151 lines
4.6 KiB
Plaintext
151 lines
4.6 KiB
Plaintext
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the BSD+Patents license found in the
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# LICENSE file in the root directory of this source tree.
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# -*- makefile -*-
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# tested on CentOS 7, Ubuntu 16 and Ubuntu 14, see below to adjust flags to distribution.
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CC=g++
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CFLAGS=-fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp
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LDFLAGS=-g -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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FAISSSHAREDFLAGS=-shared
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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 MLK 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 MLK 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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# BLASLDFLAGS=-Wl,--no-as-needed -L$(MKLROOT)/lib/intel64 -lmkl_intel_ilp64 \
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# -lmkl_core -lmkl_gnu_thread -ldl -lpthread
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#
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# BLASCFLAGS=-DFINTEGER=long
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#
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# you may have to set the LD_LIBRARY_PATH=$MKLROOT/lib/intel64 at runtime.
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# 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 add 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.
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BLASCFLAGS=-DFINTEGER=int
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# This is for Centos:
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BLASLDFLAGS?=/usr/lib64/libopenblas.so.0
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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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# BLASLDFLAGS?=/usr/lib/libopenblas.so.0
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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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# BLASLDFLAGS?=/usr/lib/libopenblas.so.0 /usr/lib/lapack/liblapack.so.3.0
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#
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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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# BLASCFLAGS=-DFINTEGER=int
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# BLASLDFLAGS=/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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SWIGEXEC=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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###########################################################################
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# Cuda GPU flags
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###########################################################################
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# a C++ compiler that supports c++11
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CC11=g++
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# root of the cuda 8 installation
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CUDAROOT=/usr/local/cuda-8.0/
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CUDACFLAGS=-I$(CUDAROOT)/include
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NVCC=$(CUDAROOT)/bin/nvcc
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NVCCFLAGS= $(CUDAFLAGS) \
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-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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--std c++11 -lineinfo \
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-ccbin $(CC11) -DFAISS_USE_FLOAT16
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# BLAS LD flags for nvcc (used to generate an executable)
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# if BLASLDFLAGS contains several flags, each one may
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# need to be prepended with -Xlinker
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BLASLDFLAGSNVCC=-Xlinker $(BLASLDFLAGS)
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# Same, but to generate a .so
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BLASLDFLAGSSONVCC=-Xlinker $(BLASLDFLAGS)
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