similar to: multicore + xeon ?

Hi everyone! The fptoui/fptosi instructions are currently specified to return a poison value if the rounded-towards-zero floating point number cannot be represented by the target integer type. The motivation for this behavior is that overflowing float to int casts in C are undefined behavior. However, many newer languages prefer to have a float to integer cast that is well-defined for all input

I would be interested in learning what the set of used semantics for float-to-int conversion is. If the only two used are 1) undefined behavior if unrepresentable and 2) saturate to int_{min,max} with NaN going to zero, then I think it makes sense to expose both of those natively in the IR. If the set is much larger, I think separate intrinsics for each behavior would make sense. It would be nice

Hello, I want to call a function "fx" given by name, where some "global" variables (in the environment of fx) are passed to the function. For compatibility reasons I cannot modify the parameter list of fx and I want to avoid setting variables in the global environment (e.g. via <<-) Is there a way, how to do this? Thomas P. The example: fx <- function(y)

Hi, the function outer can not apply a constant function as in the last line of the following example: > xg <- 1:4 > yg <- 1:4 > fxyg <- outer(xg, yg, function(x,y) x*y) > fconstg <- outer(xg, yg, function(x,y) 1.0) Error in outer(xg, yg, function(x, y) 1) : dims [product 16] do not match the length of object [1] Of course there are simpler ways to construct a constant

> Or is this a bad idea? I don't like the proposal. I have seen code like the following (in fact, I have written such code, where I had forgotten a function was not vectorized) where the error would have been discovered much later if outer() didn't catch it. > outer(1:3, 11:13, sum) Error in outer(1:3, 11:13, sum) : dims [product 9] do not match the length of object [1]

Hi all, I am new to this LLVM. I went through the documenation of LLVM but I didn't find any support for Multicore. Is there any such possibility where multicore architecture can be exploited using LLVM. Thanks Vijay -------------- next part -------------- An HTML attachment was scrubbed... URL:

Sir, I want to apply mclapply() function for my analysis. So, I have to install multicore package. But I can not install the package. >install.packages("multicore") It gives that package multicore is not available. Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

Hi, I want to parallelize some computations when it's possible on multicore machines. Each computation produces a big objects that I don't want to store if not necessary: in the end only the object that best fits my data have to be returned. In non-parallel mode, a single gloabl object is updated if the current computation gets a better result than the best previously found. My plan

It is so difficult ... Which FE? It need BE support? I didn't get it. 2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the > back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at gmail.com> wrote: > >> Multicore? >> You

On Aug 3, 2010, at 8:48 PM, Liu wrote: > It is so difficult ... > Which FE? It need BE support? I didn't get it. > > 2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

This is a 'rather than re-invent the wheel' post. Has anyone out there re-written combn so that it can be parallelized - with multicore, snow, or otherwise? I have a job that requires large numbers of combinations, and rather than get all of the index values, then crank it through mclapply, I was wondering if there was a way to just do this natively within a function. Just curious.

dear r experts---Is there a multicore equivalent of by(), just like mclapply() is the multicore equivalent of lapply()? if not, is there a fast way to convert a data.table into a list based on a column that lapply and mclapply can consume? advice appreciated...as always. regards, /iaw ---- Ivo Welch (ivo.welch at gmail.com)

Hi, in R 2.12.1, R CMD check hangs when building a vignette that uses a foreach loop with the doMC parallel backend. This does not happen in R 2.13.1, nor if I use doSEQ instead of doMC. All versions of multicore, doMC and foreach are the same on both my R installations. Has anybody encountered a similar issue? Thank you. Renaud ### UNIVERSITY OF CAPE TOWN This e-mail is subject to the

Dear List, the advent of multicore machines in the consumer segment makes me wonder whether it would, at least in principle, be possible to divide a computational task into more slave R processes running on the different cores of the same processor, more or less in the way package SNOW would do on a cluster. I am thinking of simple 'embarassingly parallel' problems, just like inverting

The attached code produces corrupted pdfs (test2.pdf, test4.pdf and test5.pdf). The resulting pdf depends on how many cores are available on the machine. I don't see why there should be any difference between the pdfs (exept for the timestamp). Doing many operations involving mclapply can increase the size of the resulting pdf by ten times! Thank you for checking this. require(multicore)

Hi all, I have R installed on a box, which is running on a machine with 16 core and Redhat - Linux. I am handling huge (size of dataset will be 5 GB) dataset. Lets assume that my data is in the form of structured (multiple) logs. I access the data by using all.files(). Since by default basic version of R utilizes single core, the processing of my analysis code is taking too much time. I got to

Multicore? You want OpenMP support? 2010/8/3 vijay kumar <vijaygbvv at gmail.com> > Hi all, > I am new to this LLVM. I went through the documenation of LLVM but > I didn't find any support for Multicore. Is there any such possibility where > multicore architecture can be exploited using LLVM. > > > > Thanks > Vijay > >

Hi, i have a function: zz<- (constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) i can get the result by using command (for example): zz$par now if i can use multicore: zz<-parallel(constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) result < collect(zz) i cant get my the result: result$par because multicore add process id. for example: