Visible to Intel only — GUID: GUID-B5E36638-0A9C-491D-B45A-1EA3A3949DD3
Visible to Intel only — GUID: GUID-B5E36638-0A9C-491D-B45A-1EA3A3949DD3
Sparse BLAS Level 1 Routines
This section describes Sparse BLAS Level 1, an extension of BLAS Level 1 included in the Intel® oneAPI Math Kernel Library beginning with the Intel® oneAPI Math Kernel Library (oneMKL) release 2.1. Sparse BLAS Level 1 is a group of routines and functions that perform a number of common vector operations on sparse vectors stored in compressed form.
Sparse vectors are those in which the majority of elements are zeros. Sparse BLAS routines and functions are specially implemented to take advantage of vector sparsity. This allows you to achieve large savings in computer time and memory. If nz is the number of non-zero vector elements, the computer time taken by Sparse BLAS operations will be O(nz).
- Vector Arguments
- Naming Conventions for Sparse BLAS Routines
- Routines and Data Types
- BLAS Level 1 Routines That Can Work With Sparse Vectors
- ?axpyi
Adds a scalar multiple of compressed sparse vector to a full-storage vector. - ?doti
Computes the dot product of a compressed sparse real vector by a full-storage real vector. - ?dotci
Computes the conjugated dot product of a compressed sparse complex vector with a full-storage complex vector. - ?dotui
Computes the dot product of a compressed sparse complex vector by a full-storage complex vector. - ?gthr
Gathers a full-storage sparse vector's elements into compressed form. - ?gthrz
Gathers a sparse vector's elements into compressed form, replacing them by zeros. - ?roti
Applies Givens rotation to sparse vectors one of which is in compressed form. - ?sctr
Converts compressed sparse vectors into full storage form.