similar to: Compile error with gfortran-4.6

Dears, I am writing a code in Fortran using OpenMP directives. When compiling the code in gfortran 4.4 does not occur any problem. When compiling the code in gfortran 4.6, an error message appears. In other compilers the error does not occur. A small example. ## Code in Fortran        subroutine hello()        implicit none        integer :: nthreads, tid        integer ::

Hi, I am using LLVM 3.6.1 to test the following code: #include <omp.h> #include <stdio.h> #include <stdlib.h> int main() { int tid, nthreads; #pragma omp parallel sections private(nthreads, tid) { #pragma omp section { tid = omp_get_thread_num(); printf("Hello, tid = %d\n ", tid); } #pragma omp section { tid = omp_get_thread_num();

Hola a todos: Necesito saber si alguien tiene alguna idea de un paquete con múltiples funciones (cálculos) para determinar el tamaño de la muestra (particularmente de estudios de psicología y ciencias sociales). De antemano agradezco la atención. Saludo desde Brasil *Guilherme Amorim Homem de Abreu Loureiro* Agronomist Engineer *CREA-BA: 051511013-2* Master of Science in Crop Production, Soil

Can you send the IR you are using? Volkan On Thu, Oct 8, 2015 at 6:28 AM Erdem Derebaşoğlu < erdemderebasoglu at hotmail.com> wrote: > Thanks. I enabled my pass. I have one resolved issue though: > MachineMemOperand::getAddrSpace() always returns zero. How can I use it to > distinguish private memory accesses? > > Erdem > > ------------------------------ > From:

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Hi everybody! Could somebody help me with the following? I'm trying to run a simple Hello World code in openmp using .C function. The C code i have is: #include <omp.h> #include <stdio.h> #include <R.h> void hello_omp(int *n) { int th_id, nthreads; omp_set_num_threads(*n); #pragma omp parallel private(th_id) { th_id = omp_get_thread_num();

Hi John, Thank you for the comments. I am addressing some of them bellow. Regards, Samuel 2014-08-11 9:36 GMT-04:00 John Leidel (jleidel) <jleidel at micron.com>: > Sergey [et.al], thanks for putting this proposal together. Overall, this > looks like a pretty solid approach to providing relatively hardware > agnostic omp target functionality. I had several comments/questions

We recently posted randomForestSRC on CRAN. It uses OpenMP in the native code extensively. We set the default number of cores to two (2), but we typically run it at the maximum (omp_get_max_threads()) during analysis. Currently, users need to set options(), set an environment variable, or edit their .Rprofile to use more than two cores. We followed the protocol for mc.cores in mclapply() in

Hi Sanjoy, Yes, that's exactly what we have been looking at recently here, but the region tags seem to make it possible to express the control flow as well, so I think we could start with reg ions+metadata, as Hal and Xinmin proposed, and then figure out what needs to be first class instructions. --Vikram Adve > On Jan 19, 2017, at 11:03 PM, Sanjoy Das <sanjoy at

> On Jan 20, 2017, at 10:44 AM, Tian, Xinmin via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > Sanjoy, the IR would be like something below. It is ok to hoist alloca instruction outside the region. There are some small changes in optimizer to understand region-annotation intrinsic. > > { void main() { > i32* val = alloca i32 > tok =

> On Jan 20, 2017, at 11:17 AM, Tian, Xinmin <xinmin.tian at intel.com> wrote: > >>>>> This means that the optimizer has to be aware of it, I’m missing the magic here? > > This is one option. > > The another option is that, as I mentioned in our LLVM-HPC paper in our implementation. We have a "prepare phase for pre-privatization" can be invoked

Hello everybody! I would like to present a proposal for implementation of OpenMP offloading in LLVM. It was created by a list of authors and covers the runtime part at most and at a very high level. I believe it will be good to have input from community at this early stage before moving deeper in details. The driver part is intentionally not touched, since we have no clear vision on how one can

> On Jan 31, 2017, at 5:38 PM, Tian, Xinmin <xinmin.tian at intel.com> wrote: > >>>>> Ok, but this looks like a “workaround" for your specific use-case, I don’t see how it can scale as a model-agnostic and general-purpose region semantic. > > I would say it is a design trade-off. I’m not sure if we’re talking about the same thing here: my understanding at

>>>>Ok, but this looks like a “workaround" for your specific use-case, I don’t see how it can scale as a model-agnostic and general-purpose region semantic. I would say it is a design trade-off. Regardless it is a new instruction or an intrinsics with token/tag, it will consist of model-agnostic part and model-non-agnostic part. The package comes with a framework for parsing

Hi, I'm going to club together some responses. I agree that outlining function sub-bodies and passing in the function pointers to said outlined bodies to OpenMP helpers lets us correctly implement the semantics we need. However, unless I severely misunderstood the thread, I thought the key idea was to move *away* from that representation and towards a representation that _allows_

Let me try this. You can simply consider the prepare-phase (e.g. pre-privatization) were done in FE (actually a library can be used by multiple FEs at LLVM IR level), the region is run with 1 thread, region annotation (scope, single-entry-single-exit) as memory barrier conservatively for now (instead of checking individual memory dependency, aliasing via tags which is the actual

> On Jan 31, 2017, at 6:48 PM, Tian, Xinmin <xinmin.tian at intel.com> wrote: > > Let me try this. > > You can simply consider the prepare-phase (e.g. pre-privatization) were done in FE (actually a library can be used by multiple FEs at LLVM IR level), the region is run with 1 thread, region annotation (scope, single-entry-single-exit) as memory barrier conservatively

In this case, inliner is educated to add all local variables to the tag of enclosing parallel region, if there is enclosing parallel region. In our icc implementation, it is even simple, as we have routine level symbol table, the inliner adds ”private” attribute to those local variables w/o checking enclosing scope, the parallelizer does check and use it. Xinmin From: mehdi.amini at apple.com

----- Original Message ----- > From: "Timothy Mattausch Creech" <tcreech at umd.edu> > To: "Sebastian Dreßler" <dressler at zib.de> > Cc: "Aparna Kotha" <akotha at umd.edu>, llvmdev at cs.uiuc.edu > Sent: Sunday, March 3, 2013 11:32:49 AM > Subject: Re: [LLVMdev] AESOP autoparallelizing compiler > > Hi Sebastian, > Sure!

[XT] Back from Biz trips, trying to catch up with the discussion. >>>>I agree that outlining function sub-bodies and passing in the function pointers to said outlined bodies to OpenMP helpers lets us correctly implement the semantics we need. However, unless I severely misunderstood the thread, I thought the key idea was to move *away* from that representation and towards a