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G'Day Tanya, Is it too late to bring in the following patches to fix some major brokenness in the AuroraUX tool chain for 2.6? http://llvm.org/viewvc/llvm-project/cfe/trunk/lib/Driver/Tools.cpp?r1=84468&r2=84469&view=diff&pathrev=84469 http://llvm.org/viewvc/llvm-project/cfe/trunk/lib/Driver/Tools.cpp?r1=84265&r2=84266&view=diff&pathrev=84266

Hi Tanya, > 1) Compile llvm from source and untar the llvm-test in the projects > directory (name it llvm-test or test-suite). Choose to use a > pre-compiled llvm-gcc or re-compile it yourself. I compiled llvm and llvm-gcc with separate objects directories. Platform is x86_64-linux-gnu. > 2) Run make check, report any failures (FAIL or unexpected pass). Note > that you need to

On Oct 20, 2009, at 6:02 AM, Duncan Sands wrote: > Hi Tanya, > >> 1) Compile llvm from source and untar the llvm-test in the projects >> directory (name it llvm-test or test-suite). Choose to use a pre- >> compiled llvm-gcc or re-compile it yourself. > > I compiled llvm and llvm-gcc with separate objects directories. > Platform is x86_64-linux-gnu. > Ok.

LLVMers, 2.6 pre-release2 is ready to be tested by the community. http://llvm.org/prereleases/2.6/ If you have time, I'd appreciate anyone who can help test the release. To test llvm-gcc: 1) Compile llvm from source and untar the llvm-test in the projects directory (name it llvm-test or test-suite). Choose to use a pre- compiled llvm-gcc or re-compile it yourself. 2) Run make check,

Hello, I have two data frames, X and Y, with two columns each and different numbers of rows. # creation of data frame X Loc1.alleles <- c(1,5,6,7,8) Loc1.Freq <- c(0.35, 0.15, 0.05, 0.10, 0.35) Loc1 <- cbind( Loc1.alleles,Loc1.Freq) X <- data.frame(Loc1) #creation of data frame Y Loc2.alleles <- c(1,4,6,8) Loc2.Freq <- c(0.35, 0.35,

Hello, I get > summary(E) level nodes ave_nodes time Min. : 1 Min. : 1.00 Min. : 10.71 Min. : 0.0000 1st Qu.: 237414 1st Qu.: 2.00 1st Qu.: 19.70 1st Qu.: 0.0100 Median : 749229 Median : 3.00 Median : 27.01 Median : 0.0100 Mean : 767902 Mean : 49.85 Mean : 98.89 Mean : 0.2296 3rd

Hi all R-users, I?m trying to assign colors on those p-value in my table output that fall above a certain critical value, let?s say a p-value >0.05. My table looks like this: Assets ADF-Level P-Value ADF-First D P-Value ADF-Second D P-Value [1,] Liabilities -2.3109 0.1988 -3.162 0.025 -6.0281

Here is a drop-in replacement for the R incomplete beta function. src/math/pbeta.c It is a slightly modified version of the cephes library one from Netlib. In the few cases I tried it seems to give at least 14 digit agreement with the one in S-PLUS (its hard to get more). I'm not sure what performance is like. I'd like to know if it helps with some of the problems which have been

Hi all, I found out that one of my perl scripts is heavily affected by the current bug. I was lazy to compile anything and I didn't want to mess up my system doing some experiments, so I tried to install ActivePerl as a temporary solution instead (RPMs are available). ActivePerl 5.8 is approximately 73x faster that CentOS version and ActivePerl 5.10 even slightly faster. Both versions and

Hi r-sers, I have a data of relative frequencies for the interval of 0-20, 20-40,...380-400.  I would like the two data on the same graph using the same x-axis label.  My question is how to get a smooth curve using kernel density code if it possible for this data.   > cbind(rel_obs,rel_gen)           rel_obs rel_gen  [1,] 0.000000000  0.0000  [2,] 0.092534175  0.0712  [3,] 0.105152471  0.1092

Building the current release 2.7 branch on x86_64-apple-darwin10 with r81455 reverted, I get the following Polyhedron 2005 benchmark results (with no test failures)... ================================================================================ Date & Time : 7 Apr 2010 22:24:16 Test Name : llvm_gfortran_lin_p4 Compile Command : llvm-gfortran -ffast-math -funroll-loops -msse3

Star Tan wrote: > I have found that the extremely expensive compile-time overhead comes from the string buffer operation for "INVALID" MACRO in the polly-detect pass. > Attached is a hack patch file that simply remove the string buffer operation. This patch file can significantly reduce compile-time overhead when compiling big source code. For example, for oggen*8.ll, the compile

f <- (structure(list(X = structure(96:97, .Label = c("119DAmm", "119DN", "119DNN", "119DO", "119DOC", "119Flow", "119Nit", "119ON", "119OPhos", "119OrgP", "119Phos", "119TKN", "119TOC", "148DAmm", "148DN", "148DNN", "148DO",

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

On Apr 7, 2010, at 8:41 PM, Jack Howarth wrote: > Building the current release 2.7 branch on x86_64-apple-darwin10 > with r81455 reverted, I get the following Polyhedron 2005 benchmark > results (with no test failures)... Very nice! A 14% speedup on a benchmark we don't tune for isn't bad. I imagine that there are several easy wins you could get on it if you were interested

Comparing the Polyhedron 2005 benchmark results for gfortran from llvm-gcc-4.2 of April 7th, 2010 and September 18th, 2010 (from the rc2 2.8 release branch), we seem to be regressing in performance for this release.... ================================================================================ Date & Time : 7 Apr 2010 22:24:16 Test Name : llvm_gfortran_lin_p4 Compile Command :

On Wed, Apr 07, 2010 at 09:54:36PM -0700, Chris Lattner wrote: > > On Apr 7, 2010, at 8:41 PM, Jack Howarth wrote: > > > Building the current release 2.7 branch on x86_64-apple-darwin10 > > with r81455 reverted, I get the following Polyhedron 2005 benchmark > > results (with no test failures)... > > Very nice! A 14% speedup on a benchmark we don't tune for

#Hello, #I have three data frames, X,Y and Z with two columns each and different numbers of rows. # creation of data frame X X.alleles <- c(1,5,6,7,8) X.Freq <- c(0.35, 0.15, 0.05 , 0.10, 0.35) Loc1 <- cbind( X.alleles,X.Freq) X <- data.frame(Loc1) #creation of data frame Y Y.alleles <- c(1,4,6,8) Y.Freq <- c(0.35, 0.35, 0.10, 0.20 )

Hi, i've been trying to apply the lme apprach to growth curves of children, but lme keeps running for ever and ever as soon as I use a reasonable basis. First Example: Data are 39 boys from the Berkeley growth study, each one measured 31 times at the ages of 1.00 1.25 1.50 1.75 2.00 3.00 4.00 5.00 6.00 7.00 8.00 8.50 9.00 9.50 10.00 10.50 11.00 11.50 12.00 12.50 13.00 13.50

On 02.10.2016 20:03, Ilia Mirkin wrote: > On Sun, Oct 2, 2016 at 1:58 PM, Tobias Klausmann > <tobias.johannes.klausmann at mni.thm.de> wrote: >> Previously we'd end up with an unnecessary mov for the thirs immediate value. >> >> total instructions in shared programs : 851881 -> 851864 (-0.00%) >> total gprs used in shared programs : 110295 -> 110295