I have sqlite3 installed in Ubuntu.
$ which sqlite3
/home/user/anaconda3/bin/sqlite3
I'm trying to install the sqlite driver in Haskell and see the following error:
$ stack install sqlite
sqlite-0.5.5: configure
sqlite-0.5.5: build
-- While building custom Setup.hs for package sqlite-0.5.5 using:
/tmp/stack16820/sqlite-0.5.5/.stack-work/dist/x86_64-linux/Cabal-2.2.0.1/setup/setup --builddir=.stack-work/dist/x86_64-linux/Cabal-2.2.0.1 build --ghc-options " -ddump-hi -ddump-to-file -fdiagnostics-color=always"
Process exited with code: ExitFailure 1
Logs have been written to: /home/user/.stack/global-project/.stack-work/logs/sqlite-0.5.5.log
[1 of 2] Compiling Main ( /tmp/stack16820/sqlite-0.5.5/Setup.hs, /tmp/stack16820/sqlite-0.5.5/.stack-work/dist/x86_64-linux/Cabal-2.2.0.1/setup/Main.o )
[2 of 2] Compiling StackSetupShim ( /home/user/.stack/setup-exe-src/setup-shim-mPHDZzAJ.hs, /tmp/stack16820/sqlite-0.5.5/.stack-work/dist/x86_64-linux/Cabal-2.2.0.1/setup/StackSetupShim.o )
Linking /tmp/stack16820/sqlite-0.5.5/.stack-work/dist/x86_64-linux/Cabal-2.2.0.1/setup/setup ...
Configuring sqlite-0.5.5...
Preprocessing library for sqlite-0.5.5..
Building library for sqlite-0.5.5..
[1 of 5] Compiling Database.SQL.Types ( Database/SQL/Types.hs, .stack-work/dist/x86_64-linux/Cabal-2.2.0.1/build/Database/SQL/Types.o )
/tmp/stack16820/sqlite-0.5.5/Database/SQL/Types.hs:481:37: error:
Ambiguous occurrence ‘<>’
It could refer to either ‘Prelude.<>’,
imported from ‘Prelude’ at Database/SQL/Types.hs:15:8-25
(and originally defined in ‘GHC.Base’)
or ‘Text.PrettyPrint.HughesPJ.<>’,
imported from ‘Text.PrettyPrint.HughesPJ’ at Database/SQL/Types.hs:56:1-32
|
481 | FunSQLExpr f es -> text f <> parens (commaH ppSQLExpr es)
| ^^
Which of ghc, stack, cabal, anaconda, sqlite should I interrogate to figure out what is failing?
I'm trying to install the sqlite driver in Haskell and see the following error
The sqlite package is deprecated in favour of sqlite-simple So, I would recommend you to not use this. If you are looking for a much higher level of interface to sqlite, I would recommend persistent library.
If you want to still make it work, the easiest way is to just fix the compile errors by forking the code and make it work with the required GHC version.
TL;DR
stack install sqlite --resolver=lts-11.22 should work
First of all i would follow sibi advise and install the maintaned package sqlite-simple.
But if you want to use the deprecated one we could analyze the error:
The error is thrown in the haskell part, the combination of stack and haskell. No mention about c code so the sqlite lib (or anaconda) is not involved here
Ambiguous occurrence ‘<>’ It could refer to either ‘Prelude.<>’, or ‘Text.PrettyPrint.HughesPJ.<>’: The simbol <> is defined in two modules and the client code doesn't disambiguate them (surely cause it was only in one module at the time)
So we have to compile with a version of Prelude or Text.PrettyPrint.HughesPJ without the definition of <>
Text.PrettyPrint.HughesPJ is a module from the pretty package and all versions of the package have the operator exported
The Prelude is defined in the base library and it is fixed for each version of ghc, that in turn is fixed for each major version of the stackage resolver. You can set the global stack resolver in ~/stack/global-project/stack.yaml or in each call to stack with the option --resolver=
The package sqlite accepts base versions in the range >=3 && <5 (in fact that is the cause of the problem, it is too flexible)
If we search for <> in the changelog of base we can see it was exported since version 4.11.0. And that version was shipped with ghc-8.4.1 (you can see the matrix between versions here). So we have to choose a stackage resolver linked to a previous version of ghc: f.e. lts-11.22 with ghc-8.2.2
I want to help Julia find my .jl file by modifying the LOAD_PATH variable:
julia> readdir()
1-element Array{String,1}:
"test.jl"
shell> cmd /c type test.jl
# Test.jl
module Test
export f
f() = println("hi")
end
julia> push!(LOAD_PATH,pwd());
julia> import Test
ERROR: ArgumentError: Module Test not found in current path.
Run `Pkg.add("Test")` to install the Test package.
in require(::Symbol) at .\loading.jl:365
The first call to readdir() proves that I have a file called test.jl in my current directory. The following shell call shows that this file contains a module called Test. The next call to push!(LOAD_PATH,pwd()); puts the current directory in LOAD_PATH. But even with the current directory in LOAD_PATH, Julia still can't find the Test module in test.jl.
What's wrong?
The error was talking about something concerning require. As the doc says:
Given the statement using Foo, the system looks for Foo within Main. If the module does not exist, the system attempts to require("Foo"), which typically results in loading code from an installed package. ... require is case-sensitive on all platforms, including those with case-insensitive filesystems like macOS and Windows.
and the reason is clear: require couldn't find a file named Test in LOAD_PATH. So we need to make the file name matching the module name, but this is just a convention, not a mandatory rule. What will happen if someone mistakenly runs using test?
julia> push!(LOAD_PATH,pwd())
julia> using test
WARNING: requiring "test" in module "Main" did not define a corresponding module.
julia> whos()
Base 34427 KB Module
Core 12386 KB Module
Main 41296 KB Module
Test 1837 bytes Module
The result shows that we've loaded the file test.jl and the module(Test) in it, but not actually using/import the module. This is a respected behavior since we used a wrong module name, which is also the reason why julia complained in the warning. In this case, using test is equivalent to include("test.jl"), but I highly recommend you to follow the convention and do not use this behavior.
BTW, require became generally case-sensitive after this PR. A side-effect is your LOAD_PATH should also be case-sensitive, this will be fixed by this PR in julia-0.6.
Can any one tell me why I can not successfully test OpenBLAS's dgemm performance (in GFLOPs) in R via the following way?
link R with the "reference BLAS" libblas.so
compile my C program mmperf.c with OpenBLAS library libopenblas.so
load the resulting shared library mmperf.so into R, call the R wrapper function mmperf and report dgemm performance in GFLOPs.
Point 1 looks strange, but I have no choice because I have no root access on machines I want to test, so actual linking to OpenBLAS is impossible. By "not successfully" I mean my program ends up reporting dgemm performance for reference BLAS instead of OpenBLAS. I hope someone can explain to me:
why my way does not work;
is it possible at all to make it work (this is important, because if it is impossible, I must write a C main function and do my job in a C program.)
I've investigated into this issue for two days, here I will include various system output to assist you to make a diagnose. To make things reproducible, I will also include the code, makefile as well as shell command.
Part 1: system environment before testing
There are 2 ways to invoke R, either using R or Rscript. There are some differences in what is loaded when they are invoked:
~/Desktop/dgemm$ readelf -d $(R RHOME)/bin/exec/R | grep "NEEDED"
0x00000001 (NEEDED) Shared library: [libR.so]
0x00000001 (NEEDED) Shared library: [libpthread.so.0]
0x00000001 (NEEDED) Shared library: [libc.so.6]
~/Desktop/dgemm$ readelf -d $(R RHOME)/bin/Rscript | grep "NEEDED"
0x00000001 (NEEDED) Shared library: [libc.so.6]
Here we need to choose Rscript, because R loads libR.so, which will automatically load the reference BLAS libblas.so.3:
~/Desktop/dgemm$ readelf -d $(R RHOME)/lib/libR.so | grep blas
0x00000001 (NEEDED) Shared library: [libblas.so.3]
~/Desktop/dgemm$ ls -l /etc/alternatives/libblas.so.3
... 31 May /etc/alternatives/libblas.so.3 -> /usr/lib/libblas/libblas.so.3.0
~/Desktop/dgemm$ readelf -d /usr/lib/libblas/libblas.so.3 | grep SONAME
0x0000000e (SONAME) Library soname: [libblas.so.3]
Comparatively, Rscript gives a cleaner environment.
Part 2: OpenBLAS
After downloading source file from OpenBLAS and a simple make command, a shared library of the form libopenblas-<arch>-<release>.so-<version> can be generated. Note that we will not have root access to install it; instead, we copy this library into our working directory ~/Desktop/dgemm and rename it simply to libopenblas.so. At the same time we have to make another copy with name libopenblas.so.0, as this is the SONAME which run time loader will seek for:
~/Desktop/dgemm$ readelf -d libopenblas.so | grep "RPATH\|SONAME"
0x0000000e (SONAME) Library soname: [libopenblas.so.0]
Note that the RPATH attribute is not given, which means this library is intended to be put in /usr/lib and we should call ldconfig to add it to ld.so.cache. But again we don't have root access to do this. In fact, if this can be done, then all the difficulties are gone. We could then use update-alternatives --config libblas.so.3 to effectively link R to OpenBLAS.
Part 3: C code, Makefile, and R code
Here is a C script mmperf.c computing GFLOPs of multiplying 2 square matrices of size N:
#include <R.h>
#include <Rmath.h>
#include <Rinternals.h>
#include <R_ext/BLAS.h>
#include <sys/time.h>
/* standard C subroutine */
double mmperf (int n) {
/* local vars */
int n2 = n * n, tmp; double *A, *C, one = 1.0;
struct timeval t1, t2; double elapsedTime, GFLOPs;
/* simulate N-by-N matrix A */
A = (double *)calloc(n2, sizeof(double));
GetRNGstate();
tmp = 0; while (tmp < n2) {A[tmp] = runif(0.0, 1.0); tmp++;}
PutRNGstate();
/* generate N-by-N zero matrix C */
C = (double *)calloc(n2, sizeof(double));
/* time 'dgemm.f' for C <- A * A + C */
gettimeofday(&t1, NULL);
F77_CALL(dgemm) ("N", "N", &n, &n, &n, &one, A, &n, A, &n, &one, C, &n);
gettimeofday(&t2, NULL);
/* free memory */
free(A); free(C);
/* compute and return elapsedTime in microseconds (usec or 1e-6 sec) */
elapsedTime = (double)(t2.tv_sec - t1.tv_sec) * 1e+6;
elapsedTime += (double)(t2.tv_usec - t1.tv_usec);
/* convert microseconds to nanoseconds (1e-9 sec) */
elapsedTime *= 1e+3;
/* compute and return GFLOPs */
GFLOPs = 2.0 * (double)n2 * (double)n / elapsedTime;
return GFLOPs;
}
/* R wrapper */
SEXP R_mmperf (SEXP n) {
double GFLOPs = mmperf(asInteger(n));
return ScalarReal(GFLOPs);
}
Here is a simple R script mmperf.R to report GFLOPs for case N = 2000
mmperf <- function (n) {
dyn.load("mmperf.so")
GFLOPs <- .Call("R_mmperf", n)
dyn.unload("mmperf.so")
return(GFLOPs)
}
GFLOPs <- round(mmperf(2000), 2)
cat(paste("GFLOPs =",GFLOPs, "\n"))
Finally there is a simple makefile to generate the shared library mmperf.so:
mmperf.so: mmperf.o
gcc -shared -L$(shell pwd) -Wl,-rpath=$(shell pwd) -o mmperf.so mmperf.o -lopenblas
mmperf.o: mmperf.c
gcc -fpic -O2 -I$(shell Rscript --default-packages=base --vanilla -e 'cat(R.home("include"))') -c mmperf.c
Put all these files under working directory ~/Desktop/dgemm, and compile it:
~/Desktop/dgemm$ make
~/Desktop/dgemm$ readelf -d mmperf.so | grep "NEEDED\|RPATH\|SONAME"
0x00000001 (NEEDED) Shared library: [libopenblas.so.0]
0x00000001 (NEEDED) Shared library: [libc.so.6]
0x0000000f (RPATH) Library rpath: [/home/zheyuan/Desktop/dgemm]
The output reassures us that OpenBLAS is correctly linked, and the run time load path is correctly set.
Part 4: testing OpenBLAS in R
Let's do
~/Desktop/dgemm$ Rscript --default-packages=base --vanilla mmperf.R
Note our script needs only the base package in R, and --vanilla is used to ignore all user settings on R start-up. On my laptop, my program returns:
GFLOPs = 1.11
Oops! This is truely reference BLAS performance not OpenBLAS (which is about 8-9 GFLOPs).
Part 5: Why?
To be honest, I don't know why this happens. Each step seems to work correctly. Does something subtle occurs when R is invoked? For example, any possibility that OpenBLAS library is overridden by reference BLAS at some point for some reason? Any explanations and solutions? Thanks!
why my way does not work
First, shared libraries on UNIX are designed to mimic the way archive libraries work (archive libraries were there first). In particular that means that if you have libfoo.so and libbar.so, both defining symbol foo, then whichever library is loaded first is the one that wins: all references to foo from anywhere within the program (including from libbar.so) will bind to libfoo.sos definition of foo.
This mimics what would happen if you linked your program against libfoo.a and libbar.a, where both archive libraries defined the same symbol foo. More info on archive linking here.
It should be clear from above, that if libblas.so.3 and libopenblas.so.0 define the same set of symbols (which they do), and if libblas.so.3 is loaded into the process first, then routines from libopenblas.so.0 will never be called.
Second, you've correctly decided that since R directly links against libR.so, and since libR.so directly links against libblas.so.3, it is guaranteed that libopenblas.so.0 will lose the battle.
However, you erroneously decided that Rscript is better, but it's not: Rscript is a tiny binary (11K on my system; compare to 2.4MB for libR.so), and approximately all it does is exec of R. This is trivial to see in strace output:
strace -e trace=execve /usr/bin/Rscript --default-packages=base --vanilla /dev/null
execve("/usr/bin/Rscript", ["/usr/bin/Rscript", "--default-packages=base", "--vanilla", "/dev/null"], [/* 42 vars */]) = 0
execve("/usr/lib/R/bin/R", ["/usr/lib/R/bin/R", "--slave", "--no-restore", "--vanilla", "--file=/dev/null", "--args"], [/* 43 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=89625, si_status=0, si_utime=0, si_stime=0} ---
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=89626, si_status=0, si_utime=0, si_stime=0} ---
execve("/usr/lib/R/bin/exec/R", ["/usr/lib/R/bin/exec/R", "--slave", "--no-restore", "--vanilla", "--file=/dev/null", "--args"], [/* 51 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=89630, si_status=0, si_utime=0, si_stime=0} ---
+++ exited with 0 +++
Which means that by the time your script starts executing, libblas.so.3 has been loaded, and libopenblas.so.0 that will be loaded as a dependency of mmperf.so will not actually be used for anything.
is it possible at all to make it work
Probably. I can think of two possible solutions:
Pretend that libopenblas.so.0 is actually libblas.so.3
Rebuild entire R package against libopenblas.so.
For #1, you need to ln -s libopenblas.so.0 libblas.so.3, then make sure that your copy of libblas.so.3 is found before the system one, by setting LD_LIBRARY_PATH appropriately.
This appears to work for me:
mkdir /tmp/libblas
# pretend that libc.so.6 is really libblas.so.3
cp /lib/x86_64-linux-gnu/libc.so.6 /tmp/libblas/libblas.so.3
LD_LIBRARY_PATH=/tmp/libblas /usr/bin/Rscript /dev/null
Error in dyn.load(file, DLLpath = DLLpath, ...) :
unable to load shared object '/usr/lib/R/library/stats/libs/stats.so':
/usr/lib/liblapack.so.3: undefined symbol: cgemv_
During startup - Warning message:
package ‘stats’ in options("defaultPackages") was not found
Note how I got an error (my "pretend" libblas.so.3 doesn't define symbols expected of it, since it's really a copy of libc.so.6).
You can also confirm which version of libblas.so.3 is getting loaded this way:
LD_DEBUG=libs LD_LIBRARY_PATH=/tmp/libblas /usr/bin/Rscript /dev/null |& grep 'libblas\.so\.3'
91533: find library=libblas.so.3 [0]; searching
91533: trying file=/usr/lib/R/lib/libblas.so.3
91533: trying file=/usr/lib/x86_64-linux-gnu/libblas.so.3
91533: trying file=/usr/lib/jvm/java-7-openjdk-amd64/jre/lib/amd64/server/libblas.so.3
91533: trying file=/tmp/libblas/libblas.so.3
91533: calling init: /tmp/libblas/libblas.so.3
For #2, you said:
I have no root access on machines I want to test, so actual linking to OpenBLAS is impossible.
but that seems to be a bogus argument: if you can build libopenblas, surely you can also build your own version of R.
Update:
You mentioned in the beginning that libblas.so.3 and libopenblas.so.0 define the same symbol, what does this mean? They have different SONAME, is that insufficient to distinguish them by the system?
The symbols and the SONAME have nothing to do with each other.
You can see symbols in the output from readelf -Ws libblas.so.3 and readelf -Ws libopenblas.so.0. Symbols related to BLAS, such as cgemv_, will appear in both libraries.
Your confusion about SONAME possibly comes from Windows. The DLLs on Windows are designed completely differently. In particular, when FOO.DLL imports symbol bar from BAR.DLL, both the name of the symbol (bar) and the DLL from which that symbol was imported (BAR.DLL) are recorded in the FOO.DLLs import table.
That makes it easy to have R import cgemv_ from BLAS.DLL, while MMPERF.DLL imports the same symbol from OPENBLAS.DLL.
However, that makes library interpositioning hard, and works completely differently from the way archive libraries work (even on Windows).
Opinions differ on which design is better overall, but neither system is likely to ever change its model.
There are ways for UNIX to emulate Windows-style symbol binding: see RTLD_DEEPBIND in dlopen man page. Beware: these are fraught with peril, likely to confuse UNIX experts, are not widely used, and likely to have implementation bugs.
Update 2:
you mean I compile R and install it under my home directory?
Yes.
Then when I want to invoke it, I should explicitly give the path to my version of executable program, otherwise the one on the system might be invoked instead? Or, can I put this path at the first position of environment variable $PATH to cheat the system?
Either way works.
*********************
Solution 2:
*********************
Here we offer another solution, by exploiting environment variable LD_PRELOAD mentioned in our solution 1. The use of LD_PRELOAD is more "brutal", as it forces loading a given library into the program before any other program, even before the C library libc.so! This is often used for urgent patching in Linux development.
As shown in the part 2 of the original post, the shared BLAS library libopenblas.so has SONAME libopenblas.so.0. An SONAME is an internal name that dynamic library loader would seek at run time, so we need to make a symbolic link to libopenblas.so with this SONAME:
~/Desktop/dgemm$ ln -sf libopenblas.so libopenblas.so.0
then we export it:
~/Desktop/dgemm$ export LD_PRELOAD=$(pwd)/libopenblas.so.0
Note that a full path to libopenblas.so.0 needs be fed to LD_PRELOAD for a successful load, even if libopenblas.so.0 is under $(pwd).
Now we launch Rscript and check what happens by LD_DEBUG:
~/Desktop/dgemm$ LD_DEBUG=libs Rscript --default-packages=base --vanilla /dev/null |& grep blas
4860: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4860: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4865: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4868: calling fini: /home/zheyuan/Desktop/dgemm/libopenblas.so [0]
4870: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4869: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4867: calling fini: /home/zheyuan/Desktop/dgemm/libopenblas.so [0]
4860: find library=libblas.so.3 [0]; searching
4860: trying file=/usr/lib/R/lib/libblas.so.3
4860: trying file=/usr/lib/i386-linux-gnu/i686/sse2/libblas.so.3
4860: trying file=/usr/lib/i386-linux-gnu/i686/cmov/libblas.so.3
4860: trying file=/usr/lib/i386-linux-gnu/i686/libblas.so.3
4860: trying file=/usr/lib/i386-linux-gnu/sse2/libblas.so.3
4860: trying file=/usr/lib/i386-linux-gnu/libblas.so.3
4860: trying file=/usr/lib/jvm/java-7-openjdk-i386/jre/lib/i386/client/libblas.so.3
4860: trying file=/usr/lib/libblas.so.3
4860: calling init: /usr/lib/libblas.so.3
4860: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4874: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4876: calling init: /home/zheyuan/Desktop/dgemm/libopenblas.so
4860: calling fini: /home/zheyuan/Desktop/dgemm/libopenblas.so [0]
4860: calling fini: /usr/lib/libblas.so.3 [0]
Comparing with what we saw in solution 1 by cheating R with our own version of libblas.so.3, we can see that
libopenblas.so.0 is loaded first, hence found first by Rscript;
after libopenblas.so.0 is found, Rscript goes on searching and loading libblas.so.3. However, this will play no effect by the "first come, first serve" rule, explained in the original answer.
Good, everything works, so we test our mmperf.c program:
~/Desktop/dgemm$ Rscript --default-packages=base --vanilla mmperf.R
GFLOPs = 9.62
The outcome 9.62 is bigger than 8.77 we saw in the earlier solution merely by chance. As a test for using OpenBLAS we don't run the experiment many times for preciser result.
Then as usual, we unset environment variable in the end:
~/Desktop/dgemm$ unset LD_PRELOAD
*********************
Solution 1:
*********************
Thanks to Employed Russian, my problem is finally solved. The investigation requires important skills in Linux system debugging and patching, and I believe this is a great asset I learned. Here I would post a solution, as well as correcting several points in my original post.
1 About invoking R
In my original post, I mentioned there are two ways to launch R, either via R or Rscript. However, I have wrongly exaggerated their difference. Let's now investigate their start-up process, via an important Linux debugging facility strace (see man strace). There are actually lots of interesting things happening after we type a command in the shell, and we can use
strace -e trace=process [command]
to trace all system calls involving process management. As a result we can watch the fork, wait, and execution steps of a process. Though not stated in the manual page, #Employed Russian shows that it is possible to specify only a subclass of process, for example, execve for the execution steps.
For R we have
~/Desktop/dgemm$ time strace -e trace=execve R --vanilla < /dev/null > /dev/null
execve("/usr/bin/R", ["R", "--vanilla"], [/* 70 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=5777, si_status=0, si_utime=0, si_stime=0} ---
execve("/usr/lib/R/bin/exec/R", ["/usr/lib/R/bin/exec/R", "--vanilla"], [/* 79 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=5778, si_status=0, si_utime=0, si_stime=0} ---
+++ exited with 0 +++
real 0m0.345s
user 0m0.256s
sys 0m0.068s
while for Rscript we have
~/Desktop/dgemm$ time strace -e trace=execve Rscript --default-packages=base --vanilla /dev/null
execve("/usr/bin/Rscript", ["Rscript", "--default-packages=base", "--vanilla", "/dev/null"], [/* 70 vars */]) = 0
execve("/usr/lib/R/bin/R", ["/usr/lib/R/bin/R", "--slave", "--no-restore", "--vanilla", "--file=/dev/null"], [/* 71 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=5822, si_status=0, si_utime=0, si_stime=0} ---
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=5823, si_status=0, si_utime=0, si_stime=0} ---
execve("/usr/lib/R/bin/exec/R", ["/usr/lib/R/bin/exec/R", "--slave", "--no-restore", "--vanilla", "--file=/dev/null"], [/* 80 vars */]) = 0
--- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=5827, si_status=0, si_utime=0, si_stime=0} ---
+++ exited with 0 +++
real 0m0.063s
user 0m0.020s
sys 0m0.028s
We have also used time to measure the start-up time. Note that
Rscript is about 5.5 times faster than R. One reason is that R will load 6 default packages on start-up, while Rscript only loads one base package by control: --default-packages=base. But it is still much faster even without this setting.
In the end both start-up processes are directed to $(R RHOME)/bin/exec/R, and in my original post, I have already exploited readelf -d to show that this executable will load libR.so, which are linked with libblas.so.3. According to #Employed Russian's explanation, the BLAS library loaded first will win, so there is no way my original method will work.
To successfully run strace, we have used the amazing file /dev/null as input file and output file when necessary. For example, Rscript demands an input file, while R demands both. We feed the null device to both to make the command run smoothly and the output clean. The null device is a physically existing file, but works amazingly. When reading from it, it contains nothing; while writing to it, it discards everything.
2. Cheat R
Now since libblas.so will be loaded anyway, the only thing we can do is to provide our own version of this library. As I have said in the original post, if we have root access, this is really easy, by using update-alternatives --config libblas.so.3, so that the system Linux will help us complete this switch. But #Employed Russian offers an awesome way to cheat the system without root access: let's check how R finds BLAS library on start-up, and make sure we feed our version before the system default is found! To monitor how shared libraries are found and loaded, use environment variable LD_DEBUG.
There are a number of Linux environment variables with prefix LD_, as documented in man ld.so. These variables can be assigned before an executable, so that we can change the running feature of a program. Some useful variables include:
LD_LIBRARY_PATH for setting run time library search path;
LD_DEBUG for tracing look-up and loading of shared libraries;
LD_TRACE_LOADED_OBJECTS for displaying all loaded library by a program (behaves similar to ldd);
LD_PRELOAD for forcing injecting a library to a program at the very start, before all other libraries are looked for;
LD_PROFILE and LD_PROFILE_OUTPUT for profiling one specified shared library. R user who have read section 3.4.1.1 sprof of Writing R extensions should recall that this is used for profiling compiled code from within R.
The use of LD_DEBUG can be seen by:
~/Desktop/dgemm$ LD_DEBUG=help cat
Valid options for the LD_DEBUG environment variable are:
libs display library search paths
reloc display relocation processing
files display progress for input file
symbols display symbol table processing
bindings display information about symbol binding
versions display version dependencies
scopes display scope information
all all previous options combined
statistics display relocation statistics
unused determined unused DSOs
help display this help message and exit
To direct the debugging output into a file instead of standard output a filename can be specified using the LD_DEBUG_OUTPUT environment variable.
Here we are particularly interested in using LD_DEBUG=libs. For example,
~/Desktop/dgemm$ LD_DEBUG=libs Rscript --default-packages=base --vanilla /dev/null |& grep blas
5974: find library=libblas.so.3 [0]; searching
5974: trying file=/usr/lib/R/lib/libblas.so.3
5974: trying file=/usr/lib/i386-linux-gnu/i686/sse2/libblas.so.3
5974: trying file=/usr/lib/i386-linux-gnu/i686/cmov/libblas.so.3
5974: trying file=/usr/lib/i386-linux-gnu/i686/libblas.so.3
5974: trying file=/usr/lib/i386-linux-gnu/sse2/libblas.so.3
5974: trying file=/usr/lib/i386-linux-gnu/libblas.so.3
5974: trying file=/usr/lib/jvm/java-7-openjdk-i386/jre/lib/i386/client/libblas.so.3
5974: trying file=/usr/lib/libblas.so.3
5974: calling init: /usr/lib/libblas.so.3
5974: calling fini: /usr/lib/libblas.so.3 [0]
shows various attempts that R program tried to locate and load libblas.so.3. So if we could provide our own version of libblas.so.3, and make sure R finds it first, then the problem is solved.
Let's first make a symbolic link libblas.so.3 in our working path to the OpenBLAS library libopenblas.so, then expand default LD_LIBRARY_PATH with our working path (and export it):
~/Desktop/dgemm$ ln -sf libopenblas.so libblas.so.3
~/Desktop/dgemm$ export LD_LIBRARY_PATH = $(pwd):$LD_LIBRARY_PATH ## put our working path at top
Now let's check again the library loading process:
~/Desktop/dgemm$ LD_DEBUG=libs Rscript --default-packages=base --vanilla /dev/null |& grep blas
6063: find library=libblas.so.3 [0]; searching
6063: trying file=/usr/lib/R/lib/libblas.so.3
6063: trying file=/usr/lib/i386-linux-gnu/i686/sse2/libblas.so.3
6063: trying file=/usr/lib/i386-linux-gnu/i686/cmov/libblas.so.3
6063: trying file=/usr/lib/i386-linux-gnu/i686/libblas.so.3
6063: trying file=/usr/lib/i386-linux-gnu/sse2/libblas.so.3
6063: trying file=/usr/lib/i386-linux-gnu/libblas.so.3
6063: trying file=/usr/lib/jvm/java-7-openjdk-i386/jre/lib/i386/client/libblas.so.3
6063: trying file=/home/zheyuan/Desktop/dgemm/libblas.so.3
6063: calling init: /home/zheyuan/Desktop/dgemm/libblas.so.3
6063: calling fini: /home/zheyuan/Desktop/dgemm/libblas.so.3 [0]
Great! We have successfully cheated R.
3. Experiment with OpenBLAS
~/Desktop/dgemm$ Rscript --default-packages=base --vanilla mmperf.R
GFLOPs = 8.77
Now, everything works as expected!
4. Unset LD_LIBRARY_PATH (to be safe)
It is a good practice to unset LD_LIBRARY_PATH after use.
~/Desktop/dgemm$ unset LD_LIBRARY_PATH