Alexander Heinecke

48 papers receiving 844 citations

Peers

Alexander Heinecke
Comparison fields: 5 of 78
  • Hardware and Architecture 353
  • Computer Networks and Communications 303
  • Artificial Intelligence 175
  • Computational Mechanics 142
  • Computer Vision and Pattern Recognition 132
Replace Guangwen Yang with:
Guangwen Yang China
Dominik Göddeke Germany
Alfredo Buttari France
Azzam Haidar United States
Luke N. Olson United States
K. Stanley United States
James McKee United Kingdom
Ruipeng Li United States
Bruno Lang Germany
Greg Henry United States
Alexander Heinecke relative to Guangwen Yang China Guangwen Yang's profile →
Citations per field
00.5×1.5×
Guangwen Yang · 1×
Citations per year

Countries citing papers authored by Alexander Heinecke

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Heinecke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander Heinecke. 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 Alexander Heinecke. The network helps show where Alexander Heinecke may publish in the future.

Co-authorship network of co-authors of Alexander Heinecke

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Heinecke. A scholar is included among the top collaborators of Alexander Heinecke based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexander Heinecke. Alexander Heinecke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 11
3
Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads
1
4 15
5 2
6 4
7
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
15
8 13
9 10
10 3
11 5
12 97
13
Demonstrating Performance Portability of A Custom OpenCL Data Mining Application to the Intel(R) Xeon Phi(TM) Coprocessor
2
14 4
15 14
16 3
17 25
18 7
19 3
20 8

About Alexander Heinecke

Alexander Heinecke is a scholar working on Computational Mathematics, Hardware and Architecture and Computer Networks and Communications, having authored 48 papers that have together received 872 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (33 papers), Advanced Data Storage Technologies (15 papers) and Distributed and Parallel Computing Systems (10 papers). The work is most often cited by research in Hardware and Architecture (353 citations), Computational Mathematics (23 citations) and Computer Networks and Communications (303 citations). Alexander Heinecke has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Hans‐Joachim Bungartz, Greg Henry, Pradeep Dubey, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Dirk Pflüger, Hans Pabst, Dhiraj Kalamkar, Maxwell Hutchinson and Michael Bäder. Their work appears in journals such as Journal of Chemical Theory and Computation, Journal of Computational and Applied Mathematics and Computers & Fluids.

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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