similar to: Thank you!

I'm trying to figure out the best way of fitting the same negative log-likelihood function to more than one set of data, using mle() from the stats4 package. Here's what I would have thought would work: -------------- library(stats4) ## simulate values r = rnorm(1000,mean=2) ## very basic neg. log likelihood function mll <- function(mu,logsigma) {

Friday I enabled the inlinehint function attribute in the inliner. It mostly affects the performance of -Os compiled code. I have made some measurements on the SPEC test suite to show what it means. I made three runs of then nightly tests. The baseline represents -Os with no inlinehint: make TEST=nightly OPTFLAGS=-Os EXTRA_LOPT_OPTIONS=-inlinehint-threshold=0

Dear R-users: The following code produces two cones in two panels. What I would like to have is to have them in one, and to meet in the origin. Does anyone have any good ideas how to do this? Thanks for your help Jaakko library(lattice) A<-matrix(ncol=2, nrow=64) for(i in 0:63) { A[i+1,1]<-sin(i/10) A[i+1,2]<-cos(i/10) }

Hello all... I am just starting to teach myself Bayesian methods, and am interested in learning how to use UMacs. I've read the documentation, but the single example is a bit over my head at the level I am at right now. I was wondering if anyone has any simple examples they'd like to share. I've successfully done a couple of simple gibbs examples, but have had a hard time

On 2/7/2017 20:02, Kostya Serebryany wrote: > ... > > My understanding is that BNDCU is the cheapest possible instruction, > just like XOR or ADD, > so the overhead should be relatively small. > Still my guesstimate would be >= 5% since stores are very numerous. > And such overhead will be on top of whatever overhead SafeStack has. > Do you have any measurements to

Thanks Teresa Yes it is astar, happen to send a tar of the sources but they are just copies from the spec distribution The ld command is: GNU ld (GNU Binutils) 2.29.1 Thanks for the guidance on path names. The prefix-replace just effects the string written to the object files right? So we could post-process that file with other tools as well, correct? Thanks again --david From: Teresa Johnson

Fails with gold too: Library-native.o:Library.cpp:regway: error: undefined reference to 'vtable for regwayobj' /home/dcallahan/fbsource/fbcode/third-party-buck/platform007/tools/binutils/bin/gold/ld: the vtable symbol may be undefined because the class is missing its key function clang-8: error: linker command failed with exit code 1 (use -v to see invocation) From: Teresa Johnson

I am trying to work through the usage of thinlto for distributed builds. Here is the simple thinlto usage, just add -flto=thin everywhere, easy: clang++ -flto=thin -O3 -c -o CreateWay_.o -DSPEC_CPU -DNDEBUG -DSPEC_CPU_LITTLE_ENDIAN -Wno-dangling-else CreateWay_.cpp clang++ -flto=thin -O3 -c -o Places_.o -DSPEC_CPU -DNDEBUG -DSPEC_CPU_LITTLE_ENDIAN

dear LLVMers, I am trying to use some of the LLVM alias analyses, and I would like to check two things with you: is scev-aa being maintained in LLVM 3.7? Second question: I run cfl-aa, and I got a very small number of pointer disambiguation (no alias) with it. My results for SPEC CINT 2006 follow below. Is this low number of no alias responses something to be excepted? Below the results that I

Hi Ghassan, this is very interesting, however... > We are currently working on revising a journal article that describes our work > on pre-allocation scheduling using LLVM and have some questions about LLVM's > pre-allocation scheduler. The answers to these question will help us better > document and analyze the results of our benchmark tests that compare our > algorithm with

Hi everyone, The code layout that TailMerging (inside BranchFolding) works on is not the final layout optimized based on the branch probability. Generally, after BlockPlacement, many new merging opportunities emerge. I did an experiment of adding additional BranchFolding and BlockPlacement after the existing BlockPlacement (i.e., -block-placement -branch-folder -block-placement) targeting

Perhaps you noticed that LLVM gained a new optimizing register allocator yesterday (r130568). Linear scan is going away, and RAGreedy is the new default for optimizing builds. Hopefully, you noticed because your binaries were suddenly 2% smaller and 10% faster*. Some noticed because LLVM started crashing or miscompiling their code. Greedy replaces a fairly big chunk of the code generator, so

Hi, I'm proposing to make vectorizer-maximize-bandwidth on by default for loop vectorizer because it should generally help performance. I've tested the performance impact on Intel sandybridge machine with speccpu benchmarks: Benchmark Base:Reference (1) ------------------------------------------------------- spec/2006/fp/C++/444.namd 26.84

The large percentages are from those tiny benchmarks. If you look at omnetpp (0.52%), and xalanc (1.46%), the increase is small. To get a better average increase, you can sum up total debug_line size before and after and compute percentage accordingly. David On Thu, Oct 27, 2016 at 1:11 PM, Dehao Chen <dehao at google.com> wrote: > The impact to debug_line is actually not small. I only

The impact to debug_line is actually not small. I only implemented the part 1 (encoding duplication factor) for loop unrolling and loop vectorization. The debug_line size overhead for "-O2 -g1" binary of speccpu C/C++ benchmarks: 433.milc 23.59% 444.namd 6.25% 447.dealII 8.43% 450.soplex 2.41% 453.povray 5.40% 470.lbm 0.00% 482.sphinx3 7.10% 400.perlbench 2.77% 401.bzip2 9.62% 403.gcc

Dear all, I would like to ask, if there is a way to make the variance / dispersion parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate the parameter vector $\beta$ additionally. If this is not possible with glm.nb, is there another function / package which might do that? Thank you very much for

Hi, I am trying to run a generalized linear regression using a negative binomial error distribution. However, I want to use an overdispersion parameter that varies (dependent on the length of a stretch of road) so glm.nb will not do. >From what I've read I should be able to do this using GLM by specifying my own quasi family and describing the variance function using varfun, validmu,

----- Original Message ----- > From: "Gerolf Hoflehner" <ghoflehner at apple.com> > To: "Hal Finkel" <hfinkel at anl.gov> > Cc: "LLVM Dev" <llvmdev at cs.uiuc.edu>, "Jiangning Liu" <liujiangning1 at gmail.com>, "George Burgess IV" > <george.burgess.iv at gmail.com> > Sent: Monday, September 15, 2014

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

On Sep 29, 2012, at 2:43 AM, Ghassan Shobaki <ghassan_shobaki at yahoo.com> wrote: > Hi, > > We are currently working on revising a journal article that describes our work on pre-allocation scheduling using LLVM and have some questions about LLVM's pre-allocation scheduler. The answers to these question will help us better document and analyze the results of our benchmark