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I tried to manually assign each of 3 arrays a unique TBAA node. But it does not seem to help: alias analysis still considers arrays as may-alias, which most likely prevents the desired optimization. Below is the sample code with TBAA metadata inserted. Could you please suggest what might be wrong with it? Many thanks, - D. marcusmae at M17xR4:~/forge/llvm$ opt -time-passes -enable-tbaa -tbaa

Dear all, Attached notunrolled.ll is a module containing reduction kernel. What I'm trying to do is to unroll it in such way, that partial reduction on unrolled iterations would be performed on register, and then stored to memory only once. Currently llvm's unroller together with all standard optimizations produce code, which stores value to memory after every unrolled iteration, which is

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

here is a snippet of data where I would like to drop all rows that have zeros across them, and keep the rest of the rows while maintaining the row names (1,2,3, ...10). The idea here is that a row of zeros is an indication that the row must be dropped. There will never be the case where there is a row(of n columns) with less than 5 zeros in this case(n zeros I am unsure how to manipulate the

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,

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

Hi, yeah in the long run showing both in an ordered manner would be a nice thing to have! That would include patching the output and the tgsi parser (who wants to delete half the output to parse it again e.g. with nouveau_compiler). I can image an output similar to the one below: IMM[5] FLT32 { 0.0000, 0.0000, 0.0000, 0.0000} ^ IMM[5] FLT32 {0x00000019, 0x0000000f, 0x00000005,

Printing without this could lead to the following output, while the values are not exactly zero: IMM[5] FLT32 { 0.0000, 0.0000, 0.0000, 0.0000} IMM[6] FLT32 { 0.0000, 0.0000, 0.0000, 0.0000} IMM[7] FLT32 { 0.0000, 0.0000, 0.0000, 0.0000} when printing the values as hex, we can now see the differences: IMM[5] FLT32 {0x00000019, 0x0000000f, 0x00000005,

Hello everyone, I am using *mlogit* to analyse my choice experiment data. I have *3 alternatives* for each individual and for each individual I have *9 questions*. I have a response from *516 individuals*. So it is a panel of 9*516 observations. I have arranged the data in long format (it contains 100 columns indicating different variables and identifiers). In mlogit I tried the following

Hi guys,   I have two data sets of prices: endprice0, endprice1   I use the Wilcox test:   wilcox.test(endprice0, endprice1, paired = TRUE, alternative = "two.sided",  conf.int = T, conf.level = 0.9)   The result is with V = 1819, p-value = 0.8812.   Then I calculated the z-value of the test: z-value = -2.661263. The corresponding p-value is: p-value = 0.003892, which is different from

Fellow R-helpers, Suppose we create a histogram as follows (although it could be any vector with zeroes in it): R> lenh <- hist(iris$Sepal.Length, br=seq(4, 8, 0.05)) R> lenh$counts [1] 0 0 0 0 0 1 0 3 0 1 0 4 0 2 0 5 0 6 0 10 0 9 0 4 0 [26] 1 0 6 0 7 0 6 0 8 0 7 0 3 0 6 0 6 0 4 0 9 0 7 0 5 [51] 0 2 0 8 0 3 0 4 0 1 0 1 0 3

Hi Elisa, I hope this is what you wanted. dat1<-read.csv("peaks.csv",sep=",") #Subset dat2<-dat1[1:5,] res1<-do.call(cbind,lapply(seq_len(nrow(dat2)),function(i) do.call(rbind,lapply(split(rbind(dat2[i,],dat2[-i,]),1:nrow(rbind(dat2[i,],dat2[-i,]))), function(x) {x1<-rbind(dat2[i,],x);

Dear All I new to using R and am struggling with some matrix multiplication. I have two matrices, one containing random numbers, these are multiplied together to get another matrix which is different each time. When I put in another for loop to repeat this process a multiple times the matrices are all the same. I?m sure there is a way to keep the randomness of the different matrices but I think

Hello; I was able to build and install llvm(3.0) under Ubuntu 11.10 (using the ./configure script found under llvm source, and then make and make install). While configuring, I gave --prefix as a directory where I would like llvm to be installed. I did not give --with-llvmgccdir and the --enable-optimized argument to configure. Because 3.0 doesn't come with llvmgcc source/binaries and I

Dear R user: I am studying the allele data of two populations. the following is the data: a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 pop1 0.0217 0.0000 0.0109 0.0435 0.0435 0.0000 0.0109 0.0543 0.1739 0.0761 0.1413 0.1522 0.1087 0.0870 0.0435 0.0217 0.0109 pop2 0.0213 0.0213 0.0000 0.0000 0.0000 0.0426 0.1702 0.2128 0.1596 0.1809 0.0957 0.0745 0.0106

Evan Cheng <evan.cheng <at> apple.com> writes: > Eli is right. We do need to see some benchmark numbers and understand how the pass will fit in the target > independent optimizer. While we encourage contribution, we typically don't commit new passes unless it > introduce new functionalities that have active clients. It would also help if you provide us with compile

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

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