search for: linpack

Dear developers, I am trying to reproduce the results of applying opt -O3 to a source file in the form of LLVM IR. I want to get the same IR by manually ordering the passes used by O3 and passing them to opt. To illustrate what I am doing on an example, as an input I use linpack benchmark from the LLVM test suite[1]: 1. First I produce the intermediate representation using clang: clang -O3 -Xclang -disable-llvm-optzns -emit-llvm -S linpack-pc.c -o linpack-pc.ll 2. Then I use opt to optimize the IR: opt -S -O3 -o linpack-pc-3.ll linpack-pc.ll Now my goal is to produce th...

Dear R Developers, I should have had the Design library running in R by now but have kept putting off changing some calls to LINPACK routines to use those builtin to R. Specifically I call dqrsl1 and dqr. Would it be an easy task to put those in the next release of R? If not I'll finally bite the bullet and get back into reading LINPACK documentation (which I have but haven't examined in depth). If dqrsl1 and dqr are...

...>> I am trying to reproduce the results of applying opt -O3 to a source file >> in the form of LLVM IR. I want to get the same IR by manually ordering the >> passes used by O3 and passing them to opt. >> >> To illustrate what I am doing on an example, as an input I use linpack >> benchmark from the LLVM test suite[1]: >> >> 1. First I produce the intermediate representation using clang: >> clang -O3 -Xclang -disable-llvm-optzns -emit-llvm -S linpack-pc.c -o >> linpack-pc.ll >> >> 2. Then I use opt to optimize the IR: >> opt...

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.31...

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

...: rel/abs: 1.600000e-02/0.000000e+00 ******************** TEST (simple) 'sse.stepfft' **************************** The third also. /home/rkotler/workspace/llvmpb3/projects/test-suite/DiffOutput.sh "/home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp -r 0.0001" simple linpack-pc ******************** TEST (simple) 'linpack-pc' FAILED! ******************** Execution Context Diff: /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: 4.500000e+01 and 0.000000e+00 abs. diff = 4.500000e+01 rel.diff = 1.000000e+00 Out of tolerance: rel/abs: 1.000000e...

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get the parameter correlations, so I need to invert the matrix. There are times where the standard "solve(X)" returns an error, but &qu...

...tepfft TEST-RESULT-exec-time: user 0.4000 TEST-RESULT-exec-real-time: real 0.4172 make[4]: Leaving directory `/home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/UnitTests/Vector/SSE' TEST-FAIL: exec /home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/Benchmarks/Linpack/linpack-pc TEST-RESULT-exec-time: user 164.9400 TEST-RESULT-exec-real-time: real 165.3131 make[3]: Leaving directory `/home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/Benchmarks/Linpack' make[3]: Entering directory `/home/rkotler/llvmpb3/build/projects/test-suite/Singl...

...gt; ******************** TEST (simple) 'sse.stepfft' > **************************** > > The third also. > > /home/rkotler/workspace/llvmpb3/projects/test-suite/DiffOutput.sh > "/home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp -r > 0.0001" simple linpack-pc > ******************** TEST (simple) 'linpack-pc' FAILED! > ******************** > Execution Context Diff: > /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: > 4.500000e+01 and 0.000000e+00 > abs. diff = 4.500000e+01 rel.diff = 1.000000e+00 >...

...gt; ******************** TEST (simple) 'sse.stepfft' **************************** >> >> The third also. >> >> /home/rkotler/workspace/llvmpb3/projects/test-suite/DiffOutput.sh "/home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp -r 0.0001" simple linpack-pc >> ******************** TEST (simple) 'linpack-pc' FAILED! ******************** >> Execution Context Diff: >> /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: 4.500000e+01 and 0.000000e+00 >> abs. diff = 4.500000e+01 rel.diff = 1.000000e+00 &...

...and Linux (Redhat 6.2, enhanced with gcc 2.95.2; g77 version egcs-2.91.66 19990314/Linux (egcs-1.1.2 release) (from FSF-g77 version 0.5.24-19981002) I have managed to make svd() stop with something like "error 170 in dsvdc" which basically comes from the underlying LINPACK routine DSVDC() which did not converge in 30 QR iterations. example code appended --- The examples I've found are machine dependent and disjoint for Linux & Solaris. HOWEVER: S-PLUS 5.1 on both machines does NOT fail for the identical matrices! S-PLUS has basically .Fortran("dsvdc...

I'm probably misunderstanding something in "Writing R Extensions" version 1.0.0. In the chapter on the R API, section 4.7, it is stated that the functions listed in R_ext/Linpack.h are available to users' Fortran code. I am developing a developing a library of ode solvers, based on lsoda and ddassl, and which in turn call some routines from linpack and double precision blas. I originally developed the library including those routines, and got a functional shared librar...

...-exec-time: user 0.4000 > TEST-RESULT-exec-real-time: real 0.4172 > make[4]: Leaving directory `/home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/UnitTests/Vector/SSE' > > TEST-FAIL: exec /home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/Benchmarks/Linpack/linpack-pc > TEST-RESULT-exec-time: user 164.9400 > TEST-RESULT-exec-real-time: real 165.3131 > make[3]: Leaving directory `/home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/Benchmarks/Linpack' > make[3]: Entering directory `/home/rkotler/llvmpb3/build/project...

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Hi, I understand that the glm.fit calls LINPACK fortran routines instead of LAPACK because it can handle the 'rank deficiency problem'. If my data matrix is not rank deficient, would a glm.fit function which runs on LAPACK be faster? Would this be worthwhile to convert glm.fit to use LAPACK? Has anyone done this already?? What is...

...t from the expected results as computed using SPSS. Description of this function says:-- Solve a System of EquationsDescription This generic function solves the equation a %*% x = b for x, where b can be either a vector or a matrix. Usage solve(a, b, ...) ## Default S3 method: solve(a, b, tol, LINPACK = FALSE, ...) Arguments a a square numeric or complex matrix containing the coefficients of the linear system. b a numeric or complex vector or matrix giving the right-hand side(s) of the linear system. If missing, b is taken to be an identity matrix and solve will return the inverse of a. tol th...

> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > if (*info == 0){ > F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job); > ........ > &gt...

...hanks to all who answered my third question. I learned something, but: On 2017-02-09 17:44, Martin Maechler wrote: > >>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>> >>> In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: >>> >>> F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); >>> if (*info == 0){ >>> F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job);...

I want to speed up computations (involving matrices) by writing some C-code to be loaded. In the C-code, I need to invert matrices etc. As I've understood the "writing R extensions" doc, I can use use #include <R_ext/Linpack.h> in my .c-file and get access to linpack's facilities within my C-code. Is that correct? If so, can anyone point me to examples on how this is actually done in practice? Best regards S??ren