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<h3 class="sectionHead"><span class="titlemark">1 </span> <a
id="x4-30001"></a>Introduction</h3>
<!--l. 60--><p class="noindent" >The PSBLAS library, developed with the aim to facilitate the parallelization of
<!--l. 59--><p class="noindent" >The PSBLAS library, developed with the aim to facilitate the parallelization of
computationally intensive scientific applications, is designed to address parallel
implementation of iterative solvers for sparse linear systems through the
distributed memory paradigm. It includes routines for multiplying sparse
@@ -27,11 +27,11 @@ diagonal entries, preprocessing sparse matrices, and contains additional
routines for dense matrix operations. The current implementation of PSBLAS
addresses a distributed memory execution model operating with message
passing.
<!--l. 71--><p class="indent" > The PSBLAS library version 3 is implemented in the Fortran&#x00A0;2008&#x00A0;<span class="cite">[<a
<!--l. 70--><p class="indent" > The PSBLAS library version 3 is implemented in the Fortran&#x00A0;2008&#x00A0;<span class="cite">[<a
href="userhtmlli3.html#Xmetcalf">17</a>]</span>
programming language, with reuse and/or adaptation of existing Fortran&#x00A0;77 and
Fortran&#x00A0;95 software, plus a handful of C routines.
<!--l. 76--><p class="indent" > The use of Fortran&#x00A0;2008 offers a number of advantages over Fortran&#x00A0;95, mostly
<!--l. 75--><p class="indent" > The use of Fortran&#x00A0;2008 offers a number of advantages over Fortran&#x00A0;95, mostly
in the handling of requirements for evolution and adaptation of the library to new
computing architectures and integration of new algorithms. For a detailed
discussion of our design see&#x00A0;<span class="cite">[<a
@@ -42,7 +42,7 @@ href="userhtmlli3.html#XRouXiaXu:11">19</a>]</span>; sufficient support for Fort
from many compilers, including recent versions of the GNU Fortran compiler from
the Free Software Foundation, and the FLANG compiler from the LLVM
project.
<!--l. 88--><p class="indent" > Previous approaches have been based on mixing Fortran&#x00A0;95, with its support for
<!--l. 87--><p class="indent" > Previous approaches have been based on mixing Fortran&#x00A0;95, with its support for
object-based design, with other languages; these have been advocated by a number
of authors, e.g.&#x00A0;<span class="cite">[<a
href="userhtmlli3.html#Xmachiels">16</a>]</span>. Moreover, the Fortran&#x00A0;95 facilities for dynamic memory
@@ -50,7 +50,7 @@ management and interface overloading greatly enhance the usability of the PSBLAS
subroutines. In this way, the library can take care of runtime memory requirements
that are quite difficult or even impossible to predict at implementation or
compilation time.
<!--l. 98--><p class="indent" > The presentation of the PSBLAS library follows the general structure of the
<!--l. 97--><p class="indent" > The presentation of the PSBLAS library follows the general structure of the
proposal for serial Sparse BLAS&#x00A0;<span class="cite">[<a
href="userhtmlli3.html#Xsblas97">8</a>,&#x00A0;<a
href="userhtmlli3.html#Xsblas02">9</a>]</span>, which in its turn is based on the proposal for
@@ -58,7 +58,7 @@ BLAS on dense matrices&#x00A0;<span class="cite">[<a
href="userhtmlli3.html#XBLAS1">15</a>,&#x00A0;<a
href="userhtmlli3.html#XBLAS2">5</a>,&#x00A0;<a
href="userhtmlli3.html#XBLAS3">6</a>]</span>.
<!--l. 103--><p class="indent" > The applicability of sparse iterative solvers to many different areas causes
<!--l. 102--><p class="indent" > The applicability of sparse iterative solvers to many different areas causes
some terminology problems because the same concept may be denoted
through different names depending on the application area. The PSBLAS
features presented in this document will be discussed referring to a finite
@@ -67,7 +67,7 @@ the scope of the library is wider than that: for example, it can be applied
to finite element discretizations of PDEs, and even to different classes of
problems such as nonlinear optimization, for example in optimal control
problems.
<!--l. 113--><p class="indent" > The design of a solver for sparse linear systems is driven by many conflicting
<!--l. 112--><p class="indent" > The design of a solver for sparse linear systems is driven by many conflicting
objectives, such as limiting occupation of storage resources, exploiting regularities in
the input data, exploiting hardware characteristics of the parallel platform. To
@@ -88,12 +88,12 @@ applications.
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