Due to copyright restrictions, the access to the full text of this article is only available via subscription.Bu çalışmada seyrek matris-vektor çarpımı için matris içeriğine göre özelleşmiş, yüksek hızlı program üretimi yapan bir kütüphane tasarımı anlatılmaktadır. Kütüphane sinyal işleme uygulamaları, bilimsel hesaplamalar, sonlu eleman analizi gibi mühendislik problemlerinde kullanılan büyük matrisler için kod üretimine olanak verir. Üretilen kod, pek çok seçenek arasından, deneysel optimizasyon yöntemiyle seçilir. Bu sayede koşumun gerçekleştiği makineye en uygun seçimin yapılması hedeflenir.TÜBİTAK ; NS
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...
Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Comput...
Runtime specialization optimizes programs based on partial infor-mation available only at run time. ...
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...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Application performance dominated by a few computational kernels Performance tuning today Vendor-tun...
Abstract. Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widel...
Sparse matrix-vector multiplications are essential in the numerical resolution of partial differenti...
this paper. We would also like to thank Rolf Strebel for explanatory discussions on the subject of s...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...
Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Comput...
Runtime specialization optimizes programs based on partial infor-mation available only at run time. ...
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...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Application performance dominated by a few computational kernels Performance tuning today Vendor-tun...
Abstract. Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widel...
Sparse matrix-vector multiplications are essential in the numerical resolution of partial differenti...
this paper. We would also like to thank Rolf Strebel for explanatory discussions on the subject of s...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...
Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the...