similar to: [LLVMdev] alias analysis results

Hi Eli, thanks for the answers helping out. I tried to understand further - got another example: void test() { int *jj, *kk; int aa = 100; jj = &aa; *jj = 300; kk = jj; *kk = 400; } int main() { test(); return 0; } bc looks like the following (only test() part) define void @test() nounwind { entry: %aa = alloca i32 ; <i32*> [#uses=2] %kk =

On Thu, May 14, 2009 at 2:19 PM, Weihua Sheng <weihua.sheng at gmail.com> wrote: > I actullay would expect the more accurate results from applying > anders-aa, but I could not interpret what has been returned - at least > I should see something like jj->aa, right? I'm not quite following... jj and aa don't alias; both versions show that. The results returned for

to compile it to bitcode I give the following command : llvm-gcc -emit-llvm -c -o s.bc s.c and then I run different alias analysis passes like -anders-aa, -basicaa using following: opt -anders-aa -aa-eval -print-all-alias-modref-info s.bc From this I get the following output: Function: main: 8 pointers, 1 call sites NoAlias: i32* %retval, i32** %j NoAlias: i32* %retval, i32**

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w

I have two more or less parallel dataframes that are giving me different results on one subset of variables. I know that I assembled the 2 dataframes slightly differently but I don't see why I am getting this result because one set of variables are labelled and the other is not. Variable names are the same, etc. as far as I can acertain. The only diffference seems to be that bdata variables

Hi, batch is a vector of lines returned by readLines from a NL-line-terminated file, here is the relevant section: ========================================================= AA BB CC DD EE FF GG H H JJ KK LL MM ========================================================= as you can see, a line is corrupt; two CRLF's are inserted. This is okay, I drop the bad lines, at least I hope I do:

Sorry, I misunderstood the question. The difference between a load and a read-only call is that load can be used as the value of the memory location. E.g. DeadStoreElimination pass removes a store that stores a just loaded value back into the same location. To do this it checks if the stored value is the value of load. Read-only call cannot be used like this. This being said, I don't know if

Hello, If I have a dataframe: example(data.frame) zz<-c("aa_bb","bb_cc","cc_dd","dd_ee","ee_ff","ff_gg","gg_hh","ii_jj","jj_kk","kk_ll") ddd <- cbind(dd, group = zz) and I want to divide the column named group by the "_", how would I do this? so instead of the first row being x

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

OK, with the restrict type qualifier, it is a little bit better: The IR's function signature becomes: define void @foo(i32* noalias %a, i32* noalias %b, i32* noalias %c) nounwind { Now the AA result: Function: foo: 13 pointers, 0 call sites NoAlias: i32* %a, i32* %b NoAlias: i32* %a, i32* %c NoAlias: i32* %b, i32* %c NoAlias: i32* %a, i32** %a_addr NoAlias:

On Fri, Sep 21, 2012 at 3:08 PM, Welson Sun <welson.sun at gmail.com> wrote: > OK, with the restrict type qualifier, it is a little bit better: > > The IR's function signature becomes: > define void @foo(i32* noalias %a, i32* noalias %b, i32* noalias %c) nounwind > { > > Now the AA result: > Function: foo: 13 pointers, 0 call sites > NoAlias: i32* %a,

Here is the result of running mem2reg then basicaa, it is even worse: (%a should be alias to %0, and partial alias to %3) opt -mem2reg -basicaa -aa-eval -print-all-alias-modref-info < foo.s > /dev/null Function: foo: 6 pointers, 0 call sites NoAlias: i32* %a, i32* %b NoAlias: i32* %a, i32* %c NoAlias: i32* %b, i32* %c PartialAlias: i32* %1, i32* %a NoAlias:

I have a data set like this in one .txt file (cols separated by !): APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! it contains over 14 000 000 records. Now

Hi all, I think I've managed to trick the legacy MemCpyOpt (MCO) into an incorrect transform, but I would like to confirm the validity of my counterexample before working on the fix. Suppose the following IR: %T = type { i32, i32 } define void @f(%T* %a, %T* %b, %T* %c, %T* %d) { %val = load %T, %T* %a, !alias.scope !{!10} ; store1 ; Aliases the load

Dear all, I am stuck at applying loop function for creating separated plots. I have coding like below: dataset.table <- table(data.frame(var1=c(1,2,3,1,2,3,1),colour=c("a","b","c","c","a","b","b") )) kk = function(f) { ls=as.character(f) pie(dataset.table[ls,],main=ls)

What I mean is if I have several clients performing backups at the same time what is the level of atomicity? I ask because of this ... take a look at the following lines from the log file: This is from one client operating. 2006/03/12 22:38:26 [11627] rsync to dlochart/backups/Sunday from cygnus-x1 (127.0.0.1) 2006/03/12 22:38:26 [11627] ./ 2006/03/12 22:38:26 [11627] wrote 28 bytes read 147

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

Hello, how to merge a data.frame and a matrix by one column in the data.frame and rownames of the matrix? df <- data.frame(col1=c("kk","yy","kk"),col2=c(6,4,3)) > df   col1 col2 1   kk    6 2   yy    4 3   kk    3 m<-matrix(c(3,8,56,9), nrow=2, dimnames = list(c("aa","kk"),c("col1","col2"))) > m    col1 col2 aa   

Dear all, I am dealing with the following (apparently simple problem): For some reasons I am interested in passing variables from a dataframe to a specific environment, and in fitting a standard glm: dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) KK<-new.env() for(i in 1:ncol(dati)) assign(names(dati[i]),dati[[i]],envir=KK) #Now the following two lines work correctly:

Dear folks? Is there a function to correctly find (and count) the NAs in a factor when exclude=NULL, regardless of whether their origin is in the original data or by subsequent assignment? In example number 1 below, where NAs are assigned by is.na()<-, testing the factor with is.na() finds the correct number of NAs. In example number 2, where the NAs are from the data, neither is.na(), ==NA,