blob: 2a0512f82b0797fa80e83843b3d5504689c35642 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
|
# INSTALL GEMMA: Genome-wide Efficient Mixed Model Association
## Check version
Simply run gemma once installed
gemma
and it should give you the version.
## GEMMA dependencies
GEMMA runs on Linux and MAC OSX and the runtime has the following
dependencies:
* C++ tool chain >= 4.9
* GNU Science library (GSL) 1.x (note that 2.x is not yet supported)
* blas/openblas
* lapack
* [Eigen3 library](http://eigen.tuxfamily.org/dox/)
* zlib
See below for installation on Guix.
## Install GEMMA
### Debian and Ubuntu
Travis-CI uses Ubuntu for testing. Check the test logs for version numbers.
[](https://travis-ci.org/genetics-statistics/GEMMA)
Current settings can be found in [travis.yml](.travis.yml).
### Bioconda
(Note Bioconda install is a work in [progress](https://github.com/genetics-statistics/GEMMA/issues/52)
Recent versions of GEMMA can be installed with
[BioConda](http://ddocent.com/bioconda/) without root permissions using the following
command
conda install gemma
### GNU Guix
The GNU Guix package manager can install recent versions of [GEMMA](https://www.gnu.org/software/guix/packages/g.html)
using the following command
guix package -i gemma
To build GEMMA from source you can opt to install the build tools with GNU Guix
guix package -i make gcc linux-libre-headers gsl eigen openblas lapack glibc ld-wrapper
### Install from source
Install listed dependencies and run
make -j 4
(the -j switch builds on 4 cores).
if you get an Eigen error you may need to override the include
path. E.g. to build GEMMA on GNU Guix with shared libs the following
may work
make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 WITH_OPENBLAS=1
another example overriding optimization and LIB flags (so as to link
against gslv1) would be
make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 WITH_OPENBLAS=1 GCC_FLAGS="-Wall -isystem/$HOME/opt/gsl1/include" LIBS="$HOME/opt/gsl1/lib/libgsl.a $HOME/opt/gsl1/lib/libgslcblas.a -L$HOME/.guix-profile/lib -pthread -llapack -lblas -lz"
to run GEMMA tests
time make check
You can run gemma in the debugger with, for example
gdb --args \
./bin/gemma -g example/mouse_hs1940.geno.txt.gz \
-p example/mouse_hs1940.pheno.txt -a example/mouse_hs1940.anno.txt \
-snps example/snps.txt -nind 400 -loco 1 -gk -debug -o myoutput
Note that if you get <optimized out> warnings on inspecting variables you
should compile with GCC_FLAGS="" to disable optimizations (-O3). E.g.
make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 WITH_OPENBLAS=1 GCC_FLAGS=
If you get older OpenBlas errors you may need to add
OPENBLAS_LEGACY=1.
Other options, such as compiling with warnings, are listed in the
Makefile.
## Run tests
GEMMA includes the shunit2 test framework (version 2.0).
make check
or
./run_tests.sh
## Optimizing performance
### OpenBlas
Linking against a built-from-source OpenBlas is a first optimization
step because it will optimize code for the local architecture (on my
workstation it easily doubles speed). When you check the output .log
file of GEMMA after a run, it will tell you how the linked-in OpenBlas
was compiled.
To link a new version, compile OpenBlas as per
[instructions](http://www.openblas.net/). You can start with the
default:
make
and/or play with the switches (listed in OpenBlas Makefile.rule)
make BINARY=64 NO_WARMUP=0 GEMM_MULTITHREAD_THRESHOLD=4 USE_THREAD=1 NO_AFFINITY=0 NO_LAPACK=1 NUM_THREADS=64 NO_SHARED=1
and you should see something like
OpenBLAS build complete. (BLAS CBLAS)
OS ... Linux
Architecture ... x86_64
BINARY ... 64bit
C compiler ... GCC (command line : gcc)
Fortran compiler ... GFORTRAN (command line : gfortran)
Library Name ... libopenblas_haswellp-r0.3.0.dev.a (Multi threaded; Max num-threads is 64)
Note that OpenBlas by default uses a 32-bit integer API which can
overflow with large matrix sizes. We don't include LAPACK - the
OpenBlas version gives problems around eigenvalues for some reason.
We now have a static library which you can link using the full path
with using the GEMMA Makefile:
time env OPENBLAS_NUM_THREADS=4 make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 LIBS="~/tmp/OpenBLAS/libopenblas_haswellp-r0.3.0.dev.a -lgsl -lgslcblas -pthread -lz -llapack" WITH_OPENBLAS=1 -j 4 unittests
Latest (INT64, no gslcblas):
time env OPENBLAS_NUM_THREADS=4 make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 LIBS="~/opt/gsl2/lib/libgsl.a ~/tmp/OpenBLAS/libopenblas_haswellp-r0.3.0.dev.a -pthread -lz -llapack" WITH_OPENBLAS=1 OPENBLAS_INCLUDE_PATH=~/tmp/OpenBLAS/ -j 4 fast-check
### OpenBlas 64-bit API
<i>Warning: This is work in progress (WIP)</i>
OpenBlas supports a 64-bit API which allows for large matrices. Unfortunately
GEMMA does not support it yet, see https://github.com/genetics-statistics/GEMMA/issues/120
For testing we can build
make BINARY=64 INTERFACE64=1 NO_WARMUP=1 USE_THREAD=0 NO_LAPACK=0 NO_SHARED=1 -j 4
This builds a 64-bit binary and API and no external LAPACK. This is a very conservative
setting for testing the 64-bit API.
Note, for performance we want a 64-bit binary with threading.
make EIGEN_INCLUDE_PATH=~/.guix-profile/include/eigen3 LIBS="~/opt/gsl2/lib/libgsl.a ~/tmp/OpenBLAS/libopenblas_haswell-r0.3.0.dev.a ~/.guix-profile/lib/libgfortran.a ~/.guix-profile/lib/libquadmath.a -pthread -lz" WITH_OPENBLAS=1 OPENBLAS_INCLUDE_PATH=~/tmp/OpenBLAS/ -j 4 fast-check
Note we don't include standard lapack, because it is 32-bits.
|
