Year: 2022
Type: conference-paper
Abstract: Sparse computations, such as sparse matrix-dense vector multiplication, are notoriously hard to optimize due to their irregularity and memory-boundedness. Solutions to improve the performance of sparse computations have been proposed, ranging from hardware-based such as gather-scatter instructions, to software ones such as generalized and dedicated sparse formats, used together with specialized executor programs for different hardware targets. These sparse computations are often performed on read-only sparse structures: while the data themselves are variable, the sparsity structure itself does not change. Indeed, sparse formats such as CSR have a typically high cost to insert/remove nonzero elements in the representation. The typical use case is to not modify the sparsity during possibly repeated computations on the same sparse structure.
Institutions: Universidade da Coruña, Colorado State University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G1071056811, G1496055848, G1855990336, G2262748287, G2530961444, G2660406951, G3480869486, G3518538256, G3690828678, G3876637791, G425668148, G5421974903, G6056837937, G7084143925, G7183395120, G726424357, CAREER: Staging Compilers for Heterogeneous Platforms, Program Optimization with Data-Specific Compilation, G8684520763
OpenAlex ID: W4318256793