similar to: [LLVMdev] Ambiguity in LLVM IR for global variable sections

On 15 September 2013 21:40, David Majnemer <david.majnemer at gmail.com> wrote: > What do we expect from the following IR? > > @i2 = constant i32 30, section "INITDATA", align 4 > @i1 = global i32 0, section "INITDATA", align 4 > > Currently, we stick them in one section that isn't writable which is > problematic because @i1 is writable. If you

I've come around and believe #3 is the right answer. Better yet, we should give a way to specify the section attributes somewhere explicitly and only infer them as a fallback. On Mon, Oct 7, 2013 at 7:45 AM, Rafael Espíndola <rafael.espindola at gmail.com > wrote: > On 15 September 2013 21:40, David Majnemer <david.majnemer at gmail.com> > wrote: > > What do we

Dear R Experts, I have written a following function :- myfunction<- function(servername,dbname,dbtablename){ library(RODBC) channel <- odbcDriverConnect("driver=SQL Server;server=servername") initdata<- sqlQuery(channel,paste("select * from dbname .. dbtablename")) return(dim(initdata)) } I have written this function which has input parameters like servername

I've spent some time trying to wrap my head around reading in large csv files with the ff-package. I think I know how to do it, but am bumping into some problems. I've tried to recreate the issues as best as I can with a smaller example and maybe someone can help explain the problems. The following code just creates a csv file with an integer column, character column and logical column.

The attached patch includes no test-case and isn't consistent with the rest of the file: - constants should be on the right hand side of comparisons - the braces around your single line 'if' aren't needed. On Sun, Sep 15, 2013 at 2:39 PM, James Courtier-Dutton < james.dutton at gmail.com> wrote: > I attach a patch that fixes this bug. Applies to llvm 3.4svn > >

Hi, The read.csv.ffdf function in package ff will create the ff object physical file in the default directories, I am trying to let the files created in the paths users specify, I think the point is to make use of the asffdf_args parameter, I have a test CSV file named D:\rtemp\fftest.csv, the content of the file is as following: col1,col2,col3 1,"amber",2.4 2,"linda",4.5

I don't know how things work at the moment, but it seems to me that you can do lots of sensible things, and avoid lots of silly things, if you keep track of four possible values for each bit: - undef (the default) - poison - known to be 0 - known to be 1 This makes both David's and Chandler's examples work nicely if you assume: - ZEXT makes all the new bits known 0 - SEXT makes all

On Tue, Jan 27, 2015 at 8:58 PM, Sanjoy Das <sanjoy at playingwithpointers.com> wrote: > > Ah, yes. You are right, we cannot always assume that %y would be zero in > > the second case. > > This wouldn't be the first time we've lost information that we could use > to > > optimize a program by transforming it. > > > > Do you think this result

Hi friends, Please can anyone help me with an easier solution of doing the below mentioned work. Suppose i have a dataset like this:--- i1 i2 i3 i4 i5 1 7 13 1 2 2 8 14 2 2 3 9 15 3 3 4 10 16 4 4 5 11 17 5 5 6 12 18 6 7 *i1,i2,i3,i4,i5 are my items.I am able to find all possible pairs i.e Say this dataframe is "item_pairs" **i1,i2 **i1,i3 **i1,i4 i1,i5 **i2,i1

Hi Duncan & Tobi, Thanks a lot for your interest, and for pointing out differences in GIMPLE I missed. Attached is simplified test case. Is it good? Tobi, regarding runtime alias analysis: in KernelGen we already do it along with runtime values substitution. For example: <------------------ __kernelgen_main_loop_17: compile started ---------------------> Integer args substituted:

I'm using functions to return a matrix of all permutations of a specified size from a larger list for predictor selection. For each predictor size I use a seperate function like this: bag2 <- function(n) { rl <- c() for (i1 in seq(n)) { for (i2 in seq(n)) { if (length(unique(c(i1,i2)))==1) {next} rl <- cbind(rl,matrix(c(i1,i2))) } }

On 9/25/2017 9:14 AM, Björn Pettersson A wrote: > > (Not sure if this exactly maps to “truncating store”, but I think it > at least touches some of the subjects discussed in this thread) > > Our out-of-tree-target need several patches to get things working > correctly for us. > > We have introduced i24 and i40 types in ValueTypes/MachineValueTypes > (in addition to

(Not sure if this exactly maps to “truncating store”, but I think it at least touches some of the subjects discussed in this thread) Our out-of-tree-target need several patches to get things working correctly for us. We have introduced i24 and i40 types in ValueTypes/MachineValueTypes (in addition to the normal pow-of-2 types). And we have vectors of those (v2i40, v4i40). And the byte size in our

False Alarm!! Still don't know how to do it! I am trying to write a transformation pass to extract a loop body into a function. For example: The Loop in question is: for (i2 = 0; i2 < LOOP_SIZE; i2++) { A[B[i2]] = 2 * B[i2]; } The IR for which is: bb13: ; preds = %bb13, %bb %i2.0.reg2mem.0 = phi i32 [ 0, %bb ], [ %indvar.next62, %bb13 ] ; <i32>

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Hi, I am trying to speed up part of an algorithm in which certain columns of a large matrix (X) are replaced by the element-wise product of a matrix (M) and a vector (v). In R, the code might be X[, ind] <- M * v I have written a small C routine to do this for me, but the timing depends on how I define the storage.mode of the objects in R and the data types in C, in a way which I don't

I am trying to multiply a 3D array of 4x4x4 by the 4 3D arrays of a 4D array with dimensions 4x4x4x4 (the last dimension being the one that I want to split by). (4x4x4 array) > hiaAry , , a1 i1 i2 i3 i4 h1 9.5936098 6.001040 0.08772 0.3138600 h2 1.2003500 1.454570 2.79248 0.0000000 h3 0.1346500 0.201220 0.39256 0.5464000 h4 0.0109000 0.012270 0.16417 0.2766900 ,

Crossed random effects: > lmer( y ~ (1 | i1) + (1|i2) ,data=dta) Linear mixed-effects model fit by REML Formula: y ~ (1 | i1) + (1 | i2) Data: dta AIC BIC logLik MLdeviance REMLdeviance 91.18 94.84 -42.59 85.2 85.18 Random effects: Groups Name Variance Std.Dev. i1 (Intercept) 0.068224 0.26120 i2 (Intercept) 0.463112

Hi Everyone, quick question before the end of the year. I have soem indices to select data from a bigger sample. I want to select n days before each index and n days after the index. Any clever way to do it. A for loop would do but I wanted to know if there is a moreR-friendly way to approach this Example # InitialIndices i2 = (90, 190, 290) # Indices I want to end up with i3 = c(85, 86, 87,

Dear list, I have a list of matrices : i1 <- matrix(1:10, nrow = 2, ncol = 5) i2 <- matrix(11:20, nrow = 2, ncol = 5) j <- list(i1 = i1, i2 = i2) I would like to attribute names to each dimension, for each matrix, as follows : $i1 B1 B2 B3 B4 B5 A1 1 3 5 7 9 A2 2 4 6 8 10 $i2 B1 B2 B3 B4 B5 A1 11 13 15 17 19 A2 12 14 16 18 20 However, I have to use lapply function and attribute