141 lines
4.5 KiB
Makefile
141 lines
4.5 KiB
Makefile
# -*- makefile -*-
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# tested on CentOS 7, Ubuntu 16 and Ubuntu 14, see below to adjust flags to distribution.
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CXX = g++ -std=c++11
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CXXFLAGS = -fPIC -m64 -Wall -g -O3 -fopenmp -Wno-sign-compare
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CPUFLAGS = -mavx -msse4 -mpopcnt
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LDFLAGS = -fPIC -fopenmp
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# common linux flags
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SHAREDEXT = so
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SHAREDFLAGS = -shared
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MKDIR_P = mkdir -p
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prefix ?= /usr/local
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exec_prefix ?= ${prefix}
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libdir = ${exec_prefix}/lib
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includedir = ${prefix}/include
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##########################################################################
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# Uncomment one of the 4 BLAS/Lapack implementation options
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# below. They are sorted # from fastest to slowest (in our
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# experiments).
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##########################################################################
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#
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# 1. Intel MKL
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#
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# This is the fastest BLAS implementation we tested. Unfortunately it
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# is not open-source and determining the correct linking flags is a
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# nightmare. See
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#
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# https://software.intel.com/en-us/articles/intel-mkl-link-line-advisor
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#
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# The latest tested version is MKL 2017.0.098 (2017 Initial Release) and can
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# be downloaded here:
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#
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# https://registrationcenter.intel.com/en/forms/?productid=2558&licensetype=2
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#
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# The following settings are working if MKL is installed on its default folder:
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#
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# MKLROOT = /opt/intel/compilers_and_libraries/linux/mkl/
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#
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# LDFLAGS += -Wl,--no-as-needed -L$(MKLROOT)/lib/intel64
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# LIBS += -lmkl_intel_ilp64 -lmkl_core -lmkl_gnu_thread -ldl -lpthread
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#
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# CPPFLAGS += -DFINTEGER=long
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#
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# You may have to set the LD_LIBRARY_PATH=$MKLROOT/lib/intel64 at runtime.
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#
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# If at runtime you get the error:
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# Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so
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# you may set
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# LD_PRELOAD=$MKLROOT/lib/intel64/libmkl_core.so:$MKLROOT/lib/intel64/libmkl_sequential.so
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# at runtime as well.
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#
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# 2. Openblas
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#
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# The library contains both BLAS and Lapack. About 30% slower than MKL. Please see
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# https://github.com/facebookresearch/faiss/wiki/Troubleshooting#slow-brute-force-search-with-openblas
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# to fix performance problemes with OpenBLAS
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# for Ubuntu 16:
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# sudo apt-get install libopenblas-dev python-numpy python-dev
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# for Ubuntu 14:
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# sudo apt-get install libopenblas-dev liblapack3 python-numpy python-dev
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CPPFLAGS += -DFINTEGER=int
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LIBS += -lopenblas -llapack
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# 3. Atlas
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#
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# Automatically tuned linear algebra package. As the name indicates,
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# it is tuned automatically for a give architecture, and in Linux
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# distributions, it the architecture is typically indicated by the
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# directory name, eg. atlas-sse3 = optimized for SSE3 architecture.
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#
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# BLASCFLAGS=-DFINTEGER=int
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# BLASLDFLAGS=/usr/lib64/atlas-sse3/libptf77blas.so.3 /usr/lib64/atlas-sse3/liblapack.so
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#
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# 4. reference implementation
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#
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# This is just a compiled version of the reference BLAS
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# implementation, that is not optimized at all.
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#
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# CPPFLAGS += -DFINTEGER=int
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# LIBS += /usr/lib64/libblas.so.3 /usr/lib64/liblapack.so.3.2
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#
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##########################################################################
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# SWIG and Python flags
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##########################################################################
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# SWIG executable. This should be at least version 3.x
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SWIG = swig
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# The Python include directories for a given python executable can
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# typically be found with
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#
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# python -c "import distutils.sysconfig; print distutils.sysconfig.get_python_inc()"
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# python -c "import numpy ; print numpy.get_include()"
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#
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# or, for Python 3, with
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#
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# python3 -c "import distutils.sysconfig; print(distutils.sysconfig.get_python_inc())"
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# python3 -c "import numpy ; print(numpy.get_include())"
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#
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PYTHONCFLAGS = -I/usr/include/python2.7/ -I/usr/lib64/python2.7/site-packages/numpy/core/include/
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PYTHONLIB = -lpython
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PYTHON = /usr/bin/python
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###########################################################################
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# Cuda GPU flags
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###########################################################################
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# root of the cuda 8 installation
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CUDAROOT = /usr/local/cuda-8.0
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NVCC = $(CUDAROOT)/bin/nvcc
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NVCCLDFLAGS = -L$(CUDAROOT)/lib64
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NVCCLIBS = -lcudart -lcublas -lcuda
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CUDACFLAGS = -I$(CUDAROOT)/include
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NVCCFLAGS = -I $(CUDAROOT)/targets/x86_64-linux/include/ \
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-Xcompiler -fPIC \
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-Xcudafe --diag_suppress=unrecognized_attribute \
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-gencode arch=compute_35,code="compute_35" \
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-gencode arch=compute_52,code="compute_52" \
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-gencode arch=compute_60,code="compute_60" \
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-lineinfo \
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-ccbin $(CXX) -DFAISS_USE_FLOAT16
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