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Beautiful Code by Andy Oram, Greg Wilson

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ScaLAPACK PDGETRF

LAPACK is designed to be highly efficient on vector processors, high-performance "super-scalar" workstations, and shared-memory multiprocessors. LAPACK can also be used sat-isfactorily on all types of scalar machines (PCs, workstations, and mainframes). However, LAPACK in its present form is less likely to give good performance on other types of parallel architectures—for example, massively parallel Single Instruction Multiple Data (SIMD) machines, or Multiple Instruction Multiple Data (MIMD) distributed-memory machines. The ScaLAPACK effort was intended to adapt LAPACK to these new architectures.

By creating the ScaLAPACK software library, we extended the LAPACK library to scalable MIMD, distributed-memory, concurrent computers. For such machines, the memory hierarchy includes the off-processor memory of other processors, in addition to the hierarchy of registers, cache, and local memory on each processor.

Like LAPACK, the ScaLAPACK routines are based on block-partitioned algorithms in order to minimize the frequency of data movement between different levels of the memory hierarchy. The fundamental building blocks of the ScaLAPACK library are distributed-memory versions of the Level-2 and Level-3 BLAS, and a set of Basic Linear Algebra Communication Subprograms (BLACS) for communication tasks that arise frequently in parallel linear algebra computations. In the ScaLAPACK routines, all interprocessor communication occurs within the distributed BLAS and the BLACS, ...

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