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We investigate the problem of symmetrically permuting a square sparse matrix into a block diagonal form with overlap. This permutation problem arises in the parallelization of an explicit formulation of the multiplicative Schwarz preconditioner and a more recent block overlapping banded linear solver as well as its application to general sparse linear(More)
Intelligent partitioning models are commonly used for efficient parallelization of irregular applications on distributed systems. These models usually aim to minimize a single communication cost metric, which is either related to communication volume or message count. However, both volume- and message-related metrics should be taken into account during(More)
We propose a comprehensive and generic framework to minimize multiple and different volume-based communication cost metrics for sparse matrix dense matrix multiplication (SpMM). SpMM is an important kernel that finds application in computational linear algebra and big data analytics. On distributed memory systems, this kernel is usually characterized with(More)
Efficient parallelization of the applications in scientific computing domain on distributed systems requires reducing the communication costs of the application in both terms: bandwidth and latency costs. Although there are many graph/hypergraph partitioning models that reduce the bandwidth cost in the literature, there exist only a few works that consider(More)
In this whitepaper, we describe the problem of permuting sparse square matrices into block diagonal form with overlap (BDO) and propose a graph partitioning algorithm for solving this problem. A block diagonal matrix with overlap is a block diagonal matrix whose consecutive diagonal blocks may overlap. The objective in this permutation problem is to(More)
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