Add to ORKGThesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Computer Science, January 2013.Sparse matrix-vector multiplication (spMV) is a kernel operation in scientific com- putation. There exist problems where a matrix is repeatedly multiplied by many different vectors. For such problems, specializing the spMV code based on the matrix has the potential of producing significantly faster code. This, in fact, has been one of the motivational examples of program generation. Using program generation, spMV code can be unfolded fully to eliminate loop overheads as well as enable high-impact optimizations. In this work we focus on specialization of spMV by unfolding the code according to a given matrix. We provid...
Abstract—In this paper we present two algorithms for perform-ing sparse matrix-dense vector multipli...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
In this thesis, performance of two important primitives, namely sparse and banded matrix – multiple ...
Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Comput...
The multiplication of a sparse matrix with a dense vector is a performance critical computational ke...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract. Sparse Matrix Vector multiplication (SpMV) is one of the most important operation for exac...
The SpMV operation -- the multiplication of a sparse matrix with a dense vector -- is used in many s...
Runtime specialization optimizes programs based on partial infor-mation available only at run time. ...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse matrix-vector multiplication (shortly SpM×V) is an important building block in algorithms sol...
AbstractThe sparse matrix-vector multiplication (SpMV) is a fundamental kernel used in computational...
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications f...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract—In this paper we present two algorithms for perform-ing sparse matrix-dense vector multipli...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
In this thesis, performance of two important primitives, namely sparse and banded matrix – multiple ...
Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Comput...
The multiplication of a sparse matrix with a dense vector is a performance critical computational ke...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract. Sparse Matrix Vector multiplication (SpMV) is one of the most important operation for exac...
The SpMV operation -- the multiplication of a sparse matrix with a dense vector -- is used in many s...
Runtime specialization optimizes programs based on partial infor-mation available only at run time. ...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse matrix-vector multiplication (shortly SpM×V) is an important building block in algorithms sol...
AbstractThe sparse matrix-vector multiplication (SpMV) is a fundamental kernel used in computational...
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications f...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Abstract—In this paper we present two algorithms for perform-ing sparse matrix-dense vector multipli...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
In this thesis, performance of two important primitives, namely sparse and banded matrix – multiple ...