search for: parallelisable

Hi Nadav, On 02/07/2013 07:46 PM, Nadav Rotem wrote: > Pekka suggested that we add two kind of metadata: llvm.loop.parallel > (attached to each loop latch) and llvm.mem.parallel (attached to each memory > instruction!). I think that the motivation for the first metadata is clear - > it says that the loop is data-parallel. I can also see us adding additional > metadata such as

Hi, I am involved in the task of achieving speculative paralellisation in llvm. I have started my work by trying to see if a simple for loop can be paralellised in llvm.. The problem is i want to know how to check if a program is automatically parallelised when compiled with llvm or if explicitly need to do it how can i go about paralellising a for loop using llvm compiler infrsatructure.how

Hi, I am continuing the discussion about Parallel Loop Metadata from here: http://lists.cs.uiuc.edu/pipermail/llvmdev/2013-February/059168.html and here: http://lists.cs.uiuc.edu/pipermail/llvmdev/2013-February/058999.html Pekka suggested that we add two kind of metadata: llvm.loop.parallel (attached to each loop latch) and llvm.mem.parallel (attached to each memory instruction!). I think

Hello, at the moment I am doing quite a lot of regression, especially logistic regression, on 20000 or more records with 30 or more factors, using the "step" function to search for the model with the smallest AIC. This takes a lot of time on this 1.8 GHZ Pentium box. Memory does not seem to be such a big problem; not much swapping is going on and CPU usage is at or close to

Dear all, I am Raghesh, a student pursuing M.Tech at Indian Institute of Technology, Madras, India. I would like to make contribution to the Polly project (http://wiki.llvm.org/Polyhedral_optimization_framework) as part of GSOC 2011. I have gained some experience working in OpenMP Code generation for Polly. This is almost stable now and planning to test with the polybench benchmarks. Some of

...when I was using vector/matrix notation. I think the greatness of R lies in a lovely vector/matrix notation, and it seems like a shame to have to not use that when trying to do deriv(). * For iid problems, the computation of the likelihood function and it's gradient vector are inherently parallelisable. How would one go about doing this within R? -- Ajay Shah Consultant ajayshah at mayin.org Department of Economic Affairs http://www.mayin.org/ajayshah Ministry of Finance, New Delhi

Hi I try parallelising some code using the snow package and the following lines: cl <- makeSOCKcluster(8) pfunc <- function (x) (if(x <= (-th)) 1 else 0) ###correlation coefficient clusterExport(cl,c("pfunc","th")) cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) The parApply results in the error message: > cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) Error

Hello, I have a question that I was wondering if anyone had a fairly straightforward answer to: what is the quickest and easiest way to take advantage of the extra cores / processors that are now commonplace on modern machines? And how do I do that in Windows? I realise that this is a complex question that is not answered easily, so let me refine it some more. The type of scripts that I'm

On 26 Sep 2012, at 01:08, Jan Voung wrote: > I've been looking into how to make llvm bitcode files smaller. There is one simple change that appears to shrink linked bitcode files by about 15% Whenever anyone proposes a custom compression scheme for a data format, the first question that should always be asked is how does it compare to using a generic off-the-shelf compression algorithm.

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

Hello, I have some code to run on an XGrid cluster. Currently the code is written as a single, large job... this is no good for trying to run in parallel. To break it up I have basically taken out the highest level for-loop and am planning on batch-running many jobs, each one representing an instance of the removed loop. However, when it comes to output I am stuck. Previously the output was

On 01/08/2011 07:34 PM, Renato Golin wrote: > On 9 January 2011 00:07, Tobias Grosser<grosser at fim.uni-passau.de> wrote: >> Matching the target vector width in our heuristics will obviously give the >> best performance. So to get optimal performance Polly needs to take target >> data into account. > > Indeed! And even if you lack target information, you

Hi, After our big ext3 file server crashes, I notice the fsck spends some time replaying the journals (about 5-10 mins for all volumes on the server in question). I guess it must do this should you want to mount the volumes as ext2. My question--is it (theoretically) possible to tell fsck only to replay half-finished and to knock out incomplete transactions from the journals, leaving the kernel

As you will have seen from previous postings, I've been working on upstream LLVM support for the RISC-V instruction set architecture. The initial RFC <http://lists.llvm.org/pipermail/llvm-dev/2016-August/103748.html> provides a good overview of my approach. Thanks to funding from a third party, I've recently been able to return to this effort as my main focus. Now feels like a good

On 9 January 2011 00:07, Tobias Grosser <grosser at fim.uni-passau.de> wrote: > Matching the target vector width in our heuristics will obviously give the > best performance. So to get optimal performance Polly needs to take target > data into account. Indeed! And even if you lack target information, you won't generate wrong code. ;) > Talking about OpenCL. The lowering

I was on vacation the last week and wrote some code to run a 500-day correlation between the Nasdaq tracking stock (QQQ) and 191 currency pairs for 500 days. The initial run took 9 hours(!) and I'd like to make it faster. So, I'm including my code below, in hopes that somebody will be able to figure out how to make it faster, either through parallelisation, or by making changes. I've

On Tue, Mar 14, 2023 at 04:06:18PM +0200, Andrey Drobyshev wrote: > Speaking of "make check": could you point out, for future reference, > which particular sub-target you're referring to here? I can see these: > check-am, check-recursive, check-slow, check-TESTS, check-valgrind. And > none of them seems to refer to checking docs integrity. Yet running > entire

Hi all, The most time consuming part of LTO at opt level 1 is by far the backend code generator. (As a reminder, LTO opt level 1 runs a minimal set of passes; it is most useful where the motivation behind the use of LTO is to deploy a transformation that requires whole program visibility such as control flow integrity [1], rather than to optimise the program using whole program visibility). Code

A question to all you R-gurus: Can R (or S-plus, for that matter) make efficient use of multiple Intel Processors running under Linux (within the same PC, not over a net)? With the release of the new 2.2 kernel, this would seem a interesting and cost-efficient way of boosting the computational power of Intel/Linux platforms when using R (or S-plus). Thanks for any wise words, Kenneth