scholarly journals Toolboxes and Templates for Large Scale Linear Algebra Problems

2002 ◽  
Author(s):  
Zhaojun Bai ◽  
James Demmel ◽  
Jack Dongarra
Keyword(s):  
Linear Algebra ◽  
Large Scale

Graph Programming Interface (GPI): A Linear Algebra Programming Model for Large Scale Graph Computations

2017 ◽  
Vol 46 (2) ◽  
pp. 412-440 ◽  
Author(s):  
William Horn ◽  
Manoj Kumar ◽  
Joefon Jann ◽  
José Moreira ◽  
Pratap Pattnaik ◽  
...  
Keyword(s):  
Linear Algebra ◽  
Large Scale ◽  
Programming Model ◽  
Programming Interface

Compressed linear algebra for large-scale machine learning

2017 ◽  
Vol 27 (5) ◽  
pp. 719-744 ◽  
Author(s):  
Ahmed Elgohary ◽  
Matthias Boehm ◽  
Peter J. Haas ◽  
Frederick R. Reiss ◽  
Berthold Reinwald
Keyword(s):  
Machine Learning ◽  
Linear Algebra ◽  
Large Scale

Using desktop computers to solve large-scale dense linear algebra problems

2010 ◽  
Vol 58 (2) ◽  
pp. 145-150 ◽  
Author(s):  
M. Marqués ◽  
G. Quintana-Ortí ◽  
E. S. Quintana-Ortí ◽  
R. van de Geijn
Keyword(s):  
Linear Algebra ◽  
Large Scale ◽  
Dense Linear Algebra ◽  
Desktop Computers

Optimizing Sparse Linear Algebra for Large-Scale Graph Analytics

Computer ◽  
2015 ◽  
Vol 48 (8) ◽  
pp. 26-34 ◽  
Author(s):  
Daniele Buono ◽  
John A. Gunnels ◽  
Xinyu Que ◽  
Fabio Checconi ◽  
Fabrizio Petrini ◽  
...  
Keyword(s):  
Linear Algebra ◽  
Large Scale ◽  
Graph Analytics ◽  
Sparse Linear Algebra

A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines

2021 ◽  
Vol 47 (3) ◽  
pp. 1-23
Author(s):  
Ahmad Abdelfattah ◽  
Timothy Costa ◽  
Jack Dongarra ◽  
Mark Gates ◽  
Azzam Haidar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Algebra ◽  
High Performance ◽  
Large Scale ◽  
Floating Point ◽  
Equal Size ◽  
Hardware Accelerators ◽  
Double Precision ◽  
Basic Linear Algebra Subprograms ◽  
Many Core

This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The matrices are grouped together in uniformly sized groups, with just one group if all the matrices are of equal size. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance many-core platforms. These include multicore and many-core CPU processors, GPUs and coprocessors, and other hardware accelerators with floating-point compute facility. As well as the standard types of single and double precision, we also include half and quadruple precision in the standard. In particular, half precision is used in many very large scale applications, such as those associated with machine learning.

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