Hans Pabst

4.5k total citations
10 papers, 140 citations indexed

About

Hans Pabst is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Hans Pabst has authored 10 papers receiving a total of 140 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Hardware and Architecture, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Hans Pabst's work include Parallel Computing and Optimization Techniques (8 papers), Advanced Neural Network Applications (4 papers) and Tensor decomposition and applications (3 papers). Hans Pabst is often cited by papers focused on Parallel Computing and Optimization Techniques (8 papers), Advanced Neural Network Applications (4 papers) and Tensor decomposition and applications (3 papers). Hans Pabst collaborates with scholars based in Germany, United States and United Kingdom. Hans Pabst's co-authors include Alexander Heinecke, Greg Henry, Maxwell Hutchinson, Dhiraj Kalamkar, Sasikanth Avancha, Evangelos Georganas, Kunal Banerjee, Michael Lass, Thomas D. Kühne and Hossam Elgabarty and has published in prestigious journals such as Parallel Computing, Frontiers in Applied Mathematics and Statistics and arXiv (Cornell University).

In The Last Decade

Hans Pabst

9 papers receiving 139 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hans Pabst Germany	4	74	43	43	36	20	10	140
Tobias Kenter Germany	10	105 1.4×	68 1.6×	30 0.7×	8 0.2×	53 2.6×	40	218
Deborah Bard United States	6	31 0.4×	50 1.2×	30 0.7×	37 1.0×	12 0.6×	11	135
Alexander McCaskey United States	8	47 0.6×	36 0.8×	236 5.5×	11 0.3×	41 2.0×	19	283
Peter Boyle United Kingdom	5	128 1.7×	139 3.2×	18 0.4×	9 0.3×	40 2.0×	5	225
Jim Kowalkowski United States	8	24 0.3×	87 2.0×	47 1.1×	15 0.4×	6 0.3×	41	206
P. Calafiura United States	10	37 0.5×	128 3.0×	45 1.0×	9 0.3×	11 0.6×	37	240
Mary Flahive United States	7	13 0.2×	68 1.6×	37 0.9×	13 0.4×	32 1.6×	13	240
Philippe Canal United States	7	35 0.5×	96 2.2×	25 0.6×	7 0.2×	7 0.3×	42	152
Kaitlin N. Smith United States	11	47 0.6×	20 0.5×	290 6.7×	7 0.2×	65 3.3×	33	334
W. Lavrijsen United States	7	71 1.0×	61 1.4×	104 2.4×	3 0.1×	38 1.9×	21	222

Countries citing papers authored by Hans Pabst

Since Specialization

Citations

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

Fields of papers citing papers by Hans Pabst

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hans Pabst

This figure shows the co-authorship network connecting the top 25 collaborators of Hans Pabst. A scholar is included among the top collaborators of Hans Pabst 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 Hans Pabst. Hans Pabst is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Georganas, Evangelos, Dhiraj Kalamkar, К. В. Воронин, et al.. (2024). Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. 950–963. 1 indexed citations

2.

Georganas, Evangelos, Dhiraj Kalamkar, Sasikanth Avancha, et al.. (2022). Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads. Frontiers in Applied Mathematics and Statistics. 8. 1 indexed citations

3.

Schade, Robert R., Tobias Kenter, Hossam Elgabarty, et al.. (2022). Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms. Parallel Computing. 111. 102920–102920. 27 indexed citations

4.

Georganas, Evangelos, Dhiraj Kalamkar, Sasikanth Avancha, et al.. (2021). Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads. arXiv (Cornell University). 1 indexed citations

5.

Georganas, Evangelos, Kunal Banerjee, Dhiraj Kalamkar, et al.. (2020). Harnessing Deep Learning via a Single Building Block. 222–233. 16 indexed citations

6.

Carminati, Federico, S. Vallecorsa, Damian Podareanu, et al.. (2020). Generative Adversarial Networks for Fast Simulation: distributed training and generalisation. 12–12. 3 indexed citations

7.

Georganas, Evangelos, Sasikanth Avancha, Kunal Banerjee, et al.. (2018). Anatomy of high-performance deep learning convolutions on SIMD architectures. 66. 21 indexed citations

8.

Heinecke, Alexander, Greg Henry, Maxwell Hutchinson, & Hans Pabst. (2016). LIBXSMM: Accelerating Small Matrix Multiplications by Runtime Code Generation. 981–991. 67 indexed citations

9.

Pabst, Hans, et al.. (2012). Performance of a Structure-Detecting SpMV Using the CSR Matrix Representation. 3–10. 3 indexed citations

10.

Pabst, Hans, et al.. (1980). Brecht und die Religion. South Atlantic Bulletin. 45(2). 79–79.

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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