A Novel Partitioning Method for Accelerating the Block Cimmino Algorithm

We propose a novel block-row partitioning method in order to improve the convergence rate of the block Cimmino algorithm for solving general sparse linear systems of equations. The convergence rate of the block Cimmino algorithm depends on the orthogonality among the block rows obtained by the partitioning method. The proposed method takes numerical orthogonality among block rows into account by proposing a row inner-product graph model of the coefficient matrix. In the graph partitioning formulation defined on this graph model, the partitioning objective of minimizing the cutsize directly corresponds to minimizing the sum of inter-block inner products between block rows thus leading to an improvement in the eigenvalue spectrum of the iteration matrix. This in turn leads to a significant reduction in the number of iterations required for convergence. Extensive experiments conducted on a large set of matrices confirm the validity of the proposed method against a state-of-the-art method

A domain decomposing parallel sparse linear system solver

The solution of large sparse linear systems is often the most time-consuming part of many science and engineering applications. Computational fluid dynamics, circuit simulation, power network analysis, and material science are just a few examples of the application areas in which large sparse linear systems need to be solved effectively. In this paper we introduce a new parallel hybrid sparse linear system solver for distributed memory architectures that contains both direct and iterative components. We show that by using our solver one can alleviate the drawbacks of direct and iterative solvers, achieving better scalability than with direct solvers and more robustness than with classical preconditioned iterative solvers. Comparisons to well-known direct and iterative solvers on a parallel architecture are provided.Comment: To appear in Journal of Computational and Applied Mathematic

Parallel scalable PDE-constrained optimization: antenna identification in hyperthermia cancer treatment planning

We present aPDE-constrained optimization algorithm which is designed for parallel scalability on distributed-memory architectures with thousands of cores. The method is based on aline-search interior-point algorithm for large-scale continuous optimization, it is matrix-free in that it does not require the factorization of derivative matrices. Instead, it uses anew parallel and robust iterative linear solver on distributed-memory architectures. We will show almost linear parallel scalability results for the complete optimization problem, which is anew emerging important biomedical application and is related to antenna identification in hyperthermia cancer treatment plannin

Parallel minimum norm solution of sparse block diagonal column overlapped underdetermined systems

Underdetermined systems of equations in which the minimum norm solution needs to be computed arise in many applications, such as geophysics, signal processing, and biomedical engineering. In this article, we introduce a new parallel algorithm for obtaining the minimum 2-norm solution of an underdetermined system of equations. The proposed algorithm is based on the Balance scheme, which was originally developed for the parallel solution of banded linear systems. The proposed scheme assumes a generalized banded form where the coefficient matrix has column overlapped block structure in which the blocks could be dense or sparse. In this article, we implement the more general sparse case. The blocks can be handled independently by any existing sequential or parallel QR factorization library. A smaller reduced system is formed and solved before obtaining the minimum norm solution of the original system in parallel. We experimentally compare and confirm the error bound of the proposed method against the QR factorization based techniques by using true single-precision arithmetic. We implement the proposed algorithm by using the message passing paradigm. We demonstrate numerical effectiveness as well as parallel scalability of the proposed algorithm on both shared and distributed memory architectures for solving various types of problems. © 2017 ACM

Short report. The AIDIT and IMPACT conference 2006: Outcomes and future directions

IMPACT (Identification of Men with a genetic predisposition to ProstAte Cancer: Targeted screening in BRCA1/2 mutation carriers and controls) is an international collaboration investigating the utility of targeted prostate-specific antigen (PSA) screening for men at increased risk of prostate cancer due to inherited predisposition. Although the majority of prostate cancer occurs sporadically, it is recognized that family history plays a role in a significant number of cases: a family history either of prostate cancer alone, or of other cancers including breast and ovarian cancer. Evidence of the link between single genes and prostate cancer risk is strongest for the BRCA1 and BRCA2 genes, with BRCA2 in particular thought to lead to a relative risk of 4.65 (95%CI 3.48-6.22). This relative risk may be as high as 7.33 in men under the age of 65 years

Lack of association between RNASEL Arg462Gln variant and the risk of breast cancer

Background: The RNASEL G1385A variant was recently found to be implicated in the development of prostate cancer. Considering the function of RNase L and the pleiotropic effects of mutations associated with cancer, we sought to investigate whether the RNASEL G1385A variant is a risk factor for breast cancer. Patients and Methods: A total of 453 breast cancer patients and 382 age- and sex-matched controls from Greece and Turkey were analyzed. Genotyping for the RNASEL G1385A variant was performed using an Amplification Refractory Mutation System (ARMS). Results: Statistical evaluation of the RNASEL G1385A genotype distribution among breast cancer patients and controls revealed no significant association between the presence of the risk genotype and the occurrence of breast cancer. Conclusion: Although an increasing number of studies report an association between the RNASEL G1385A variant and prostate cancer risk, this variant does not appear to be implicated in the development of breast cancer