search for: converged

Hi Jingyue, I consider it a very important element of the design of convergent that it does not require baseline LLVM to contain a definition of uniformity, which would itself pull in a definition of SIMT/SPMD, warps, threads, etc. The intention is that it should be a conservative (but hopefully not too conservative) approximation, and that implementations of specific GPU programming models

Hi all, In light of recent discussions regarding updating passes to respect convergent semantics, and whether or not it is sufficient for barriers, I would like to propose a change in convergent semantics that should resolve a lot of the identified problems regarding loop unrolling, loop unswitching, etc. Credit to John McCall for talking this over with me and seeding the core ideas. Today,

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

Hi, When I run the following code : Y <- c(rep(0,35),1,2,0,6,8,16,43) cst <- log(choose(42, 42:1)) beta <- 42:1 tau <- (beta^2)/2 fit <- glm(formula = Y ~ offset(cst) + beta + tau, family = poisson) fit fit$converged glm prints a warning saying that the algorithm did not converge. However, fit$converged takes the value TRUE. I don't understand why fit$converged is not always FALSE when the warning "algorithm did not converge" is produced. Could someone help me understand why I get this result?...

Hi Hal, On Mon, Aug 17, 2020 at 2:13 AM Hal Finkel <hfinkel at anl.gov> wrote: > Thanks for sending this. What do you think that we should do with the > existing convergent attribute? My preference, which is implicitly expressed in the review, is to use `convergent` both for the new and the old thing. They are implicitly distinguished via the "convergencectrl" operand

Hi all, please see https://reviews.llvm.org/D85603 and its related changes for our most recent and hopefully final attempt at putting the `convergent` attribute on a solid theoretical foundation in a way that is useful for modern GPU compiler use cases. We have clear line of sight to enabling a new control flow implementation in the AMDGPU backend which is built on this foundation. I have

On Mon, Aug 17, 2020 at 7:14 PM Hal Finkel <hfinkel at anl.gov> wrote: > On 8/17/20 11:51 AM, Nicolai Hähnle wrote: > > Hi Hal, > > > > On Mon, Aug 17, 2020 at 2:13 AM Hal Finkel <hfinkel at anl.gov> wrote: > >> Thanks for sending this. What do you think that we should do with the > >> existing convergent attribute? > > My preference, which

Hi Jingyue, Convergent is not intended to prevent inlining. It’s tricky to formalize this inter-procedurally, but the intended interpretation is that a convergent operation cannot be move either into or out of a conditionally executed region. Normal inlining would not violate that. I would imagine that it would make sense to use a combination of convergent and noduplicate for barrier-like

Full_Name: Bonnie Version: 1.6.1 OS: Windows Submission from: (NULL) (160.129.25.106) glm() seems to converge, even when it shouldn't. I am trying to fit a model where $converge=FALSE and I am fitting models that do not converge in SAS, but they seem to converge in R ... Thank you.

Hi Mehdi, My reading of it is that if you have a convergent instruction A, it is legal to duplicate it to instruction B if (assuming B is after A in program flow) A dominates B and B post-dominates A. James On Fri, 14 Aug 2015 at 08:32 Mehdi Amini via llvm-dev < llvm-dev at lists.llvm.org> wrote: > On Aug 13, 2015, at 9:43 PM, Owen Anderson via llvm-dev < > llvm-dev at

hie.. how can i write a loop that makes algorithm keeps repeating until a solution is converged?do i use a for loop? i know that we can use for loop to ask for a number of repetitions, but how to use it to ask the algorithm to keep repeating until a solution is converged? Thanks -- View this message in context: http://www.nabble.com/convergence-tf3606834.html#a10076822 Sent from the R help m...

I really want to do this: abline( a=tan(-kT*pi/180), b=kY-tan(-kT*pi/180)*kX ) where kX,kY and kT are vectors of equal length. But I can't do that with abline unless I use a loop, and I haven't figured out the least unelegant way of writing the loop yet. So is there a way to do this without a loop? Or if I am to resort to the loop, what's the best way of doing it considering that I

Are the messages below to be expected from make check-all ? using the rc today, Aug 21, on Red Hat Enterprise Linux AS release 4 (Nahant Update 4) Kernel 2.6.9-42.0.8.ELsmp on an x86_64 Paul ________ .... checking package 'utils' .... * checking R code for possible problems ... NOTE install.packages: no visible global function definition for ?.install.winbinary? install.packages: no

> On Sep 14, 2015, at 12:15 PM, Philip Reames <listmail at philipreames.com> wrote: > > On 09/04/2015 01:25 PM, Owen Anderson via llvm-dev wrote: >> Hi all, >> >> In light of recent discussions regarding updating passes to respect convergent semantics, and whether or not it is sufficient for barriers, I would like to propose a change in convergent semantics that

Hi R-users, I'm attempting to fit a number of mixed models, all with the same structure, across a spatial grid with data points collected at various time points within each grid cell. I'm trying to use a 'for' loop to try the model fit on each grid cell. In some cells lme does not converge, giving me the error: Error message: In lme.formula(logarea ~ year + summ_d, data =

Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't update to 2.13). I am iterating through many linear mixed models for longitudinal data and I occasionally receive the following convergence error: > BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12, random =~ 1 + time | IID, data = d) Error in lme.formula(cd4 ~ time + genBI + genBI:time +

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hola Carlos, Gracias por la respuesta. phen_tot es un data frame visualizado como tibble, y contiene (entre otras) las columnas "convergence", "r_square" y "maxlog10mfi". Y queremos crear nuevas columnas, como "use", "convergence_cor", etc. phen_tot$convergence debería ser un factor de si el modelo converge o no (levels: 1 y 2), pero tibble lo

What are some reliable R functions that can compute the value of a convergent series? David -- David R. Bickel, PhD Associate Professor Ottawa Institute of Systems Biology Biochem., Micro. and I. Department Mathematics and Statistics Department University of Ottawa 451 Smyth Road Ottawa, Ontario K1H 8M5 http://www.statomics.com Office Tel: (613) 562-5800 ext. 8670 Office Fax: (613) 562-5185