Abstract. We present compiler technology for generating sparse matrix code from (i) dense matrix code and (ii) a description of the indexing structure of the sparse matrices. This technology embeds statement instances into a Carte-sian product of statement iteration and data spaces, and produces efcient sparse code by identifying common enumerations for multiple references to sparse ma-trices. This approach works for imperfectly-nested codes with dependences, and produces sparse code competitive with hand-written library code.
Space-efficient data structures for sparse matrices are an important concept in numerical programmin...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
International audienceIn this paper, we propose a generic method of automatic parallelization for sp...
We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
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...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
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...
Automatic program comprehension techniques have been shown to improve automatic parallelization of d...
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 ...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
Space-efficient data structures for sparse matrices are an important concept in numerical programmin...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
International audienceIn this paper, we propose a generic method of automatic parallelization for sp...
We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze ...
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...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
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...
Automatic program comprehension techniques have been shown to improve automatic parallelization of d...
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 ...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
Space-efficient data structures for sparse matrices are an important concept in numerical programmin...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
International audienceIn this paper, we propose a generic method of automatic parallelization for sp...