\nomenclatureentry{AlaCholesky       decomposition~:@[{Cholesky       decomposition~:}]\begingroup In mathematics, the Cholesky decomposition is       named after Andr\'e-Louis Cholesky, who found that a symmetric       positive-definite matrix can be decomposed into a lower       triangular matrix and the transpose of the lower triangular       matrix. The lower triangular matrix is the Cholesky triangle of       the original, positive-definite matrix. Cholesky's result has       since been extended to matrices with complex       entries. (en.wikipedia.org)\nomeqref {1.0}|nompageref}{4}
\nomenclatureentry{AlaSeparator~:@[{Separator~:}]\begingroup A set of vertices that connects two disjoint parts   of the graph.\nomeqref {1.0}|nompageref}{5}
\nomenclatureentry{aBLAS~:@[{BLAS~:}]\begingroup Basic   Linear Algebra Subprograms, de facto application programming interface   standard for publishing libraries to perform basic linear algebra   operations such as vector and matrix multiplication. They were first   published in 1979, and are used to build larger packages such as   LAPACK. Heavily used in high-performance computing, highly optimized   implementations of the BLAS interface have been developed by   hardware vendors such as by Intel as well as by other authors   (e.g. ATLAS is a portable self-optimizing BLAS). The LINPACK   benchmark relies heavily on DGEMM, a BLAS subroutine, for its   performance. (en.wikipedia.org)\nomeqref {1.0}|nompageref}{6}
\nomenclatureentry{AlaGMRES~:@[{GMRES~:}]\begingroup In mathematics, the   generalized minimal residual method is an iterative method for the   numerical solution of a system of linear equations. The method   approximates the solution by the vector in a Krylov subspace with   minimal residual. The Arnoldi iteration is used to find this   vector.\nomeqref {1.0}|nompageref}{7}
\nomenclatureentry{AlaNUMA~:@[{NUMA~:}]\begingroup Non-Uniform Memory Access or   Non-Uniform Memory Architecture (NUMA) is a computer memory design   used in multiprocessors, where the memory access time depends on the   memory location relative to a processor. Under NUMA, a processor can   access its own local memory faster than non-local memory, that is,   memory local to another processor or memory shared between   processors.\nomeqref {2.0}|nompageref}{11}
