search for: block1

...s entered. So it would be convenient to refer to such a sequence of commands as a "named block" -- just give its name, and they are executed. In my experiments, wrapping the first occurrence of such a sequence in a function definition seems to work, e.g. the first time they are needed: block1 <- function(){ sequence of commands that you would have enetered for execution at this point } block1() This first call to block1() seems to work OK, in my tests, PROVIDED, of course, a) The variables it uses and assigns to exist already; b) all internal "<-" assignments are wr...

...root, - struct btrfs_path *path, int level) +static noinline void reada_for_balance(struct btrfs_root *root, + struct btrfs_path *path, int level) { int slot; int nritems; @@ -2192,12 +2188,11 @@ static noinline int reada_for_balance(struct btrfs_root *root, u64 gen; u64 block1 = 0; u64 block2 = 0; - int ret = 0; int blocksize; parent = path->nodes[level + 1]; if (!parent) - return 0; + return; nritems = btrfs_header_nritems(parent); slot = path->slots[level + 1]; @@ -2224,28 +2219,11 @@ static noinline int reada_for_balance(struct btrfs_root *root...

Lets say you had 4 servers and you wanted to setup replicate and distribute. What methoid would be better: server sdb1 xen0 brick0 xen1 mirror0 xen2 brick1 xen3 mirror1 replicate block0 - brick0 mirror0 replicate block1 - brick1 mirror1 distribute unify - block0 block1 or server sdb1 sdb2 xen0 brick0 mirror3 xen1 brick1 mirror0 xen2 brick2 mirror1 xen3 brick3 mirror2 replicate block0 - brick0 mirror0 replicate block1 - brick1 mirror1 replicate block2 - brick2 mirror2 replicate block3 - brick3 mirror3 distribute...

...se the global could be > mutated > >> between the first return and the resume. > > Hmmm... I don't see the problem. I think hoisting the store is perfectly > legal > transformation with all semantics preserved. > > Let's look at your example: > > >> block1: > >> suspend > >> switch (resume, destroy, return) > >> > >> resume: > >> store zero to global @g > >> doA() > >> [...] > >> > >> destroy: > >> store zero to global @g > >> doB() >...

On Thu, Jun 9, 2016 at 1:49 PM, Eli Friedman <eli.friedman at gmail.com> wrote: >> Right... but that doesn't mean the call to the suspend intrinsic has to be >> the last non-terminator instruction in the basic block before you run >> CoroSplit. You can split the basic block in CoroSplit so any instructions >> after the suspend call are part of a different basic

...alloca, so the barrier doesn't actually block code movement. Got it. I am new to this and learning a lot over the course of this thread. Thank you for being patient with me. Two questions and one clarification: Q1: Do we have to have a load here? =================================== >> block1: >> %first_time = load... <--- What are we loading here? >> br i1 %first_time, label return, label suspend1 >> >> supend1: >> %0 = coro.suspend() >> switch %0 (resume1, destroy1) Can we use three way coro.suspend instead? Block1: %0 = call...

...fork intrinsic... thinking about it a bit more, I think > you're going to run into problems with trying to keep around a return block > through optimizations: > > [...] > %first.return = call i1 @llvm.experimental.coro.fork() > %cmp = icmp eq i32 %x, 0 > br i1 %cmp, label %block1, label %block2 > > block1: > [...] > br i1 %first.return, label %coro.return, label %coro.start > > block2: > [...] > br i1 %first.return, label %coro.return, label %coro.start > > coro.return: > %xxx = phi i32 [ %a, %block1 ], [ %b, %block2 ] > call void @f(i32...

...in destroy clone > (in destroy clone none of the suspends points should be reachable, > if they are it is a front end bug) > > Much prettier than before :-) > > > I'm not sure this quite works the way you want it to in terms of the optimizer. For example: block1: suspend switch (resume, destroy, return) resume: store zero to global @g [...] destroy: store zero to global @g [...] return: store zero to global @g [...] Naively, you would expect that it would be legal to hoist the store... but that breaks your coroutine semantics because the global could b...

...rking environment utils::data(npk, package="MASS") # get data model <- yield ~ block + N*P*K ## Using lm npk.lm <- lm(model, npk) ( s.npk.lm <- summary(npk.lm) ) ... Estimate Std. Error t value Pr(>|t|) (Intercept) 54.8750 0.8021 68.415 < 2e-16 *** block1 1.7125 1.3893 1.233 0.24131 block2 1.6792 0.8021 2.093 0.05822 . block3 -1.8229 0.5672 -3.214 0.00744 ** ... s.npk.lm$coef["block1","Pr(>|t|)"] # this works [1] 0.2413061 ## Using aov npk.aov <- aov(model, npk) ( s.npk.aov...

...ed block layout optimizations). > Again, any ordering changes that cause optimization differences are clear bugs in the pass. Yes, i know that this means we have a lot of bugs :) I consider any case where, due to a limit, identical [1] code optimizes differently, to be a bug. You should too! block1: foo block2: bar should optimize the same as block2: bar block1: foo Let me try to put this another way: My argument is precisely that code that is literally identical (but subject to expression in multiple textual forms) should be optimized the same way, and that's the avoidance of non-dete...

Hi Rasha, > for(rit=Result.begin();rit!=Result.end();++rit) > { > Value* Address= BlockAddress::get (*rit); > > IndirectBrInst *IBI = IndirectBrInst::Create(Address, Result.size(),i->getTerminator() ); > IBI->addDestination((*rit)); > } This would be creating a block looking something like: [ Do stuff ] indirectbr i8*

I have 6 boxes with a client config (see below) across 6 boxes. I am using distribute across 3 replicate pairs. Since I am running xen I need to disable-direct-io and that slows things down quite a bit. My thought was to move the replicate / distribute to the backend server config so that self heal can happen on faster backend rather then frontend client with disable-direct-io. Does this

...ition identifiers, include a column with block layout=new[,1:5] layout[1:30,] #9th columns which give the median foreground =values of x-variables fg1=as.matrix(new[,9]) length(fg1) mean(fg1) # calculate the mean of x1 #### I try to do something like :########## block1=fg1[layout$Block==1,] block2=fg1[layout$Block==1,] block2=fg1[layout$Block==1,] average=(block1+block2+block3)/3 but it did not work. ################## How can i calculate the means of remaining x_variables? ######### Read data for the remaining slides =x2,x3,x4,x5 #########...

Hi 1- for(rit=Result.begin();rit!=Result.end();++rit) { Value* Address= BlockAddress::get (*rit); IndirectBrInst *IBI = IndirectBrInst::Create(Address, Result.size(),i->getTerminator() ); IBI->addDestination((*rit)); } I tried this code , but the needed destination wasn't added. 2- About LLVM backend $ llc -march=cpp example_file.ll -o I think it

...quot; [7] "OUTCOME5" "SNP1X" "SNP1Y" "SNP2X" "SNP2Y" "SNP3X" [13] "SNP3Y" "SNP4X" "SNP4Y" "SNP5X" "SNP5Y" "SNP6X" [19] "SNP6Y" "SNP7X" "SNP7Y" > block1<-datafile.dat[,c(8:21)] > loci<-c("1","2","3","4","5","6","7") > em<-haplo.em(geno=block1,locus.label=loci,miss.val=c(0,NA)) Error in .Call("R_lazyLoadDBfetch", key, file, compressed, hook, PACKAGE = &...

Hello all, I have a 2.0 GB dataset that I can't load into R, due to memory issues. The dataset itself is in a tab-delimited .txt file with 25 variables. I have a variable I'd like to add to the dataset. How do I do this? Best, Guillaume

The following R commands were written: >help.search("factorial") >data(npk) >npk >coef(npk.aov) In the output of coef command, please explain me the interpretation of coefficients of block1 to block 6 in this 2^3 confounded factorial experiment. Thanks. [[alternative HTML version deleted]]

...akeCluster(detectCores()-1) registerDoParallel(cl) # foreach(i=1:100) %dopar% sqrt(i) d <- sort(rep(letters[1:24], 5)) e <- rep(1:24, 5) rand.int <- rnorm(n=length(e),mean=e, sd=4) f <- 3+ (e*rand.int)^2 g <- sort(rep(1:6, 20)) one <- data.frame(d,e,f,g) names(one) <- c('block1', 'ind', 'res','block2') one[1:50,] two <- expand.grid(one[,1], one[,4]) #actually is expand.grid(unique(one[,1]), unique(one[,4])) str(two) names(two) <- c('block1', 'block2') fitting <- function(ndx.grd=two,dt.grd=one,ind.vr='ind',r...

...the wrong file in earlier... this is the correct one: pValCalculator(b, n=20, m=20) { ind <- 1:min(c(n,m)) prob <- (1-pnorm(b,sd=std*sqrt(ind))) prob1 <- sum((n-ind+1)*(m-ind+1)*prob) prob1 } inputData <- scan("/users/lvssso/projects/LAMA/output/pValLamaScore.tmp", list(block1 = "",block2 = "",width1 = 0,width2 = 0,alignment = 0,score = 0)); pVal <- vector(mode = "numeric", length(inputData$score)); for(i in 1:length(inputData$score)) pVal[i] <- pValCalculator(inputData$score, inputData$width1, inputData$width2); inputData$pValue...

Hi, I am running a large number of jobs (thousands) in parallel (linux OS 64bit), R version 2.14.1 (2011-12-22), Platform: x86_64-redhat-linux-gnu (64-bit). Up to yesterday everything ran fine with jobs in several blocks (block1, block2 etc) of submission. They are sent to an LSF platform to handle the parallel submission. Today I see that only one of the blocks (the 19) has not finished correct: It reports in the out file: Error: ReadItem: unknown type 98, perhaps written by later version of R Execution halted Checkin...