Alexander Heinecke

2.1k total citations
48 papers, 872 citations indexed

About

Alexander Heinecke is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Alexander Heinecke has authored 48 papers receiving a total of 872 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Hardware and Architecture, 19 papers in Computer Networks and Communications and 11 papers in Computational Mechanics. Recurrent topics in Alexander Heinecke's work include Parallel Computing and Optimization Techniques (33 papers), Advanced Data Storage Technologies (15 papers) and Distributed and Parallel Computing Systems (10 papers). Alexander Heinecke is often cited by papers focused on Parallel Computing and Optimization Techniques (33 papers), Advanced Data Storage Technologies (15 papers) and Distributed and Parallel Computing Systems (10 papers). Alexander Heinecke collaborates with scholars based in Germany, United States and United Kingdom. Alexander Heinecke's co-authors include Hans‐Joachim Bungartz, Greg Henry, Pradeep Dubey, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Dirk Pflüger, Hans Pabst, Dhiraj Kalamkar, Michael Bäder and Maxwell Hutchinson and has published in prestigious journals such as Journal of Chemical Theory and Computation, Journal of Computational and Applied Mathematics and Computers & Fluids.

In The Last Decade

Alexander Heinecke

48 papers receiving 844 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Heinecke Germany 16 353 303 175 142 132 48 872
Paulius Micikevicius United States 8 213 0.6× 218 0.7× 148 0.8× 89 0.6× 178 1.3× 15 735
Azzam Haidar United States 19 692 2.0× 469 1.5× 173 1.0× 89 0.6× 86 0.7× 79 1.2k
Everett Phillips United States 10 279 0.8× 240 0.8× 112 0.6× 175 1.2× 124 0.9× 14 752
Guangwen Yang China 19 437 1.2× 619 2.0× 287 1.6× 102 0.7× 184 1.4× 97 1.3k
Sam S. Stone United States 9 673 1.9× 625 2.1× 172 1.0× 73 0.5× 195 1.5× 13 1.2k
Bruno Lang Germany 17 251 0.7× 156 0.5× 141 0.8× 88 0.6× 63 0.5× 95 1.2k
Guochun Shi United States 9 584 1.7× 567 1.9× 187 1.1× 67 0.5× 183 1.4× 22 1.2k
Alfredo Buttari France 19 676 1.9× 434 1.4× 100 0.6× 232 1.6× 53 0.4× 42 1.4k
Nathan Doss United States 4 821 2.3× 877 2.9× 169 1.0× 88 0.6× 85 0.6× 4 1.4k
K. Stanley United States 7 433 1.2× 400 1.3× 87 0.5× 309 2.2× 42 0.3× 11 1.2k

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
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, et al.. (2022). FPGA-Based AI Smart NICs for Scalable Distributed AI Training Systems. IEEE Computer Architecture Letters. 21(2). 49–52. 11 indexed citations
3.
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
4.
Heinecke, Alexander, et al.. (2021). PolyDL. ACM Transactions on Architecture and Code Optimization. 18(1). 1–27. 15 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.
Kalamkar, Dhiraj, Kunal Banerjee, Sudarshan Srinivasan, et al.. (2019). Training Google Neural Machine Translation on an Intel CPU Cluster. abs 1810 4805. 1–10. 4 indexed citations
7.
Heinecke, Alexander, Alexander Breuer, & Yifeng Cui. (2019). Tensor-optimized hardware accelerates fused discontinuous Galerkin simulations. Parallel Computing. 89. 102550–102550. 2 indexed citations
8.
Venkat, Anand, et al.. (2019). ISA mapper. 164–173. 5 indexed citations
9.
Das, Dipankar, Naveen Mellempudi, Dheevatsa Mudigere, et al.. (2018). Mixed Precision Training of Convolutional Neural Networks using Integer Operations. arXiv (Cornell University). 15 indexed citations
10.
Heinecke, Alexander, Greg Henry, Maxwell Hutchinson, & Hans Pabst. (2016). LIBXSMM: Accelerating Small Matrix Multiplications by Runtime Code Generation. 981–991. 67 indexed citations
11.
Mudigere, Dheevatsa, Srinivas Sridharan, Jongsoo Park, et al.. (2015). Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 723–732. 13 indexed citations
12.
Wang, Yida, Michael J. Anderson, Jonathan D. Cohen, et al.. (2015). Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. 1–12. 10 indexed citations
13.
Heinecke, Alexander, et al.. (2015). Data mining on vast data sets as a cluster system benchmark. Concurrency and Computation Practice and Experience. 28(7). 2145–2165. 3 indexed citations
14.
Niethammer, Christoph, Stefan Becker, Martin Bernreuther, et al.. (2014). ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems. Journal of Chemical Theory and Computation. 10(10). 4455–4464. 97 indexed citations
15.
Heinecke, Alexander, et al.. (2013). Demonstrating Performance Portability of A Custom OpenCL Data Mining Application to the Intel(R) Xeon Phi(TM) Coprocessor. mediaTUM (Technical University of Munich). 2 indexed citations
16.
Heinecke, Alexander & Carsten Trinitis. (2012). Cache‐oblivious matrix algorithms in the age of multicores and many cores. Concurrency and Computation Practice and Experience. 27(9). 2215–2234. 4 indexed citations
17.
Bungartz, Hans‐Joachim, et al.. (2011). Option pricing with a direct adaptive sparse grid approach. Journal of Computational and Applied Mathematics. 236(15). 3741–3750. 25 indexed citations
18.
Bungartz, Hans‐Joachim, et al.. (2010). Parallelizing a Black-Scholes solver based on finite elements and sparse grids. 31. 1–8. 7 indexed citations
19.
Heinecke, Alexander, Carsten Trinitis, & Josef Weidendorfer. (2010). Porting existing cache-oblivious linear algebra HPC modules to larrabee architecture. 91–92. 3 indexed citations
20.
Heinecke, Alexander & Michael Bäder. (2008). Parallel matrix multiplication based on space-filling curves on shared memory multicore platforms. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 385–392. 8 indexed 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.

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