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Hi Jack >               dragonegg degg+vectorize degg+optnz  gfortran > ac               12.45       12.45         8.85       8.80 > gas_dyn          11.72       11.80         4.47       4.26 > induct           24.02       24.91        12.08      13.65 > rnflow           32.25       32.35        26.47      24.06 Any idea what might cause such differences here? -- With best

Attached are the Polyhedron 2005 benchmark results for current llvm/dragonegg svn on x86_64-apple-darwin11 built against Xcode 4.3.2 and FSF gcc 4.6.3. The benchmarks for -msse3 and -msse4 appear identical (at least for degg+optnz). This is fortunate since there seems to be a bug in -msse4 on 2.33 GHz (T7600) Intel Core 2 Duo Merom (http://llvm.org/bugs/show_bug.cgi?id=12434).

Hello, Is there a analysis pass which calculates the ModRefBehaviours OnlyReadsPointerArguments and OnlyAccessesPointerArguments? I tried to find one but so far I only saw that different AA's return OnlyReadsMemory and DoesNotAccessMemory if the readonly and readnone attributes are set. Best regards and thanks in advance, Johannes -- Johannes Doerfert Researcher / PhD Student

Hi Johannes, Actually, I'm working in the same scenario, i.e. configure + make of a benchmark/program/library like you said. I've got your point of using this script as a replacement to generate .bc files instead of a executable. That's truly helpful and has already answered my original question. Now I'm actually moving a step further. Take the same example in your reply, say, if

Hi Anton, >> dragonegg degg+vectorize degg+optnz gfortran >> ac 12.45 12.45 8.85 8.80 >> gas_dyn 11.72 11.80 4.47 4.26 >> induct 24.02 24.91 12.08 13.65 >> rnflow 32.25 32.35 26.47 24.06 > Any idea what might cause such differences

Hi all, I am relatively new to Linux and wine and I am trying to run an audio editing software called Goldwave 5.06 (version 4.26 works relatively well). 5.06 installs ok but when I go to run it I get the following error: "Driver does not support 16 bit colour depth or visual buffer could not be created. Visual disabled." And the meter displays do not work. I have tried adjusting

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not

Hi Johannes, By following your directions, I can use your script as is to produce the .bc file now. Here's my command line for compiling s_sin.c into s_sin.bc file and the output: command line: ~/Downloads/newlib-2.1.0/newlib/libm/mathfp » python ~/llvm_link.py s_sin.c -I../common/ -I../../libc/include/ -o s_sin.bc output: Initiate CLANG (/path-to-clang): Options: 's_sin.c

Below are the results for the Polyhedron 2005 benchmarks compiled with llvm/compiler-rt/dragonegg 3.3svn at r182439 against current FSF gcc 4.7.3svn and 4.8.1svn. The only major bug remaining in the dragonegg 3.3svn support for gcc 4.8.x is http://llvm.org/bugs/show_bug.cgi?id=15980 which results in unresolved symbols for _iround and _iroundf in the aermod and rnflow testcases. Note that this

Hi Jack, > Attached are the Polyhedron 2005 benchmark results for current llvm/dragonegg svn > on x86_64-apple-darwin11 built against Xcode 4.3.2 and FSF gcc 4.6.3. thanks for the numbers. How does this compare to LLVM 3.0 - were there any regressions? Ciao, Duncan. The benchmarks > for -msse3 and -msse4 appear identical (at least for degg+optnz). This is fortunate > since

Duncan, With r182593, the dragonegg 3.3 branch now completely passes the Polyhedron 2005 benchmarks using the FSF gcc 4.8.1svn compiler. Thanks. Jack Tested on x86_apple-darwin12 Compile Flags: -ffast-math -funroll-loops -O3 de-gfortran47: /sw/lib/gcc4.7/bin/gfortran -fplugin=/sw/lib/gcc4.7/lib/dragonegg.so -specs=/sw/lib/gcc4.7/lib/integrated-as.specs de-gfortran48:

On Tue, Apr 03, 2012 at 09:26:38AM +0200, Duncan Sands wrote: > Hi Jack, > >> Attached are the Polyhedron 2005 benchmark results for current llvm/dragonegg svn >> on x86_64-apple-darwin11 built against Xcode 4.3.2 and FSF gcc 4.6.3. > > thanks for the numbers. How does this compare to LLVM 3.0 - were there any > regressions? The results from just before

Hal, Johannes, Thanks for the feedback. I have been digging into this a little bit more and was able to have some of this metadata being generated. Nevertheless, I am confused about the semantics of this metadata. Let me explain: I was expecting the alias metadata to complement the information that alias analysis passes compute. However, it seems that the alias information of the pointers used

Hi all, In LLVM language reference I read that one can use noalias and alias.scope metadata to provide more detailed information about pointer aliasing. However, I was unable to obtain any LLVM IR annotations using this metadata from any LLVM optimization pass or Clang frontend (am I missing something?). If I understand it correctly, this information would complement the type-based alias

On Tue, 3 Apr 2012 08:57:51 -0400 Jack Howarth <howarth at bromo.med.uc.edu> wrote: > On Tue, Apr 03, 2012 at 09:26:38AM +0200, Duncan Sands wrote: > > Hi Jack, > > > >> Attached are the Polyhedron 2005 benchmark results for current > >> llvm/dragonegg svn on x86_64-apple-darwin11 built against Xcode > >> 4.3.2 and FSF gcc 4.6.3. > > >

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

I am getting this error on the clients. (I am running with --test). rerunning the command works as expected. This does not happen every time. I have no idea what the message means or more precisely what a PSON is... Russell -- You received this message because you are subscribed to the Google Groups "Puppet Users" group. To post to this group, send email to

On Wed, May 29, 2013 at 03:25:30PM +0200, Duncan Sands wrote: > Hi Jack, I pulled the loop vectorizer and fast math changes into the 3.3 branch, > so hopefully they will be part of 3.3 rc3 (and 3.3 final!). It would be great > if you could redo the benchmarks rc3. > Duncan, As requested, appended are the updated Polyhedron 2005 benchmark results with both RC1 and RC3 llvm 3.3

"It seems therefore that there is no other way than read in individually > 100 weather tables using read.tables., right? Using file.choose() doesn't change the work." Yes. With that many files, file.choose() does not make sense. However, I still do not understand what is the problem with using lapply() on the character vector of file names with read.table() as you did in your

Hello, I am trying to extract t and pvalues from a 1000 ttests using the by-function but everythinhg I tried did not work. Unfortunately googling "by" is not very helpful. Any help will be very appreciated. Cheers, Danile Stall *creating a data set library(MASS) dataset <- mvrnorm(160, mu, Sigma) dataset <- as.data.frame(dataset) dataset$GROUP <- rep(1:10, each=16) data.uni