Using PLAPACK : parallel linear algebra package
著者
書誌事項
Using PLAPACK : parallel linear algebra package
(Scientific and engineering computation)
MIT, c1997
- : pbk
大学図書館所蔵 全15件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Bibliography: p. [185]-186
Includes indexes
内容説明・目次
内容説明
This book is a comprehensive introduction to all the components of a high-performance parallel linear algebra library, as well as a guide to the PLAPACK infrastructure.
PLAPACK is a library infrastructure for the parallel implementation of linear algebra algorithms and applications on distributed memory supercomputers such as the Intel Paragon, IBM SP2, Cray T3D/T3E, SGI PowerChallenge, and Convex Exemplar. This infrastructure allows library developers, scientists, and engineers to exploit a natural approach to encoding so-called blocked algorithms, which achieve high performance by operating on submatrices and subvectors. This feature, as well as the use of an alternative, more application-centric approach to data distribution, sets PLAPACK apart from other parallel linear algebra libraries, allowing for strong performance and significanltly less programming by the user. This book is a comprehensive introduction to all the components of a high-performance parallel linear algebra library, as well as a guide to the PLAPACK infrastructure. Scientific and Engineering Computation series
目次
- Part 1 Introduction: why a new infrastructure? natural description of linear algebra algorithms
- physically based matrix distribution
- redistributing and duplicating matrices and vectors
- implementation of basic matrix-vector operations (preview)
- basic linear algebra subprograms
- message-passing interface
- parallel sparse linear algebra
- FORTRAN interface
- availability. Part 2 Templates and linear algebra objects: initializing PLAPACK
- distribution templates
- linear algebra objects
- example
- return values
- more operations and information. Part 3 Advanced linear algebra object manipulation: creating views into objects
- , splitting of linear algebra objects
- shifting of linear algebra objects
- determining where to split
- creating objects 'conformal to' other objects
- annotating object orientation
- casting object types
- more operations and information. Part 4 Application program interface: introduction
- API-activation
- opening and closing an object
- accessing a vector
- accessing a matrix
- completion and synchronization
- examples
- more operations and information. Part 5 Data duplication and consolidation: copy
- reduce
- pipeline computation and communication
- a building block approach to implementing copy and reduce
- more operations and information. Part 6 Vector-vector operations: copy
- swap
- scaling a vector (object)
- scaled vector (object) addition
- inner product of vectors
- norms of vectors
- maximum absolute value in vector
- example - parallelizing inner product
- example - parallelizing 'axpy' for vector objects
- more operations and information. Part 7 Matrix-vector operations: general matrix-vector multiplication
- symmetric matrix-vector multiplication
- triangular matrix-vector multiplication
- triangular solve
- Rank-1 update
- symmetric Rank-1 update
- symmetric Rank-2 update
- example - parallelizing matrix-vector multiplication
- example parallelizing Rank-1 update
- more operations and information. Part 8 Matrix-matrix operations: general matrix-matrix multiplication
- symmetric matrix-matrix multiplication
- symmetric Rank-k update
- symmetric Rank-2k update
- triangular matrix-matrix multiplication
- triangular solve with multiple right-hand-sides
- example - parallelizing matrix-matrix multiplication
- queering algorithmic blocking size
- more operations and information. Part 9 Application of the infrastructure: Cholesky factorization
- right-looking variant
- left-looking variant
- more operations and information. Summaries: PLAPACK routines and their arguments
- BLAS related routines.
「Nielsen BookData」 より