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<h3 class="likesectionHead"><a
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class="cmr-12">Abstract</span></h3>
<!--l. 7--><p class="noindent" ><span
class="cmcsc-10x-x-120">AMG4PSBLAS (A<span
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class="small-caps">a</span><span
class="small-caps">i</span><span
class="small-caps">c</span> M<span
class="small-caps">u</span><span
class="small-caps">l</span><span
class="small-caps">t</span><span
class="small-caps">i</span>G<span
class="small-caps">r</span><span
class="small-caps">i</span><span
class="small-caps">d</span> P<span
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class="small-caps">r</span><span
class="small-caps">s</span> P<span
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class="small-caps">c</span><span
class="small-caps">k</span><span
class="small-caps">a</span><span
class="small-caps">g</span><span
class="small-caps">e</span> <span
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<span
class="cmcsc-10x-x-120">PSBLAS</span><span
class="cmr-12">) is a package of parallel algebraic multilevel preconditioners included in the</span>
<span
class="cmr-12">PSCToolkit (Parallel Sparse Computation Toolkit) software framework. It is a progress</span>
<span
class="cmr-12">of a software development project started in 2007, named MLD2P4, which originally</span>
<span
class="cmr-12">implemented a multilevel version of some domain decomposition preconditioners of</span>
<span
class="cmr-12">additive-Schwarz type, and was based on a parallel decoupled version of the well known</span>
<span
class="cmr-12">smoothed aggregation method to generate the multilevel hierarchy of coarser</span>
<span
class="cmr-12">matrices. In the last years, within the context of the EU-H2020 EoCoE project</span>
<span
class="cmr-12">(Energy Oriented Center of Excellence), the package was extended for including</span>
<span
class="cmr-12">new algorithms and functionalities for the setup and application new AMG</span>
<span
class="cmr-12">preconditioners with the final aims of improving efficiency and scalability when tens of</span>
<span
class="cmr-12">thousands cores are used, and of boosting reliability in dealing with general</span>
<span
class="cmr-12">symmetric positive definite linear systems. Due to the significant number</span>
<span
class="cmr-12">of changes and the increase in scope, we decided to rename the package as</span>
<span
class="cmr-12">AMG4PSBLAS.</span>
<!--l. 16--><p class="indent" > <span
class="cmr-12">AMG4PSBLAS has been designed to provide scalable and easy-to-use</span>
<span
class="cmr-12">preconditioners in the context of the PSBLAS (Parallel Sparse Basic Linear Algebra</span>
<span
class="cmr-12">Subprograms) computational framework and can be used in conjuction with the Krylov</span>
<span
class="cmr-12">solvers available in this framework. Our package is based on a completely</span>
<span
class="cmr-12">algebraic approach; therefore users level interfaces assume that the system matrix</span>
<span
class="cmr-12">and preconditioners are represented as PSBLAS distributed sparse matrices.</span>
<span
class="cmr-12">AMG4PSBLAS enables the user to easily specify different features of an algebraic</span>
<span
class="cmr-12">multilevel preconditioner, thus allowing to experiment with different preconditioners for</span>
<span
class="cmr-12">the problem and parallel computers at hand.</span>
<!--l. 27--><p class="indent" > <span
class="cmr-12">The package employs object-oriented design techniques in Fortran</span><span
class="cmr-12"> 2003, with</span>
<span
class="cmr-12">interfaces to additional third party libraries such as MUMPS, UMFPACK, SuperLU,</span>
<span
class="cmr-12">and SuperLU</span><span
class="cmr-12">_Dist, which can be exploited in building multilevel preconditioners. The</span>
<span
class="cmr-12">parallel implementation is based on a Single Program Multiple Data (SPMD)</span>
<span
class="cmr-12">paradigm; the inter-process communication is based on MPI and is managed mainly</span>
<span
class="cmr-12">through PSBLAS.</span>
<!--l. 35--><p class="indent" > <span
class="cmr-12">This guide provides a brief description of the functionalities and the user interface</span>
<span
class="cmr-12">of AMG4PSBLAS.</span>
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