic -Wextra -Weffc++ -Woverloaded-virtual -W -Wfloat-equal -Wshadow -Wredundant-decls
# -pedantic -Wunreachable-code -Wextra -Winline
# -Wunreachable-code
#PGI_PROFILING = -Minfo=ccff,loop,vect,opt,intensity,mp,accel
PGI_PROFILING = -Minfo=ccff,accel,ipa,loop,lre,mp,opt,par,unified,vect,intensity
# -Minfo
# -Mprof=time
# -Mprof=lines
# take care with option -Msafeptr
CXXFLAGS += -O3 -std=c++17 ${WARNINGS}
#CXXFLAGS += -O3 -std=c++11 -DNDEBUG ${PGI_PROFILING} ${WARNINGS}
# -fastsse -fargument-noalias ${WARNINGS} -msse3 -vec-report=3
default: ${PROGRAM}
${PROGRAM}: ${OBJECTS}
$(LINKER) $^ ${LINKFLAGS} -o $@
clean:
@rm -f ${PROGRAM} ${OBJECTS}
clean_all:: clean
@rm -f *_ *~ *.bak *.log *.out *.tar
run: clean ${PROGRAM}
./${PROGRAM}
# tar the current directory
MY_DIR = `basename ${PWD}`
tar: clean_all
@echo "Tar the directory: " ${MY_DIR}
@cd .. ;\
tar cf ${MY_DIR}.tar ${MY_DIR} *default.mk ;\
cd ${MY_DIR}
# tar cf `basename ${PWD}`.tar *
doc:
doxygen Doxyfile
#########################################################################
.cpp.o:
$(CXX) -c $(CXXFLAGS) -o $@ $<
.c.o:
$(CC) -c $(CFLAGS) -o $@ $<
.f.o:
$(F77) -c $(FFLAGS) -o $@ $<
##################################################################################################
# # some tools
# # Simple run time profiling of your code
# # CXXFLAGS += -g -pg
# # LINKFLAGS += -pg
# Profiling options PGI, see: pgcollect -help
# CPU_PROF = -allcache
CPU_PROF = -time
# GPU_PROF = -cuda=gmem,branch,cc13 -cudainit
#GPU_PROF = -cuda=branch:cc20
#
PROF_FILE = pgprof.out
cache: prof
prof: ${PROGRAM}
${OPTIRUN} ${BINDIR}pgcollect $(CPU_PROF) ./$^
${OPTIRUN} ${BINDIR}pgprof -exe ./$^ $(PROF_FILE) &
info:
pgaccelinfo -v

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ex1/ex1A_mean_values/.vscode/tasks.json vendored Normal file
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{
"tasks": [
{
"type": "cppbuild",
"label": "C/C++: gcc build active file",
"command": "/usr/bin/gcc",
"args": [
"-fdiagnostics-color=always",
"-g",
"${file}",
"-o",
"${fileDirname}/${fileBasenameNoExtension}"
],
"options": {
"cwd": "${fileDirname}"
},
"problemMatcher": [
"$gcc"
],
"group": {
"kind": "build",
"isDefault": true
},
"detail": "Task generated by Debugger."
}
],
"version": "2.0.0"
}

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#
# use GNU-Compiler tools
COMPILER=GCC_
# alternatively from the shell
# export COMPILER=GCC_
# or, alternatively from the shell
# make COMPILER=GCC_
# use Intel compilers
#COMPILER=ICC_
# use PGI compilers
# COMPILER=PGI_
SOURCES = main.cpp ../ex1A_mean_values/means.cpp
OBJECTS = $(SOURCES:.cpp=.o)
PROGRAM = main.${COMPILER}
# uncomment the next to lines for debugging and detailed performance analysis
CXXFLAGS += -g
LINKFLAGS += -g
# do not use -pg with PGI compilers
ifndef COMPILER
COMPILER=GCC_
endif
include ../${COMPILER}default.mk

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ex1/ex1A_mean_values/main.GCC_ Executable file

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#include "means.h"
#include <vector>
#include <iostream>
using namespace std;
int main(int argc, char **argv)
{
double arithmetic_mean, geometric_mean, harmonic_mean;
// Fixed version
calculate_means(1, 4, 16, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
calculate_means(2, 3, 5, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
calculate_means(1000, 4000, 16000, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
cout << "--------------------------------" << endl;
// Scalable version
calculate_means(vector<int> {1, 4, 16}, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
calculate_means(vector<int> {2, 3, 5}, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
calculate_means(vector<int> {1000, 4000, 16000}, arithmetic_mean, geometric_mean, harmonic_mean);
cout << arithmetic_mean << ", " << geometric_mean << ", " << harmonic_mean << endl;
return 0;
}

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#include "../ex1A_mean_values/means.h"
#include <cmath>
#include <vector>
void calculate_means(int a, int b, int c, double &am, double &gm, double &hm)
{
am = (a + b + c)/3.0;
gm = exp((log(a)+log(b)+log(c))/3);
hm = 3.0/(1.0/a + 1.0/b + 1.0/c);
}
void calculate_means(std::vector<int> numbers, double &am, double &gm, double &hm)
{
int n = numbers.size();
am = 0.;
gm = 0.;
hm = 0.;
for (int i = 0; i < n; ++i)
{
am += numbers[i];
gm += log(numbers[i]);
hm += 1.0/numbers[i];
}
am /= n;
gm = exp(gm/n);
hm = n/hm;
}

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#pragma once
#include <vector>
/**
This function calculates arithmetic mean, geometric mean and harmonic mean of three integers.
@param[in] a first integer
@param[in] b second integer
@param[in] c third integer
@param[out] am arithmetic mean
@param[out] gm geometric mean
@param[out] hm harmonic mean
*/
void calculate_means(int a, int b, int c, double &am, double &gm, double &hm);
/**
This function calculates arithmetic mean, geometric mean and harmonic mean of an integer vector.
@param[in] numbers vector containing integers
@param[out] am arithmetic mean
@param[out] gm geometric mean
@param[out] hm harmonic mean
*/
void calculate_means(std::vector<int> numbers, double &am, double &gm, double &hm);

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#
# use GNU-Compiler tools
COMPILER=GCC_
# alternatively from the shell
# export COMPILER=GCC_
# or, alternatively from the shell
# make COMPILER=GCC_
# use Intel compilers
#COMPILER=ICC_
# use PGI compilers
# COMPILER=PGI_
SOURCES = main.cpp ../ex1A_mean_values/means.cpp
OBJECTS = $(SOURCES:.cpp=.o)
PROGRAM = main.${COMPILER}
# uncomment the next to lines for debugging and detailed performance analysis
CXXFLAGS += -g
LINKFLAGS += -g
# do not use -pg with PGI compilers
ifndef COMPILER
COMPILER=GCC_
endif
include ../${COMPILER}default.mk

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#include "../ex1A_mean_values/means.h"
#include <iostream>
#include <fstream>
#include <cmath>
#include <vector>
#include <algorithm>
using namespace std;
int main(int argc, char **argv)
{
// read vector from file
vector<int> data_vector = {};
ifstream input_stream("data_1.txt");
int line;
while(input_stream >> line)
{
data_vector.push_back(line);
}
data_vector.shrink_to_fit();
// calculate minimum and maximum
vector<int>::iterator min_it = min_element(data_vector.begin(), data_vector.end());
vector<int>::iterator max_it = max_element(data_vector.begin(), data_vector.end());
// calculate arithmetic mean, geometric mean and harmonic mean
double am, gm, hm;
calculate_means(data_vector, am, gm, hm);
// calculate standard deviation
double sd = 0.;
int n = data_vector.size();
for (int i = 0; i < n; ++i)
{
sd += pow(data_vector[i] - am, 2);
}
sd = sqrt(sd/n);
// print results
cout << "minimum: " << *min_it << endl;
cout << "maximum: " << *max_it << endl;
cout << "arithmetic mean: " << am << endl;
cout << "geometric mean: " << gm << endl;
cout << "harmonic mean: " << hm << endl;
cout << "standard deviation: " << sd << endl;
return 0;
}

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#include "special_sum.h"
#include "../utils/timing.h"
#include <iostream>
#include <chrono>
#include <stack>
using namespace std;
int main(int argc, char **argv)
{
// check results and compare speeds
for(size_t n : {15, 1001, 1432987})
{
cout << "n = " << n << endl;
size_t sum_1, sum_2;
tic();
for(size_t i = 0; i < 1000; ++i)
sum_1 = special_sum_loop(n);
double time_1 = toc();
tic();
for(size_t i = 0; i < 1000; ++i)
sum_2 = special_sum_noloop(n);
double time_2 = toc();
cout << "loop: " << sum_1 << "\t\tDuration: " << time_1 << endl;
cout << "no loop: " << sum_2 << "\t\tDuration: " << time_2 << endl << "---------------------------------------------------" << endl;
}
return 0;
}

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#include "special_sum.h"
size_t gauss_sum(size_t n)
{
return (n*(n+1))/2;
}
size_t special_sum_loop(size_t n)
{
size_t sum = 0;
for (size_t i = 1; i < n+1; ++i)
{
if (i % 3 == 0 || i % 5 == 0)
{
sum += i;
}
}
return sum;
}
size_t special_sum_noloop(size_t n)
{
size_t factor_3 = gauss_sum(n/3); // dividing int by int automatically gets rounded off
size_t factor_5 = gauss_sum(n/5);
size_t factor_15 = gauss_sum(n/15);
return factor_3*3 + factor_5*5 - factor_15*15;
}

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#include <cstddef>
/**
This function returns the sum of all positive integers less or equal n which are a multiples of 3 or of 5, WITH using a loop.
@param[in] n
@param[out] M
*/
size_t special_sum_loop(size_t n);
/**
This function returns the sum of all positive integers less or equal n which are a multiples of 3 or of 5, WITHOUT using a loop.
Example: For n=15, we have 60 = 3+5+6+9+10+12+15 = (1+2+3+4+5)*3 + (1+2+3)*5 - 1*15
Formula: M = (\sum_{i=1}^{k_3} i)*3 + (\sum_{i=1}^{k_5} i)*5 - (\sum_{i=1}^{k_15} i)*15
@param[in] n
@param[out] M
*/
size_t special_sum_noloop(size_t n);

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ex1/ex1D_kahan_summation/main Executable file

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#include "mylib.h"
#include <cmath>
#include <iostream>
using namespace std;
int main(int argc, char **argv)
{
for(size_t i = 1; i < 8; ++i)
{
size_t n = pow(10,i);
vector<double> x(n);
for (size_t k = 0; k < n; ++k)
x[k] = 1.0/(k + 1);
// compute scalar products
double sum_1 = scalar(x, x);
double sum_2 = Kahan_skalar(x, x);
// compute error
double err_1 = abs(sum_1 - pow(M_PI,2)/6);
double err_2 = abs(sum_2 - pow(M_PI,2)/6);
cout << "n = " << n << endl;
cout << "Normal scalar product: " << sum_1 << "\terror: " << err_1 << endl;
cout << "Kahan scalar product: " << sum_2 << "\terror: " << err_2 << endl;
cout << endl;
}
return 0;
}

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#include "mylib.h"
#include <cassert> // assert()
#include <cmath>
#include <vector>
using namespace std;
double scalar(vector<double> const &x, vector<double> const &y)
{
assert(x.size() == y.size());
size_t const N = x.size();
double sum = 0.0;
for (size_t i = 0; i < N; ++i)
{
sum += x[i] * y[i];
}
return sum;
}
double Kahan_skalar(vector<double> const &x, vector<double> const &y)
{
double sum = 0;
double c = 0;
size_t n = x.size();
for (size_t i = 0; i < n; ++i)
{
double z = x[i]*y[i] - c; // c is the part that got lost in the last iteration
double t = sum + z; // when adding sum + z, the lower digits are lost if sum is large
c = (t - sum) - z; // now we recover the lower digits to add in the next iteration
sum = t;
}
return sum;
}

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#ifndef FILE_MYLIB
#define FILE_MYLIB
#include <vector>
/** Inner product
@param[in] x vector
@param[in] y vector
@return resulting Euclidian inner product <x,y>
*/
double scalar(std::vector<double> const &x, std::vector<double> const &y);
/** Inner product using BLAS routines
@param[in] x vector
@param[in] y vector
@return resulting Euclidian inner product <x,y>
*/
double scalar_cblas(std::vector<double> const &x, std::vector<double> const &y);
float scalar_cblas(std::vector<float> const &x, std::vector<float> const &y);
/** L_2 Norm of a vector
@param[in] x vector
@return resulting Euclidian norm <x,y>
*/
double norm(std::vector<double> const &x);
double Kahan_skalar(std::vector<double> const &x, std::vector<double> const &y);
#endif

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ex1/ex1E_vector_vs_list/main Executable file

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#include "../utils/timing.h"
#include <iostream>
#include <random>
#include <chrono>
#include <vector>
#include <list>
#include <algorithm>
using namespace std;
size_t random_integer(int lower_bound, int upper_bound)
{
unsigned seed = chrono::system_clock::now().time_since_epoch().count();
minstd_rand0 generator (seed);
return lower_bound + generator() % (upper_bound - lower_bound + 1);
}
int main(int argc, char **argv)
{
// start with generating a sorted vector/list
size_t n = 10000;
vector<int> x_vec(n);
list<int> x_list(n);
for(size_t k = 0; k < n; ++k)
{
x_vec[k] = k + 1;
x_list.push_back(k + 1);
}
// insert new random entries such that the container stays sorted
tic();
for(size_t i = 0; i < n; ++i)
{
size_t new_entry = random_integer(1,n);
auto it = lower_bound(x_vec.begin(), x_vec.end(), new_entry);
x_vec.insert(it, new_entry);
}
double time_1 = toc();
tic();
for(size_t i = 0; i < n; ++i)
{
size_t new_entry = random_integer(1,n);
auto it = lower_bound(x_list.begin(), x_list.end(), new_entry);
x_list.insert(it, new_entry);
}
double time_2 = toc();
// check results
cout << "New vector is sorted: " << std::boolalpha << is_sorted(x_vec.cbegin(), x_vec.cend()) << "\tsize: " << x_vec.size() << "\tduration: " << time_1 << endl;
cout << "New list is sorted: " << std::boolalpha << is_sorted(x_list.cbegin(), x_list.cend()) << "\tsize: " << x_list.size() << "\tduration: " << time_2 << endl;
// Vector stores 3 pointers
// List stores two pointers for every element: one to the previous, one to the next element
return 0;
}

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#include "goldbach.h"
size_t single_goldbach(size_t k)
{
const std::vector<size_t> relevant_primes = get_primes(k);
size_t m = relevant_primes.size();
size_t counter = 0;
for(size_t i = 0; i < m; ++i)
{
for(size_t j = i; j < m; ++j)
{
if(relevant_primes[i] + relevant_primes[j] == k)
++counter;
}
}
return counter;
}
std::vector<size_t> count_goldbach(size_t n)
{
const std::vector<size_t> relevant_primes = get_primes(n);
size_t m = relevant_primes.size();
std::vector<size_t> counter_vector(n + 1, 0);
for(size_t i = 0; i < m; ++i)
{
for(size_t j = i; j < m; ++j)
{
size_t sum = relevant_primes[i] + relevant_primes[j];
if(sum <= n)
++counter_vector[relevant_primes[i] + relevant_primes[j]];
}
}
return counter_vector;
}

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#pragma once
#include "mayer_primes.h"
#include <iostream>
#include <vector>
#include <iterator>
#include <cassert>
/**
This function returns the number of possible decompositions of an integer into a sum of two prime numbers.
@param[in] k first integer
@param[out] count number of decompositions
*/
size_t single_goldbach(size_t k);
/**
This function returns the number of possible decompositions into a sum of two prime numbers of all even integers in the interval [4,n].
@param[in] n upper integer bound
@param[out] count_vector vector containing the number of decompositions for a natural number the corresponding index
*/
std::vector<size_t> count_goldbach(size_t n);

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#include "../utils/timing.h"
#include "goldbach.h"
#include <iostream>
#include <algorithm>
using namespace std;
int main(int argc, char **argv)
{
cout << "Check: 694 has "<< single_goldbach(694) << " decompositions." << endl << "----------------------------------------" << endl;
for(size_t n : {10000, 100000, 400000, 1000000, 2000000})
{
tic();
auto goldbach_vector = count_goldbach(n);
auto max_it = max_element(goldbach_vector.begin(), goldbach_vector.end());
size_t max_number = distance(goldbach_vector.begin(), max_it);
double time = toc();
cout << "The number " << max_number << " has " << *max_it << " decompositions. Duration: " << time << endl;
}
/*
The number 9240 has 329 decompositions. Duration: 0.00572876
The number 99330 has 2168 decompositions. Duration: 0.3342
The number 390390 has 7094 decompositions. Duration: 4.23734
The number 990990 has 15594 decompositions. Duration: 29.5817
The number 1981980 has 27988 decompositions. Duration: 135.985
*/
return 0;
}

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#pragma once
#include <cstring> //memset
#include <vector>
//using namespace std;
/** \brief Determines all prime numbers in interval [2, @p max].
*
* The sieve of Eratosthenes is used.
*
* The implementation originates from <a href="http://code.activestate.com/recipes/576559-fast-prime-generator/">Florian Mayer</a>.
*
* \param[in] max end of interval for the prime number search.
* \return vector of prime numbers @f$2,3,5, ..., p<=max @f$.
*
* \copyright
* Copyright (c) 2008 Florian Mayer (adapted by Gundolf Haase 2018)
*
* 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. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
*/
template <class T>
std::vector<T> get_primes(T max)
{
std::vector<T> primes;
char *sieve;
sieve = new char[max / 8 + 1];
// Fill sieve with 1
memset(sieve, 0xFF, (max / 8 + 1) * sizeof(char));
for (T x = 2; x <= max; x++)
{
if (sieve[x / 8] & (0x01 << (x % 8))) {
primes.push_back(x);
// Is prime. Mark multiplicates.
for (T j = 2 * x; j <= max; j += x)
{
sieve[j / 8] &= ~(0x01 << (j % 8));
}
}
}
delete[] sieve;
return primes;
}
//---------------------------------------------------------------
//int main() // by Florian Mayer
//{g++ -O3 -std=c++14 -fopenmp main.cpp && ./a.out
// vector<unsigned long> primes;
// primes = get_primes(10000000);
// // return 0;
// // Print out result.
// vector<unsigned long>::iterator it;
// for(it=primes.begin(); it < primes.end(); it++)
// cout << *it << " ";
//
// cout << endl;
// return 0;
//}

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#pragma once
#include "sigmoid.h"
#include <iostream>
#include <vector>
using namespace std;
class DenseMatrix
{
private:
vector<double> M;
size_t n;
size_t m;
public:
vector<double> Mult(const vector<double> &x) const
{
vector<double> y(n,0);
for(size_t i = 0; i < n; ++i) // iterate row
{
for(size_t j = 0; j < m; ++j) // iterate column
{
y[i] += M[i*m + j]*x[j];
}
}
return y;
}
vector<double> MultT(const vector<double> &y) const
{
vector<double> x(m,0);
for(size_t j = 0; j < m; ++j) // iterate column
{
for(size_t i = 0; i < n; ++i) // iterate row
{
x[j] += M[i*m + j]*y[i];
}
}
return x;
}
void Print() const
{
for(size_t i = 0; i < n; ++i) // iterate row
{
for(size_t j = 0; j < m; ++j) // iterate column
{
cout << M[i*m + j] << " ";
}
cout << endl;
}
cout << endl;
}
DenseMatrix(size_t n, size_t m)
{
this->n = n;
this->m = m;
M = vector<double>(n*m);
size_t nm = max(n,m);
for(size_t i = 0; i < n; ++i) // iterate row
{
for(size_t j = 0; j < m; ++j) // iterate column
{
M[i*m + j] = sigmoid(x_entry(i,nm))*sigmoid(x_entry(j,nm));
}
}
}
};

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#pragma once
#include <cmath>
#include <iostream>
#include <vector>
using namespace std;
class ProductMatrix
{
private:
vector<double> u;
vector<double> v;
size_t n;
size_t m;
public:
vector<double> Mult(const vector<double> &x) const
{
vector<double> y(n,0);
for(int i = 0; i < n; ++i)
{
for(int j = 0; j < m; ++j)
{
y[i] += v[j]*x[j];
}
y[i] *= u[i];
}
return y;
}
vector<double> MultT(const vector<double> &y) const
{
vector<double> x(m,0);
for(int j = 0; j < m; ++j)
{
for(int i = 0; i < n; ++i)
{
x[j] += y[i]*u[i];
}
x[j] *= v[j];
}
return x;
}
ProductMatrix(const vector<double> &u, const vector<double> &v)
{
n = u.size();
m = v.size();
this->u = u;
this->v = v;
}
};

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#include "../utils/timing.h"
#include "DenseMatrix.h"
#include "ProductMatrix.h"
#include <algorithm>
int main()
{
// b) ------------------------------------------------------------------------------------------------------
DenseMatrix const M(5,3);
vector<double> const u{{1, 2, 3}};
vector<double> f1 = M.Mult(u);
vector<double> const v{{-1, 2, -3, 4, -5}};
vector<double> f2 = M.MultT(v);
M.Print();
for(size_t i = 0; i < f1.size(); ++i)
cout << f1[i] << endl;
cout << endl;
for(size_t j = 0; j < f2.size(); ++j)
cout << f2[j] << " ";
cout << endl << "-------------------------------------------------" << endl;
// c) ------------------------------------------------------------------------------------------------------
size_t n = pow(10,3);
DenseMatrix const M_1(n,n);
vector<double> x(n, 1.0);
size_t n_loops = 100;
vector<double> y_1;
vector<double> y_2;
double time_1 = 0;
double time_2 = 0;
tic();
for(int l = 0; l < n_loops; ++l)
y_1 = M_1.Mult(x);
time_1 += toc();
tic();
for(int l = 0; l < n_loops; ++l)
y_2 = M_1.MultT(x);
time_2 += toc();
vector<double> error_vec(n,0);
for(int i = 0; i < n; ++i)
error_vec[i] = abs(y_1[i] - y_2[i]);
double sup_error = *max_element(error_vec.begin(), error_vec.end());
cout << "n = " << n << endl;
cout << "Average duration for Mult: " << time_1/n_loops << endl;
cout << "Average duration for MultT: " << time_2/n_loops << endl;
cout << "sup-error: " << sup_error << endl;
cout << "-------------------------------------------------" << endl;
// d) ------------------------------------------------------------------------------------------------------
vector<double> u_M(n,0);
for(int i = 0; i < n; ++i)
u_M[i] = sigmoid(x_entry(i, n));
ProductMatrix const M_2(u_M, u_M);
time_1 = 0;
time_2 = 0;
tic();
for(int l = 0; l < n_loops; ++l)
y_1 = M_2.Mult(x);
time_1 += toc();
tic();
for(int l = 0; l < n_loops; ++l)
y_2 = M_2.MultT(x);
time_2 += toc();
for(int i = 0; i < n; ++i)
error_vec[i] = abs(y_1[i] - y_2[i]);
sup_error = *max_element(error_vec.begin(), error_vec.end());
cout << "n = " << n << endl;
cout << "Average duration for Mult: " << time_1/n_loops << endl;
cout << "Average duration for MultT: " << time_2/n_loops << endl;
cout << "sup-error: " << sup_error << endl;
cout << "-------------------------------------------------" << endl;
return 0;
}

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#pragma once
#include <cmath>
double sigmoid(double x)
{
return 1./(1. + exp(-x));
}
double x_entry(size_t k, size_t nm)
{
return (10.*k)/(nm - 1) - 5.;
}