Ecient Sparse Matrix-Vector Multiplication on CUDA
Impact in
Classified as
- Authors
- Nathan BellMichael Garland
In The Last Decade
doi.org/w53546128 →Countries where authors are citing Ecient Sparse Matrix-Vector Multiplication on CUDA
This map shows the geographic impact of Ecient Sparse Matrix-Vector Multiplication on CUDA. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ecient Sparse Matrix-Vector Multiplication on CUDA with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ecient Sparse Matrix-Vector Multiplication on CUDA more than expected).
Fields of papers citing Ecient Sparse Matrix-Vector Multiplication on CUDA
This network shows the impact of Ecient Sparse Matrix-Vector Multiplication on CUDA. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Ecient Sparse Matrix-Vector Multiplication on CUDA.
About Ecient Sparse Matrix-Vector Multiplication on CUDA
This paper, published in 2008, received 374 indexed citations . Written by Nathan Bell and Michael Garland covering the research area of Hardware and Architecture, Computational Theory and Mathematics and Computer Networks and Communications. It is primarily cited by scholars working on Hardware and Architecture (184 citations), Computer Networks and Communications (150 citations), Computational Theory and Mathematics (111 citations), Computational Mechanics (82 citations) and Computer Vision and Pattern Recognition (58 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w53546128.