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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

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

On 09/09/2013 05:18 AM, Star Tan wrote: > > At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: > >> On 09/08/2013 08:03 PM, Star Tan wrote: >> Also, I wonder if your runs include the dependence analysis. If this is >> the case, the numbers are very good. Otherwise, 30% overhead seems still >> to be a little bit much. > I think

I have a fairly large set of data with the following attributes: >str(raw.data) `data.frame': 1429 obs. of 16 variables: $ TStamp :`POSIXlt', format: chr "2001-11-25 02:00:00" "2001-11-25 01:55:00" "2001-11-25 01:50:00" "2001-11-25 01:45:00" ... $ iPDT.AHU14.14: num 0.0122 0.0125 0.0120 0.0120 0.0122 ... $ iPDT.AHU14.15: num 0.0121

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

At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: >On 09/08/2013 08:03 PM, Star Tan wrote: >> Hello all, >> >> >> I have done some basic experiments about Polly canonicalization passes and I found the SCEV canonicalization has significant impact on both compile-time and execution-time performance. > >Interesting. > >>

At 2013-09-09 13:07:07,"Tobias Grosser" <tobias at grosser.es> wrote: >On 09/09/2013 05:18 AM, Star Tan wrote: >> >> At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: >> >>> On 09/08/2013 08:03 PM, Star Tan wrote: >>> Also, I wonder if your runs include the dependence analysis. If this is >>> the

Hello all, I have evaluated the compile-time and execution-time performance of Polly canonicalization passes. Details can be referred to http://188.40.87.11:8000/db_default/v4/nts/recent_activity. There are four runs: pollyBasic (run 45): clang -O3 -Xclang -load -Xclang LLVMPolly.so pollyNoGenSCEV (run 44): clang -O3 -Xclang -load -Xclang LLVMPolly.so -mllvm -polly -mllvm -polly-codegen-scev

I'm a little further along now. I've started to put together a simple driver for Elsa and LLVM that I'm calling "ellsif" (cute name, I think it works). The file being compiled is a "printf" function. Here are timing results for optimized and unoptimized runs: [~/elsa/ellsif] dev% ./ellsif -v test/ofmt.i -time-actions Adding test/ofmt.i as a preprocessed C file

Now, we come to more evaluations on http://188.40.87.11:8000/db_default/v4/nts/recent_activity I mainly care about the compile-time and execution time impact for the following cases: pBasic (run 45): clang -O3 -load LLVMPolly.so pNoGenSCEV (run 44): clang -O3 -load LLVMPolly.so -polly-codegen-scev -polly -polly-optimizer=none -polly-code-generator=none pNoGenSCEV_nocan (run 47): same option

HI I use glm in R to do logistic regression. and treat both response and predictor as factor In my first try: ******************************************************************************* Call: glm(formula = as.factor(diagnostic) ~ as.factor(7161521) + as.factor(2281517), family = binomial()) Deviance Residuals: Min 1Q Median 3Q Max -1.5370 -1.0431 -0.9416 1.3065 1.4331 Coefficients:

On 09/08/2013 08:03 PM, Star Tan wrote: > Hello all, > > > I have done some basic experiments about Polly canonicalization passes and I found the SCEV canonicalization has significant impact on both compile-time and execution-time performance. Interesting. > Detailed results for SCEV and default canonicalization can be viewed on: http://188.40.87.11:8000/db_default/v4/nts/32 (or

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 all, I have done some basic experiments about Polly canonicalization passes and I found the SCEV canonicalization has significant impact on both compile-time and execution-time performance. Detailed results for SCEV and default canonicalization can be viewed on: http://188.40.87.11:8000/db_default/v4/nts/32 (or 33, 34) *pNoGen with SCEV canonicalization (run 32): -O3 -Xclang -load

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 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

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 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

Dear all, I am trying to transform polynomial coefficients from orthogonal form to the standard power basis. There's poly.transform in S-plus. Does anybody know how to do that in R ? I've found question about that in the archives of R-help but no real answer. Example : I'm doing polynomial regression of percentage of one insect in a community on altitude, precipitations,