similar to: [LLVMdev] Perfect forwarding?

Hello, I have a class Person class Persoon < ActiveRecord::Base set_table_name "Persoon" set_primary_key "p_persoon" has_many :adres, :class_name => "Adres" composed_of :name, :class_name => Name, :mapping => [ [:naam,:naam], [:voornaam,:voornaam], [:voornaam2,:voornaam2], [:persnickname,:persnickname], [:perssortname,:perssortname] ] def

Hi, This is my first post, sorry if not in the right format. I am stuck at a place where I have a code fragment that runs in normal gcc but fails when I give -fplugin=dragonegg.so extern int *testfunc(); extern __typeof (testfunc) testfunc __asm__ ("" "__GI_testfunc") __attribute__ ((visibility ("hidden"))); extern __typeof(testfunc) __testfunc; extern __typeof

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

Hi all. I have a next problem. I need to implement some object that will work with both const and "constless" object: Instruction* and const Instruction*, for example. How to implement it better? Currently I see two possible ways: 1. Templates. template <class InstructionTy> class InstructionProcessorT { InstructionTy *Inst; InstructionProcessor(InstructionTy *I) : Inst(I) {}

Hi, I'm looking for a most efficient way to call an R function from C++ in a package. I know there are two functions (`R_forceAndCall` and `Rf_eval`) that can do the "call" part, but both are slow compared to calling the same function in R. I also try to use Rcpp and it is the worse one. Here is my test code: C++ code: ``` // [[Rcpp::export]] SEXP C_test1(SEXP f, SEXP x) { SEXP

Hello Group, I'm trying to learn R and am having a problem getting output from a function I'm trying to write. The problem is clearly one of scope, but I can't find the documentation that tells me how to get around the issue. Here is an example of my problem. testfunc<-function(x) { y<-10 print(y) print(x) } testfunc(4) The variables x and y are accessible during execution

First off, thanks for the help so far. >From what I have been able to tell, emitPrologue kicks in after the arguments for the function have been copied. For example, consider the function int testfunc( int foo, int bar ); Emitting assembly code from the llvm-gcc frontend in a small test program gives the following for the call to testfunc movl $1338, (%esp) movl $1339, 4(%esp) call

Hi, I think Chris recently did this with ArrayRef - that might be a good template to base your solution on? Cheers, James -----Original Message----- From: llvmdev-bounces at cs.uiuc.edu [mailto:llvmdev-bounces at cs.uiuc.edu] On Behalf Of Stepan Dyatkovskiy Sent: 16 February 2012 06:36 To: llvmdev at cs.uiuc.edu Subject: [LLVMdev] Question about "const" Hi all. I have a next

Blast! LLVM mailing server *still* has the headers broken... On Mon, Aug 31, 2009 at 11:03 PM, Talin<viridia at gmail.com> wrote: > OvermindDL1 wrote: >> >> BLAST! LLVM mailing list headers are still royally screwed up... >> My message is below... >> >> On Sun, Aug 30, 2009 at 2:20 PM, Talin<viridia at gmail.com> wrote: >> >>>

I noticed this oddity about [ and setMethod. First, I define testFunc, which sorts a data frame by the first column and returns the entries that aren't NAs, and testIt, which runs testFunc repeatedly on a random large data frame, each time saving the return into a dummy placeholder (for demonstration's sake). > require(methods) Loading required package: methods [1] TRUE > testFunc

I noticed this oddity about [ and setMethod. First, I define testFunc, which sorts a data frame by the first column and returns the entries that aren't NAs, and testIt, which runs testFunc repeatedly on a random large data frame, each time saving the return into a dummy placeholder (for demonstration's sake). > require(methods) Loading required package: methods [1] TRUE > testFunc

Hello, I'm trying to combine the parallel computing power available through NVIDIA CUDA (www.nvidia.com/cuda) from within R. CUDA is an extension to the C language, so I thought it would be possible to do this. If I have a C file with an empty function which includes a needed CUDA library (cutil.h) and compile this to an .so file using a NVIDIA compiler (nvcc), called 'myFunc.so' I

Hi! We have started to use deopt operand bundle to make our native stacktrace deoptimizable and garbage collectable. We stumbled upon an issue and we don't know if it is really an issue on our side or really a problem within LLVM. For example, for this input: declare { i8*, i8* } @getCode() define void @testFunc() { entry: %0 = call { i8*, i8* } @getCode() %1 = extractvalue { i8*, i8* }

Dear all, I'm looking for a function comparable to switch, to categorize a continuous variable in a few levels. Off course that can be done with a series of ifelse statements, but that looks rather clumsy. I looked at switch, but couldn't figure out how to use it for this. I guess that's not possible, as it only works with characters or integers, not with intervals. Basically,

Hi, I'm trying to follow the advice from this message: http://lists.cs.uiuc.edu/pipermail/llvmdev/2008-October/017574.html, to create a call instruction from a function pointer. But I'm getting... Assertion failed: (isa<X>(Val) && "cast<Ty>() argument of incompatible type!"), function cast, file /usr/local/include/llvm/Support/Casting.h, line 202.

For reference, your benchmark using UNWIND_PROTECT: > system.time(test(testFunc, evn$x)) user system elapsed 0.331 0.000 0.331 > system.time(test(C_test1, testFunc, evn$x)) user system elapsed 2.029 0.000 2.036 > system.time(test(C_test2, expr, evn)) user system elapsed 2.307 0.000 2.313 > system.time(test(C_test3, testFunc, evn$x)) user system

Hello! I have the problem that in my function the passed variable is not used, but the variable name of the dataframe itself?- difficult to explain, but an easy example: TestFunc<-function(df, group) { ??? print(names(subset(df, select=group))) } df1<-data.frame(group="G1", visit="V1", value=0.9) TestFunc(df1, c("group", "visit")) Result: [1]

Hello everyone, I bring to discussion the necessity/design of a new type of smart pointer. r215176 and r217791 rise the problem, D5443 <http://reviews.llvm.org/D5443> is devoted to the solution. r215176 applies several temporary ugly fixes of memory leaks in TGParser.cpp which would be great to be refactored using smart pointers. D5443 <http://reviews.llvm.org/D5443> demonstrates

Hi there R-helpers: I'm having problems with the function ode() found in the package deSolve. It seems that when my state variables are too numerous (>33000 elements), the function throws the following error: Error in vode(y, times, func, parms, ...) : cannot allocate memory block of size 137438953456.0 Gb In addition: Warning message: In vode(y, times, func, parms, ...) : NAs

On Jan 17, 2011, at 5:01 PM, Rob Nikander wrote: > Hi, > > I'm trying to follow the advice from this message: > http://lists.cs.uiuc.edu/pipermail/llvmdev/2008-October/017574.html, > to create a call instruction from a function pointer. But I'm > getting... > > Assertion failed: (isa<X>(Val) && "cast<Ty>() argument of > incompatible