We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (ii) a description of the index structure of a sparse matrix. Our approach is to embed statement instances into a Cartesian product of statement iteration and data spaces, and to produce efficient sparse code by identifying common enumerations for multiple references to sparse matrices. The approach works for imperfectly-nested codes with dependences, and produces sparse code competitive with hand-written library code for the Basic Linear Algebra Subroutines (BLAS)
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper proposes a set of Level 3 Basic Linear Algebra Subprograms and associated kernels for sp...
Abstract. We present compiler technology for generating sparse matrix code from (i) dense matrix cod...
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...
Sparse matrices are stored in compressed formats in which zeros are not stored explicitly. Writing h...
Sparse matrix formats encode very large numerical matrices with relatively few nonzeros. They are ty...
Sparse matrices are stored in compressed formats in which zeros are not stored explicitly. Writing h...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
This paper shows how to compile sparse array programming languages. A sparse array programming langu...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
Automatic program comprehension techniques have been shown to improve automatic parallelization of d...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper proposes a set of Level 3 Basic Linear Algebra Subprograms and associated kernels for sp...
Abstract. We present compiler technology for generating sparse matrix code from (i) dense matrix cod...
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...
Sparse matrices are stored in compressed formats in which zeros are not stored explicitly. Writing h...
Sparse matrix formats encode very large numerical matrices with relatively few nonzeros. They are ty...
Sparse matrices are stored in compressed formats in which zeros are not stored explicitly. Writing h...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
This paper shows how to compile sparse array programming languages. A sparse array programming langu...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
Automatic program comprehension techniques have been shown to improve automatic parallelization of d...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper proposes a set of Level 3 Basic Linear Algebra Subprograms and associated kernels for sp...