Graph expansion analysis of computational DAGs is useful for obtaining communication cost lower bounds where previous methods, such as geometric embedding, are not applicable. This has recently been demonstrated for Strassen’s and Strassen-like fast square matrix multiplication algorithms. Here we extend the expansion analysis approach to fast algorithms for rectangular matrix multiplication, obtaining a new class of communication cost lower bounds. These apply, for example to the algorithms of Bini et al. (1979) and the algorithms of Hopcroft and Kerr (1971). Some of our bounds are proved to be optimal.
The movement of data (communication) between levels of a memory hierarchy, or between parallel proce...
AbstractIn the last twenty-five years there has been much research into “fast” matrix multiplication...
International audienceCommunication lower bounds have long been established for matrix multiplicatio...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
Parallel matrix multiplication is one of the most studied fun-damental problems in distributed and h...
Parallel matrix multiplication is one of the most studied fun-damental problems in distributed and h...
A parallel algorithm has perfect strong scaling if its running time on P processors is linear in 1/P...
In this paper we study the tradeoff between parallelism and communication cost in a map-reduce compu...
We present lower bounds on the amount of communication that matrix multiplication algorithms must pe...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
Dense linear algebra computations are essential to nearly every problem in scientific computing and ...
Graph algorithms typically have very low computational intensities, hence their execution times are ...
AbstractFirst we study asymptotically fast algorithms for rectangular matrix multiplication. We begi...
Matrix multiplication (hereafter we use the acronym MM) is among the most fundamental operations of ...
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the perform...
The movement of data (communication) between levels of a memory hierarchy, or between parallel proce...
AbstractIn the last twenty-five years there has been much research into “fast” matrix multiplication...
International audienceCommunication lower bounds have long been established for matrix multiplicatio...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
Parallel matrix multiplication is one of the most studied fun-damental problems in distributed and h...
Parallel matrix multiplication is one of the most studied fun-damental problems in distributed and h...
A parallel algorithm has perfect strong scaling if its running time on P processors is linear in 1/P...
In this paper we study the tradeoff between parallelism and communication cost in a map-reduce compu...
We present lower bounds on the amount of communication that matrix multiplication algorithms must pe...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
Dense linear algebra computations are essential to nearly every problem in scientific computing and ...
Graph algorithms typically have very low computational intensities, hence their execution times are ...
AbstractFirst we study asymptotically fast algorithms for rectangular matrix multiplication. We begi...
Matrix multiplication (hereafter we use the acronym MM) is among the most fundamental operations of ...
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the perform...
The movement of data (communication) between levels of a memory hierarchy, or between parallel proce...
AbstractIn the last twenty-five years there has been much research into “fast” matrix multiplication...
International audienceCommunication lower bounds have long been established for matrix multiplicatio...