search for: final1

Hi, final<-data.frame() ?? for (m1 in 4:10) { ?????? for (n1 in 4:10){? ?????????? for (x1 in 0: m1) { ????????????? for (y1 in 0: n1) { final<- rbind(final,c(m1,n1,x1,y1)) res}}}} ?final1<-within(final,{flag<-ifelse(x1/m1>y1/n1, 1,0)}) ?head(final1) #? m1 n1 x1 y1 flag #1? 4? 4? 0? 0??? 0 #2? 4? 4? 0? 1??? 0 #3? 4? 4? 0? 2??? 0 #4? 4? 4? 0? 3??? 0 #5? 4? 4? 0? 4??? 0 #6? 4? 4? 1? 0??? 1 Also, just by looking at your code, you have "flag" and "flap". A...

so, thanx to all.... now I understand my fault and now I have another question: how I can write those code with using API: %final = alloca [256 x i8], align 16 ; <[256 x i8]*> [#uses=1] %final1 = getelementptr [256 x i8]* %final, i32 0, i32 0 ; <i8*> [#uses=2] call void @llvm.memcpy.i32( i8* %final1, i8* getelementptr ([3 x i8]* @.str, i32 0, i32 0), i32 3, i32 1 ) %tmp5 = call i8* bitcast (i8* (i8*, i8*)* @strcat to i8* (i8* noalias , i8* noalias )*)(...

Zalunin Pavel wrote: > hm.... I think, that is valid in c [snip] > I tried decompile code: > main(int argc, char **argv) { > char str1[] = "mother "; > strcat(str1, "father"); > return 0; > } > This is valid C but you forget that str1 is not magically expanded by strcat. It starts out as, and remains a char array with 8 elements. >

...:20,4),decreasing=F) year3<-rep(2001:2004,5) industry3<-rep(40,20) X<-rnorm(20) Y<-rnorm(20) Z<-rnorm(20) data3<-data.frame(firm3,year3,industry3,X,Y,Z) data3 colnames(data3)<-c("firm","year","industry","X","Y","Z") final1<-rbind(data1,data2) final2<-rbind(final1,data3) final2 final3<-final2[order(final2$industry,final2$year),] final3 I need to estimate a linear model Y = b0 + b1X + b2Z by industry and year, to obtain the estimates of b0, b1 and b2 by industry and year (for example I need to have de b0 fo...

Dear R community, I am working with huge arrays, so I spend a lot of time computing. This is my code: for (x in 1:dim(variable)[1]){ for (y in 1:dim(variable)[2]){ for (z in 1:dim(variable)[3]){ result <- max(variable[x,y,z,]) } } } Is there a more efficient procedure to do this task? Thanks in advance! [[alternative HTML version deleted]]

So i have a simple function: bmass=function(y){ weight=y$WT*y$MSTR return(bio) } And want to apply to a whole bunch of rows in my data.frame: final1=apply(final,1,yldbu) BUT...recieve the following error: "Error in y$WT : $ operator is invalid for atomic vectors" However when i try: > final[1,]$WT*final[1,]$MSTR [1] 156.3 It gives me the correct answer....what is apply not liking in my code? Thanks -- View this message in...

hm.... I think, that is valid in c but next code too doesn't works right: ; ModuleID = 'sample.lz' @.str1 = internal global [6 x i8] c"world\00" ; <[6 x i8]*> [#uses=1] @.str2 = internal global [7 x i8] c"hello \00" ; <[7 x i8]*> [#uses=1] @.str7 = internal global [7 x i8] c"father\00" ; <[7 x i8]*> [#uses=1]

...on3<-c(23456,1181,32489,2345,6754,8976,3245,1234,1288,1200,2345,2765,389,23456,2367,3892,6438,24824, 23,2897) data3<-data.frame(firm3,year3,industry3,dummy3,dimension3) data3 colnames(data3)<-c("firm","year","industry","dummy","dimension") final1<-rbind(data1,data2) final2<-rbind(final1,data3) final2 final3<-final2[order(final2$year,final2$industry,final2$dimension),] final3 Thank you very much, Cecília Carmo Universidade de Aveiro - Portugal [[alternative HTML version deleted]]

Michael Spencer via llvm-dev <llvm-dev at lists.llvm.org> writes: > I've recently implemented profile guided section layout in llvm + lld using > the Call-Chain Clustering (C³) heuristic from > https://research.fb.com/wp-content/uploads/2017/01/cgo2017-hfsort-final1.pdf > . In the programs I've tested it on I've gotten from 0% to 5% performance > improvement over standard PGO with zero cases of slowdowns and up to 15% > reduction in ITLB misses. > > > There are three parts to this implementation. > > The first is a new llvm pass...

I've recently implemented profile guided section layout in llvm + lld using the Call-Chain Clustering (C³) heuristic from https://research.fb.com/wp-content/uploads/2017/01/cgo2017-hfsort-final1.pdf . In the programs I've tested it on I've gotten from 0% to 5% performance improvement over standard PGO with zero cases of slowdowns and up to 15% reduction in ITLB misses. There are three parts to this implementation. The first is a new llvm pass which uses branch frequency info to...

...affic on the net this does not happen. It seens to me that this must be a bug, because I dont think that nmbd shut shut down only because of no traffic. Also this behaviour does not happen in Samba 2.2.x - only since I upgraded to Samba 3.0.0 I am currently using Debian/testing Package Samba-3.0.0-final1 Can anybody help, or perhaps a Samab Developer clarify the situation ? I habe a cron job - restarting Samba every 60 minutes, but I think this would not be the best solution :-/ Thanks in advance, Bernd

Hi, I would like to learn the details regarding what exactly PGO does for basic block layout/code placement optimizations in llvm. Could you please point me to some descriptions? Is it close to this paper (Karl Pettis and Robert C. Hansen. 1990. Profile guided code positioning. PLDI'90) http://perso.ensta-paristech.fr/~bmonsuez/Cours/B6-4/Articles/papers15.pdf? Whether it is purely

Hi Rafael, On 07/31/2017 04:20 PM, Rafael Avila de Espindola via llvm-dev wrote: > However, do we need to start with instrumentation? The original paper > uses sampling with good results and current intel cpus can record every > branch in a program. > > I would propose starting with just an lld patch that reads the call > graph from a file. The format would be very similar to

...+// Sort sections by the profile data provided in the .note.llvm.callgraph +// sections. +// +// This algorithm is based on Call-Chain Clustering from: +// Optimizing Function Placement for Large-Scale Data-Center Applications +// https://research.fb.com/wp-content/uploads/2017/01/cgo2017-hfsort-final1.pdf +// +// This first builds a call graph based on the profile data then iteratively +// merges the hottest call edges as long as it would not create a cluster larger +// than the page size. All clusters are then sorted by a density metric to +// further improve locality. +template <class ELFT&...

...+// Sort sections by the profile data provided in the .note.llvm.callgraph +// sections. +// +// This algorithm is based on Call-Chain Clustering from: +// Optimizing Function Placement for Large-Scale Data-Center Applications +// https://research.fb.com/wp-content/uploads/2017/01/cgo2017-hfsort-final1.pdf +// +// This first builds a call graph based on the profile data then iteratively +// merges the hottest call edges as long as it would not create a cluster larger +// than the page size. All clusters are then sorted by a density metric to +// further improve locality. +template <class ELFT&...