Type: Package
Title: 'Rcpp' Integration for the 'Blaze' High-Performance 'C++' Math Library
Version: 1.0.1
Date: 2024-04-27
Maintainer: Ching-Chuan Chen <zw12356@gmail.com>
URL: https://github.com/Chingchuan-chen/RcppBlaze, https://bitbucket.org/blaze-lib/blaze
BugReports: https://github.com/Chingchuan-chen/RcppBlaze/issues
Description: Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze'.
Depends: R (≥ 4.2.0)
Imports: Rcpp (≥ 1.0.0), Matrix (≥ 1.5-0)
LinkingTo: Rcpp
Suggests: MatrixExtra, tinytest, microbenchmark
LazyLoad: yes
Encoding: UTF-8
License: BSD_3_clause + file LICENSE
RoxygenNote: 7.3.1
NeedsCompilation: yes
Packaged: 2024-05-06 15:03:27 UTC; root
Author: Ching-Chuan Chen ORCID iD [aut, cre, ctr], Klaus Iglberger [aut] (blaze), Georg Georg [aut] (blaze), Tobias Scharpff [aut] (blaze)
Repository: CRAN
Date/Publication: 2024-05-08 12:50:07 UTC

RcppBlaze - 'Rcpp' Integration for the 'Blaze' High-Performance 'C++' Math Library

Description

RcppBlaze constructs a bridge between R and Blaze.

Details

Blaze is an open-source, high-performance C++ math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest C++ math libraries available. The RcppBlaze package includes the header files from the Blaze library with disabling some functionalities related to link to the thread and system libraries which make RcppBlaze be a header-only library. Therefore, users do not need to install Blaze.

Using RcppBlaze

To use RcppBlaze in your package, there are some important steps:

  1. Include the ‘⁠RcppBlaze.h⁠’ header file, which also includes ‘⁠blaze/Blaze.h⁠’.

  2. Import Rcpp, LinkingTo Rcpp and RcppBlaze by adding these lines to the ‘⁠DESCRIPTION⁠’ file: 

      Imports: Rcpp (>= 1.0.0)
  LinkingTo: Rcpp, RcppBlaze

  3. Link against the BLAS and LAPACK libraries, by adding following two lines in the ‘⁠Makevars⁠’ and ‘⁠Makevars.win⁠’ files: 

      PKG_CXXFLAGS=$(SHLIB_OPENMP_CXXFLAGS)
  PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS)

  4. Since there are conflicted definitions between R and blaze which is TRUE and FALSE. You have to write the initializing function for C/C++ code which the function is named after R_init_YourPackageName You can refer to our another package, https://github.com/ChingChuan-Chen/RcppLbfgsBlaze for example.

Notes

  1. If you would like to enable Boost threads support, you need to import BH package in your DESCRIPTION.

  2. CompressedVector and CompressedMatrix only support int, float and double types.

Author(s)

For RcppBlaze: Ching-Chuan Chen Maintainer: Ching-Chuan Chen <zw12356@gmail.com> For blaze: Klaus Iglberger, Georg Hager, Christian Godenschwager, Tobias Scharpff

References

  1. Blaze project: https://bitbucket.org/blaze-lib/blaze.

  2. K. Iglberger, G. Hager, J. Treibig, and U. Ruede: Expression Templates Revisited: A Performance Analysis of Current Methodologies. SIAM Journal on Scientific Computing, 34(2): C42–C69, 2012, doi:10.1137/110830125.

  3. K. Iglberger, G. Hager, J. Treibig, and U. Ruede, High Performance Smart Expression Template Math Libraries. Proceedings of the 2nd International Workshop on New Algorithms and Programming Models for the Manycore Era (APMM 2012) at HPCS 2012, doi:10.1109/HPCSim.2012.6266939.

See Also

Useful links:

Set/Get the Number of Threads used in blaze

Description

Set/Get the Number of Threads used in blaze

Usage

blaze_set_num_threads(n)

blaze_get_num_threads()

Arguments

n

The number of threads to set in blaze.

Value

blaze_get_threads returns an integer and blaze_set_threads returns nothing.

See Also

blaze wiki: https://bitbucket.org/blaze-lib/blaze/wiki/Shared%20Memory%20Parallelization.

Set/Get the random number generator for blaze with given seed

Description

Set/Get the random number generator for blaze with given seed

Usage

blaze_set_seed(seed)

blaze_get_seed()

Arguments

seed

A positive integer to specify the seed value for the random number generator.

Value

No return value.

The version of Blaze used in RcppBlaze

Description

To return the version of Blaze used in RcppBlaze.

Usage

blaze_version(single)

Arguments

single

A logical value indicates which type to return. If TRUE, it returns an integer. If FALSE, it returns a named vector.

Value

A number or a named vector to represent the version of blaze depending on the input, single.

See Also

Blaze header file blaze/system/Version.h.

Examples

blaze_version(FALSE)

linear model fitting function based on RcppBlaze

Description

fastLmPure provides the estimates of the linear model based on RcppBlaze.

Usage

fastLmPure(X, y, type)

Arguments

X

A model matrix.

y

A response vector.

type

A integer. 0 is QR solver, 1 is LDLT solver, 2 is LLT sovler and 3 is LU solver.

Details

fastLm estimates the linear model using the solve.

Value

A list containing coefficients, standard errors, rank of model matrix, degree of freedom of residuals, residuals, the standard deviation of random errors and fitted values.

Examples

# according to fastLm example in RcppArmadillo
data(trees, package="datasets")
flm <- fastLmPure(cbind(1, log(trees$Girth)), log(trees$Volume), 0)
print(flm)

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