James Demmel

282 papers receiving 16.0k citations

Hit Papers

A view of the parallel computing landscape 2009 · 369 citations
369198720262000201350010001.5k2.0k

Peers

James Demmel
Comparison fields: 5 of 166
  • Computational Mathematics 715
  • Hardware and Architecture 4.8k
  • Numerical Analysis 2.6k
  • Computational Theory and Mathematics 6.8k
  • Computer Networks and Communications 4.4k
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Jack Dongarra United States
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Countries citing papers authored by James Demmel

Since Specialization
Citations

This map shows the geographic impact of James Demmel's research. 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 James Demmel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Demmel more than expected).

Fields of papers citing papers by James Demmel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Demmel. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James Demmel. The network helps show where James Demmel may publish in the future.

Co-authorship network

The 25 scholars most cited alongside James Demmel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James Demmel Line = papers co-authored together James Demmel links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20243
2
ImageNet Training by CPU: AlexNet in 11 Minutes and ResNet-50 in 48 Minutes
20170
3
100-epoch ImageNet Training with AlexNet in 24 Minutes.
201722
4
Asynchronous Parallel Greedy Coordinate Descent
201615
5 20155
6 201337
7 200925
8 200829
9
Error Bounds from Extra Precise Iterative Refinement
20052
10 200439
11
Practical Techniques for Measuring MEMS Properties
200418
12 200282
13
LAPACK Users' guide (third ed.)
1999139
14 1999171
15 199810
16
The Performance of Finding Eigenvalues and Eigenvaectors of Dense Symmetric Matrices on Distributed Memory Computers.
199510
17
On the correctness of some bisection-like parallel eigenvalue algorithms in floating point arithmetic.
199512
18
Design of a Parallel Nonsymmetric Eigenroutine Toolbox, Part I
199253
19 1988104
20 19887

About James Demmel

James Demmel is a scholar working on Computational Mathematics, Hardware and Architecture, Computational Theory and Mathematics, Numerical Analysis and Computer Networks and Communications, having authored 289 papers that have together received 17.8k indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (111 papers), Parallel Computing and Optimization Techniques (106 papers), Numerical Methods and Algorithms (46 papers), Advanced Optimization Algorithms Research (38 papers), Interconnection Networks and Systems (36 papers), Distributed and Parallel Computing Systems (33 papers), Stochastic Gradient Optimization Techniques (23 papers) and Tensor decomposition and applications (19 papers). The work is most often cited by research in Computational Mathematics (715 citations), Hardware and Architecture (4.8k citations), Numerical Analysis (2.6k citations), Computational Theory and Mathematics (6.8k citations) and Computer Networks and Communications (4.4k citations). James Demmel has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Jack Dongarra, Xiaoye Sherry Li, Henk van der Vorst, Zhaojun Bai, В. М. Волков, Richard Vuduc, Victor Eijkhout, Roldan Pozo, J.M. Donato and C.H. Romine. Their work appears in journals such as SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, ACM Transactions on Mathematical Software, Linear Algebra and its Applications and SIAM Journal on Numerical Analysis.

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.

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