We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and dense) as distributed relations and parallel loop execution as distributed relational query evaluation. This approachprovides for a uniform treatment of arbitrary sparse matrix formats and partitioning information formats. The relational algebra view of computation and communication sets provides new opportunities for the optimization of node program performance and the reduction of communucation set generation and index translation overhead
This work presents a novel strategy for the parallelization of applications containing sparse matrix...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
Sparse matrix computations are ubiquitous in computational science. However, the development of high...
We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (...
Sparse matrix formats encode very large numerical matrices with relatively few nonzeros. They are ty...
Introduction A wide range of applications make use of regular dynamic data structures. Dynamic data...
Applications that manipulate sparse data structures contain memory reference patterns that are un-kn...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
. The paper describes a parallel algorithm for the LU factorization of sparse matrices on distribute...
Abstract—Vienna Fortran, High Performance Fortran (HPF), and other data parallel languages have been...
Abstract. We present compiler technology for generating sparse matrix code from (i) dense matrix cod...
Many important applications including machine learning, molecular dynamics, and computational fluid ...
This work presents a novel strategy for the parallelization of applications containing sparse matrix...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
Sparse matrix computations are ubiquitous in computational science. However, the development of high...
We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (...
Sparse matrix formats encode very large numerical matrices with relatively few nonzeros. They are ty...
Introduction A wide range of applications make use of regular dynamic data structures. Dynamic data...
Applications that manipulate sparse data structures contain memory reference patterns that are un-kn...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
. The paper describes a parallel algorithm for the LU factorization of sparse matrices on distribute...
Abstract—Vienna Fortran, High Performance Fortran (HPF), and other data parallel languages have been...
Abstract. We present compiler technology for generating sparse matrix code from (i) dense matrix cod...
Many important applications including machine learning, molecular dynamics, and computational fluid ...
This work presents a novel strategy for the parallelization of applications containing sparse matrix...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...