similar to: Profiler for R ? (HFWUtils package)

Hi, is there such a thing as a profiler for R that informs about a) how much processing time is used by particular functions and commands and b) how much memory is used for creating how many objects (or types of data structures)? In a way I am looking for something similar to the java profiler (which is started by command line and provides profiling information collected from the run of a

Sorry, this might be a really basic question, but I'm trying to interpret the results from memory profiling, and I have a few questions (marked by *Q#*). From the summaryRprof() documentation, it seems that the four columns of statistics that are reported when setting memory.profiling=TRUE are - vector memory in small blocks on the R heap - vector memory in large blocks (from malloc) - memory

I'm picking up this 5-year old thread. 1. About the four memory allocations without a stacktrace I think the four memory allocations without a stacktrace reported by Rprofmem(): > Rprofmem(); x <- raw(2000); Rprofmem("") > cat(readLines("Rprofmem.out", n=5, warn=FALSE), sep="\n") 192 :360 :360 :1064 :2040 :"raw" are due to some

Hi all, When I run the example in RProfmem, I get: Rprofmem("Rprofmem.out", threshold=1000) example(glm) Rprofmem(NULL) noquote(readLines("Rprofmem.out", n=5)) ... [1] 1384 :5416 :5416 :1064 :1064 :"readRDS" "index.search" "example" [2] 1064 :"readRDS" "index.search" "example" [3] 4712

The Rprofmem facility is currently enabled only if the configuration option --enable-memory-profiling is used. However, the overhead of having it enabled is negligible when profiling is not actually being done, and can easily be made even smaller. So I think it ought to be enabled all the time. I've attached a patch doing this, which also makes a number of other improvements to Rprofmem,

I had assumed you would treat phi nodes differently from other operations in that they don’t need to keep the block alive just to retain the data flow facts but it would be simplest to do that. Thanks Daniel From: Daniel Berlin <dberlin at dberlin.org<mailto:dberlin at dberlin.org>> Date: Friday, January 29, 2016 at 10:26 PM To: David Callahan <dcallahan at

Hello all, I am trying to use cbind to add a named empty column to a Matrix: outputmatrix <- cbind(outputmatrix,kog_id = seq(0,0,0)) The problem I have is that kog_id is a variable that has a value e.g. "KOG1234", but I when I try to use this to name the added column, it is named literally "kog_id" instead of "KOG1234". How can I name a column by passing in a

I think you can also avoid the RDF computation using a more directed form of control dependence testing such as described in Keshav Pingali and Gianfranco Bilardi. 1997. Optimal control dependence computation and the Roman chariots problem. ACM Trans. Program. Lang. Syst. 19, 3 (May 1997), 462-491. DOI=http://dx.doi.org/10.1145/256167.256217 However one challenge seems to be fixing the SSA graph

Hello all, I am trying to use the micEcon 'insertRow' function inside a function I have written. For example: insert_row_test <- function(m){ insertRow(m,nrow(m)+1,v=0,rName="test") } However, when I try to call the 'insert_row_test' function (after loading the micEcon package), it does not insert a row into the matrix I pass in. When I call the insertRow

Hi, there was a memory improvement done in R going from R 3.3.3 to R 3.4.0 when it comes to comparing an integer 'x' an double 'y' (either may be scalar or vector). For example, in R 3.3.3, I get: > getRversion() [1] '3.3.3' > x <- integer(1000) > y <- double(1000) > profmem::profmem(z <- (x < y)) Rprofmem memory profiling of: z <- (x < y)

Hi all, Where can I find an R binary version (>2.4.0 ) for windows that compiled with R_MEMORY_PROFILING? Within our application we are experiencing serious problems with memory usage. And being able to use "Rprofmem" and "tracemem" command seems like our best option. Thanks, Yoni [[alternative HTML version deleted]]

Thanks for the response. To clarify in your suggestion, llvm-link will combine the modules but not run the optimization pass, that is still delayed until the final binary is built, correct? My use case is apply LTO to roughly program subsets; sacrificing effectiveness to avoid scaling problems and to allow the artifacts to be reused like archives and cached like .o’s. I need to trigger the

Hello, I have current data from a nortek ADP, which is basically current speed and direction data in a 3 dimensional X Y Z format http://www.nortekusa.com/usa/products/current-profilers/aquadopp-profiler-1 The instrument logs data in a complex way and I was wondering if anyone has had any experience using R to at least read the data, and perhaps smooth it as well. If so, are there any

Maybe I was too quick here. Does gcc record the incoming edge to a phi? If so, won’t those change when you delete blocks in a non-trivial manner? How are those updated? From: David Callahan <dcallahan at fb.com<mailto:dcallahan at fb.com>> Date: Saturday, January 30, 2016 at 7:02 AM To: Daniel Berlin <dberlin at dberlin.org<mailto:dberlin at dberlin.org>>, Hal Finkel

(and sys::cas_flag that STATISTIC uses is a uint32 ...) On Thu, Aug 25, 2016 at 9:54 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > Okay, dumb question: > Are you really getting negative numbers in the second column? > > 526,766 -136 mem2reg # PHI nodes inserted > > http://llvm.org/docs/doxygen/html/PromoteMemoryToRegister_8cpp_source.html >

Thanks Also I found that some cases are also caught by a specialized routine to remove dead loops which is missing the case I noticed. odavd From: Daniel Berlin <dberlin at dberlin.org<mailto:dberlin at dberlin.org>> Date: Thursday, January 28, 2016 at 8:45 PM To: David Callahan <dcallahan at fb.com<mailto:dcallahan at fb.com>>, LLVM Dev Mailing list <llvm-dev at

On Thu, Jan 28, 2016 at 10:09 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > ------------------------------ > > *From: *"David Callahan via llvm-dev" <llvm-dev at lists.llvm.org> > *To: *"Daniel Berlin" <dberlin at dberlin.org>, "LLVM Dev Mailing list" < > llvm-dev at lists.llvm.org> > *Sent: *Thursday, January 28, 2016

I am interested in putting together a tool which can verify that an external declaration conforms with the actual definition. A.c: extern int32_t foo(); Int bar() { return foo(); } B.c: int64_t foo() { ... } I had thought that I could use the debug information but the debug information in A.o does not include the type of the declaration "foo", just the type of bar. Is there a way to

In practice, APT is not faster to build than rdf. The df calculator we use is linear time and quite fast. Updating is also pretty trivial since it's only deletes of dead and unreachable code. So anything it reached can be replaced with undef in most cases. Cd-dce is not slower in GCC than dce On Fri, Jan 29, 2016, 8:31 PM David Callahan <dcallahan at fb.com> wrote: > I think you

Hi, I have a new variant of Aggressive Dead Code Elimination that also removes dead branching. It is designed to minimize the cost of control-dependence analysis in the common case where almost the entire program is live. It also can optionally remove dead but may-be-infinite loops. When enabled for –O3 (replacing current ADCE pass) and removing loops, impact on SPEC2006 is in the noise but it