search for: 0.0010

Did something change with exception handling recently? A bunch of lit bots are showing slower compile times for many tests. Ciao, Duncan. On 20/06/12 07:53, llvm-testresults at cs.uiuc.edu wrote: > > lab-mini-03__O0-g__clang_DEV__x86_64 test results > <http://llvm.org/perf/db_default/v4/nts/1283?compare_to=1278&baseline=999> > > Run Order Start Time Duration >

Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? -bw On Jun 20, 2012, at 12:38 AM, Duncan Sands <baldrick at free.fr> wrote: > Did something change with exception handling recently? A bunch of lit bots are > showing slower compile times for many tests. > > Ciao, Duncan. > > On 20/06/12 07:53, llvm-testresults at cs.uiuc.edu

Hi Bill, > Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? I don't see any relevant LLVM changes, so I guess clang C++ compilation slowed down due to some clang changes. I'm not going to investigate this. Ciao, Duncan. > > -bw > > On Jun 20, 2012, at 12:38 AM, Duncan Sands <baldrick at free.fr> wrote: > >> Did

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

On Jul 5, 2012, at 1:33 AM, Duncan Sands wrote: > Hi Bill, > >> Nothing that I'm aware of has changed with EH. Is it possible to bisect the problem? > > I don't see any relevant LLVM changes, so I guess clang C++ compilation slowed > down due to some clang changes. I'm not going to investigate this. > Crumbs. John, Do you know of anything that went into

Here is some timing information from running the Zig standard library tests: $ ./zig test ../std/index.zig --enable-timing-info Name Start End Duration Percent Initialize 0.0000 0.0010 0.0010 0.0001 Semantic Analysis 0.0010 0.9968 0.9958 0.1192 Code Generation 0.9968 1.4000 0.4032

Thanks, that was a really helpful suggestion. If you're curious- here are some of the high cost areas: ===-------------------------------------------------------------------------=== DWARF Emission ===-------------------------------------------------------------------------=== Total Execution Time: 2.0117 seconds (2.0185 wall clock) ---User Time---

I have a file for which clang-7 takes over 2 hours to compile with -O3. For the same file, clang-5 takes less than 2 minutes (which is also high IMHO). I will try to create a test case (but it is pretty simple, it only contains initializations of many arrays of structs where the structs are of the following form: struct Foo { EnumType1 e1; // there are 700+ enum labels std::string s1;

Hi, I have a transition matrix T for which I want to find the steady state matrix for. This could be approximated by taking T^n , for large n. T= [ 0.8797 0.0382 0.0527 0.0008 0.0212 0.8002 0.0041 0.0143 0.0981 0.0273 0.8802 0.0527 0.0010 0.1343 0.0630 0.9322] According to a text book I have T^200 should have reached the steady state L L

LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When

Hello all, I'll level with you: I'm puzzled! How is it that this constrained regression routine using -pcls- runs satisfactorily (courtesy of Tian Zheng): library(mgcv) options(digits=3) x.1=rnorm(100, 0, 1) x.2=rnorm(100, 0, 1) x.3=rnorm(100, 0, 1) x.4=rnorm(100, 0, 1) y=1+0.5*x.1-0.2*x.2+0.3*x.3+0.1*x.4+rnorm(100, 0, 0.01) x.mat=cbind(rep(1, length(y)), x.1, x.2, x.3, x.4)

Dear R-helpers, Happy New Year to all the helpful members of the list. Here is the behavior I'm looking for: > v1 <- c("a","b","c") > n1 <- c(0, 1, 2) > v2 <- c("c", "a", "b") > n2 <- c(0, 1 , 2) > (f1 <- data.frame(v1, n1)) v1 n1 1 a 0 2 b 1 3 c 2 > (f2 <- data.frame(v2, n2))

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

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",

Hi Bob, are these performance regressions real? They look pretty serious. Ciao, Duncan. On 10/12/11 09:40, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/332/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time >

For those who are interested, I've run 3 EAQUAL tests on both the LAME and OGG encoders: LAME with the --alt-standard preset (with and without --nspsytune) and OGGENC (RC3) with -q 6 (my personal "sweet spot") I am somewhat unsure though about the validity of these tests, since the original and decoded files were not the same size (2 KB difference). The LAME encoded files were also

Hi, Can anyone explain how the standard error in arima() is calculated? Also, how can I extract it from the Arima object? I don't see it in there. > x <- rnorm(1000) > a <- arima(x, order = c(4, 0, 0)) > a Call: arima(x = x, order = c(4, 0, 0)) Coefficients: ar1 ar2 ar3 ar4 intercept -0.0451 0.0448 0.0139 -0.0688 0.0010 s.e.

Yes, they are real. I re-ran the two tests with the biggest execution time regressions, and the results were completely reproducible. On Oct 12, 2011, at 1:24 AM, Duncan Sands wrote: > Hi Bob, are these performance regressions real? They look pretty serious. > > Ciao, Duncan. > > On 10/12/11 09:40, llvm-testresults at cs.uiuc.edu wrote: >> >>

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

Most of libc++ doesn't have explicit template instantiations, which leads to a pretty significant build time and code size cost when using libc++, since a large number of common templates will be emitted by the compiler and coalesced by the linker. Notably, in include/__config, we have: #ifndef _LIBCPP_EXTERN_TEMPLATE #define _LIBCPP_EXTERN_TEMPLATE(...) #endif whereas before