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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,

I just upgraded our optimizer to LLVM 3.0 from 2.8 and noticed that the scalar replacement of aggregates pass takes a lot longer for some code. Has there been a performance regression in this pass, or does it do more work? LLVM 3.0: Total Execution Time: 1.0600 seconds (1.0526 wall clock) ---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name --- 0.5100

Hi, LLVM 2.1-pre1 test results: Linux (SUSE) on x86 (P4) Release mode, but with assertions enabled LLVM srcdir == objdir # of expected passes 2250 # of expected failures 5 I ran the llvm-test suite on my desktop while I was also working on that PC, so don't put too much trust in the timing info. Especially during the "spiff" test the machine was swapping

On 28 June 2013 19:45, Chris Matthews <chris.matthews at apple.com> wrote: > Given this tradeoff I think we want to tend towards false positives (over > false negatives) strictly as a matter of compiler quality. > False hits are not binary, but (at least) two-dimensional. You can't say it's better to have any amount of false positives than any amount of false negatives

>> GCC (4.5.2, Windows build from CodeSourcery) - With -O0: 110ms, with -O2: 215ms >> Clang/LLVM (Release mode, LLVM git hash 7f5714f4..., clang git hash >> 9d9cf5...) - With -O0: 110ms, with -O2: 640ms Hi Matt, I only see 2x slowdown on my machine (consistently, O2 and O3), but that's still bad. If you compile to IR then pass "opt -time-passes" you can get a

I should describe the cost of false negatives and false positives, since I think it matters for how this problem is approached. False negatives mean we miss a real regression --- we don’t want that. False positives mean somebody has to spend some time looking at and reproducing the regression when there is not one --- bad too. Given this tradeoff I think we want to tend towards false positives

On 06/28/2013 01:19 PM, Renato Golin wrote: > On 28 June 2013 19:45, Chris Matthews <chris.matthews at apple.com> > wrote: > >> Given this tradeoff I think we want to tend towards false positives >> (over false negatives) strictly as a matter of compiler quality. >> > > False hits are not binary, but (at least) two-dimensional. You can't > say it's

LLVMers, The 2.5 pre-release is available for testing: http://llvm.org/prereleases/2.5/ If you have time, I'd appreciate anyone who can help test the release. Please do the following: 1) Download/compile llvm source, and either compile llvm-gcc source or use llvm-gcc binary (please compile llvm-gcc with fortran if you can). 2) Run make check, send me the testrun.log 3) Run "make

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,

Please help: I am using qplot as below and want to specify a different color scheme for race but dont know how, can someone show me. Thanks in advance Code and input file below: library(ggplot2) library(gridExtra) d<-read.table("results", header=TRUE, fill=TRUE) plot2<-qplot(X,Y,data=d,color=race,facets=TYPE~., xlab="X", ylab="Y") + theme_bw()

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

LLVMers, The 2.1 pre-release (version 1) is available for testing: http://llvm.org/prereleases/2.1/version1/ I'm looking for members of the LLVM community to test the 2.1 release. There are 2 ways you can help: 1) Download llvm-2.1, llvm-test-2.1, and the appropriate llvm-gcc4.0 binary. Run "make check" and the full llvm-test suite (make TEST=nightly report). 2) Download

All, I'm having problems w/ a simple attempt to subset an xyplot. The first plot below is a plot of y versus x for certain values of a third categorical variable z. Now I'd like to further restrict this to certain values of variable y. Neither of the two attempts below work. Any suggestions much appreciated. (note: I don't want to merely use ylim since I have a loess plot and I

All, How does one replace plot symbols with say subject IDs when using xyplot? Or superimpose them next to plot symbols? I searched the archives under various key words but haven't had much. Any suggestions or links much appreciated. Sample code below. David junk.frm = data.frame(ID = rep(1:16, each = 2), x, y, z = rep(c("D", "P"), 16)) y = c( 0.4, 0.6, -0.1,

Thanks, Matt. This is great information. Sounds like Chandler is looking into the details of what's going on. -Jim On Jul 19, 2012, at 3:55 PM, Matt Fischer <mattfischer84 at gmail.com> wrote: > I've been doing some profiling of LLVM on our codebase, to see how it > stacks up to the existing GCC build that we do. The primary thing I'm > focusing on at the moment is

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

I've been doing some profiling of LLVM on our codebase, to see how it stacks up to the existing GCC build that we do. The primary thing I'm focusing on at the moment is build speed, and in this regard LLVM seems to be pretty all over the map. On some files it seems to go quite a bit faster than GCC, and on others it's slower, leading to an aggregate build time for our repository