Sparse matrix multiplication: The distributed block-compressed sparse row library
نویسندگان
چکیده
منابع مشابه
Sparse matrix multiplication: The distributed block-compressed sparse row library
Efficient parallel multiplication of sparse matrices is key to enabling many large-scale calculations. This article presents the DBCSR (Distributed Block Compressed Sparse Row) library for scalable sparse matrix-matrix multiplication and its use in the CP2K program for linear-scaling quantum-chemical calculations. The library combines several approaches to implement sparse matrix multiplication...
متن کاملBlock-Row Sparse Matrix-Vector Multiplication on SIMD Machines
The irregular nature of the data structures required to efficiently store arbitrary sparse matrices and the architectural constraints of a SIMD computer make it difficult to design an algorithm that can efficiently multiply an arbitrary sparse matrix by a vector. A new ‘‘block-row’’ algorithm is proposed. It allows the ‘‘regularity’’ of a data structure with a row-major mapping to be varied by ...
متن کاملGPU-Accelerated Sparse Matrix-Matrix Multiplication by Iterative Row Merging
We present an algorithm for general sparse matrix-matrix multiplication (SpGEMM) on many-core architectures, such as GPUs. SpGEMM is implemented by iterative row merging, similar to merge sort, except that elements with duplicate column indices are aggregated on the fly. The main kernel merges small numbers of sparse rows at once using sub-warps of threads to realize an early compression effect...
متن کاملVectorized Sparse Matrix Multiply for Compressed Row Storage Format
The innovation of this work is a simple vectorizable algorithm for performing sparse matrix vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be easily adapted to a sophisticated library framework such as PETSc. Numerical experiments on the Cray X1 show an order of magnitude ...
متن کاملBlock Algorithms for Sparse Matrix by Dense Matrix Multiplication
Sparse matrix computations appear in many linear algebra kernels of scienti c applications. The study, evaluation and optimization of sparse matrix codes is more complex than the dense case. Moreover, the irregularity of some memory accesses and the a-priory lack of knowledge of the number of iterations to be perfomed in some loops (both depending on the sparsity pettern) limit the succes of pr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Parallel Computing
سال: 2014
ISSN: 0167-8191
DOI: 10.1016/j.parco.2014.03.012