similar to: Help: Where can I find the code for 'C_Cdqrls'?

Hi, Is there a way to passing llc options to clang, for example -march -mcpu, etc. ? Some threads suggested using -mllvm flag, I tried ./clang -mllvm -march=X86-64 -mcpu=core2 -o hello hello.c, but got "Unknown command line argument" Thanks, Ziqiang -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi, I'm working on a project that use llvm openrisc beckend (currently not part of the upstream). Right now I'm looking at a bug where llc generates memory instructions that has out-of-range displacement, for example l.sb 37668(r1), r2 in which 37668 is a 17 bit signed integer, but the instruction only allows 16 bit signed displacement. As a result, after running through the

Hi John Thanks for your suggestions, they all sound reasonable to me. The way I'm thinking right now is to write a MachineFuncionPass that iterate through each MachinBasicBlock, for each MBB, adds up the instructions counts of previous MBBs, that number multiply by 4 should be the offset of that MBB from its MachineFunction. In order to correctly count the instructions, this pass should be

This might fall under the purview of bundles, but I could not find any example bundles which demonstrated what I am after. I would like to construct two packages (A, B) which utilize a number of common C functions. The most straightforward way to do this is just copy the relevant .c and .h files from one src directory to the next, but this is tedious especially in the face of multiple

Hi, Is there a way to see what the default argument llc takes from clang/clang++ ? I'm debugging my backend with a very simple c++ program. Running through *clang++ -target myTarget -S simple.cpp -o simple.s * (bug does no show up) gives me different results from *clang++ -target myTarget -S -emit-llvm -o simple.cpp -o simple.ll* *llc simple.ll -o simple.s *(bug shows up) Just trying to

On Sat, Feb 28, 2015 at 7:39 AM, Ziqiang Patrick Huang < ziqiang.huang1001 at gmail.com> wrote: > Hi John > > Thanks for your suggestions, they all sound reasonable to me. The way I'm > thinking right now is to write a MachineFuncionPass that iterate through > each MachinBasicBlock, for each MBB, adds up the instructions counts of > previous MBBs, that number multiply

Hi, all. I want to write some functions like glm() so i studied it. In glm.fit(), it calls a fortran subroutine named "dqrfit" to compute least squares solutions to the system x * b = y To learn how "dqrfit" works, I just follow how glm() calls "dqrfit" by my own example, my codes are given below: > qr <- >

Hi, all Is there a way of getting the basic block offset from its parent function ? What I'm trying to do is to get an execution count of each basic blocks, so I need to know the starting address of each basic blocks. Obviously we can't get the absolute address before linking the program, but the offset relative to parent function should be available so I can take it and get the function

On 2/27/15 6:30 PM, Ziqiang Patrick Huang wrote: > Hi, all > > Is there a way of getting the basic block offset from its parent > function ? At the LLVM IR level, no. At the code generator layer (MachineFunctionPass layer or the MC layer), probably yes. > > What I'm trying to do is to get an execution count of each basic > blocks, so I need to know the starting

I'm trying to clone a MachineInstr in the AsmPrinter::EmitInstruction, Here is the code: void EsenciaAsmPrinter::EmitInstruction(const MachineInstr *MI) { const MachineFunction *MF = MI->getParent()->getParent(); MachineInstr *CloneMI = MF->CloneMachineInstr(MI); ... ... } The problem is that MF is a const and CloneMachineInstr expects a non-const. Does anybody know if there is

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Hi All: I'm new to R and have a few questions about getting R to run efficiently with large datasets. I'm running R on Windows XP with 1Gb ram (so about 600mb-700mb after the usual windows overhead). I have a dataset that has 4 million observations and about 20 variables. I want to run probit regressions on this data, but can't do this with more than about 500,000 observations before

Hi all, I¡¯m trying to build R (32-bit) under Windows 7 (64-bit). To do so, I just use ¡±make all recommended¡± as mentioned in the documents and it does work for R 2.15.2. However, for R 3.0.2, I get the following error message, gcc -shared -s -static-libgcc -o tools.dll tmp.def text.o init.o Rmd5.o md5.o signals.o install.o getfmts.o http.o

What kind of special magic does glm have? I'm working on a logistic regression solver (L-BFGS) in c and I've been using glm to check my results. I came across a data set that has a very high condition number (the data matrix transpose the data matrix) that when running my solver does not converge, but the same data set with glm was converging ( I love R :) ). I noticed that glm using

Dear R developers, Taking advantage of the yesterday discussion about the use of Depends/Import/Suggests/Enhances, I would like to add a related question. Let's assume, in the DESCRIPTION file of my package foo0, I have: Depends: foo1 Imports: foo2 while in the NAMESPACE file of my package I have importFrom("foo2", f2) and within my package I use the following two external

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Full_Name: Alexander Blocker Version: 2.7.1 OS: Ubuntu 8.04 / Windows XP Submission from: (NULL) (76.119.235.225) When running lm(modeleq, weights=wt, data=dataset) with small weights (<1e-10), I have encountered an odd phenomenon with fitted values. Due to numerical precision issues, the fitted values and residuals returned by lm.wfit (from its .Fortran call to dqrls) can differ greatly from

Consider the following missing data problem: y = c(1, 2, 2, 2, 3) a = factor(c(1, 1, 1, 2, 2)) b = factor(c(1, 2, 3, 1, 2)) fit = lm(y ~ a + b) anova(fit) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) a 1 0.83333 0.83333 1.3637e+33 < 2.2e-16 *** b 2 1.16667 0.58333 9.5461e+32 < 2.2e-16 *** Residuals 1 0.00000 0.00000 ---

Dear Sirs, I am trying to compile R v1.2.1 under Digital Unix 4.0D. The configure-script runs without any problems but during 'make' I receive the following error-message: [......] g77 -mieee -g -O2 -c dtrco.f -o dtrco.o g77 -mieee -g -O2 -c dtrsl.f -o dtrsl.o g77 -mieee -g -O2 -c eigen.f -o eigen.o g77 -mieee -g -O2 -c lminfl.f -o lminfl.o ar cr libappl.a Rsock.o approx.o bakslv.o

dear R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is