Azzam Haidar

2.6k total citations
79 papers, 1.2k citations indexed

About

Azzam Haidar is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computational Theory and Mathematics. According to data from OpenAlex, Azzam Haidar has authored 79 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Hardware and Architecture, 44 papers in Computer Networks and Communications and 32 papers in Computational Theory and Mathematics. Recurrent topics in Azzam Haidar's work include Parallel Computing and Optimization Techniques (66 papers), Matrix Theory and Algorithms (31 papers) and Distributed and Parallel Computing Systems (22 papers). Azzam Haidar is often cited by papers focused on Parallel Computing and Optimization Techniques (66 papers), Matrix Theory and Algorithms (31 papers) and Distributed and Parallel Computing Systems (22 papers). Azzam Haidar collaborates with scholars based in United States, United Kingdom and France. Azzam Haidar's co-authors include Jack Dongarra, Stanimire Tomov, Piotr Łuszczek, Nicholas J. Higham, Ahmad Abdelfattah, Jakub Kurzak, Hatem Ltaief, Asim YarKhan, Mark Gates and Tingxing Dong and has published in prestigious journals such as SIAM Review, SIAM Journal on Scientific Computing and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Azzam Haidar

75 papers receiving 1.1k citations

Peers

Azzam Haidar

CNC

CTM

Gregorio Quintana‐Ortí Spain

Hartwig Anzt United States

Hatem Ltaief Saudi Arabia

Greg Henry United States

Alfredo Buttari France

Mark Frederick Hoemmen United States

Emmanuel Agullo France

Marc Baboulin France

Laura Grigori France

Jakub Kurzak United States

Gregorio Quintana‐Ortí Spain

Azzam Haidar

528 ×0.8

430 ×0.9

CNC

376 ×0.9

CTM

182 ×1.1

77 ×0.7

Citations per year, relative to Azzam Haidar Azzam Haidar (= 1×) peers Gregorio Quintana‐Ortí

Countries citing papers authored by Azzam Haidar

Since Specialization

Citations

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

Fields of papers citing papers by Azzam Haidar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Azzam Haidar

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

All Works

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20 of 20 papers shown

Tomov, Stanimire, et al.. (2021). Accelerating Multi - Process Communication for Parallel 3-D FFT. 46–53. 2 indexed citations

Abdelfattah, Ahmad, Stanimire Tomov, Mark Gates, et al.. (2020). MAGMA templates for scalable linear algebra on emerging architectures. The International Journal of High Performance Computing Applications. 34(6). 645–658. 7 indexed citations

Nichols, Daniel, Kwai Wong, Stanimire Tomov, et al.. (2019). MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs. 3 indexed citations

Tomov, Stanimire, et al.. (2019). Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation. 12–18. 14 indexed citations

Haidar, Azzam, Stanimire Tomov, Jack Dongarra, & Nicholas J. Higham. (2018). Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers. Research Explorer (The University of Manchester). 603–613. 119 indexed citations

Dong, Tingxing, Azzam Haidar, Stanimire Tomov, & Jack Dongarra. (2018). Accelerating the SVD bi-diagonalization of a batch of small matrices using GPUs. Journal of Computational Science. 26. 237–245. 10 indexed citations

Abdelfattah, Ahmad, Azzam Haidar, Stanimire Tomov, & Jack Dongarra. (2018). Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization. 9137. 1–7.

Sun, Jian, Joshua S. Fu, John B. Drake, et al.. (2018). Computational Benefit of GPU Optimization for the Atmospheric Chemistry Modeling. Journal of Advances in Modeling Earth Systems. 10(8). 1952–1969. 8 indexed citations

Haidar, Azzam, et al.. (2018). Investigating power capping toward energy‐efficient scientific applications. Concurrency and Computation Practice and Experience. 31(6). 26 indexed citations

10.

Lopez, M. Graham, Verónica Melesse Vergara, Wayne Joubert, et al.. (2016). Towards achieving performance portability using directives for accelerators. IEEE International Conference on High Performance Computing, Data, and Analytics. 2016. 13–24. 5 indexed citations

11.

Haidar, Azzam, et al.. (2016). LU, QR, and Cholesky factorizations: Programming model, performance analysis and optimization techniques for the Intel Knights Landing Xeon Phi. 1–7. 5 indexed citations

12.

Abdelfattah, Ahmad, Azzam Haidar, Stanimire Tomov, & Jack Dongarra. (2016). On the Development of Variable Size Batched Computation for Heterogeneous Parallel Architectures. 1249–1258. 10 indexed citations

13.

Haidar, Azzam, et al.. (2015). Weighted dynamic scheduling with many parallelism grains for offloading of numerical workloads to multiple varied accelerators. 1–8. 2 indexed citations

14.

Haidar, Azzam, Stanimire Tomov, Piotr Łuszczek, & Jack Dongarra. (2015). MAGMA embedded: Towards a dense linear algebra library for energy efficient extreme computing. 1–6. 9 indexed citations

15.

Haidar, Azzam, Tingxing Dong, Piotr Łuszczek, Stanimire Tomov, & Jack Dongarra. (2015). Optimization for performance and energy for batched matrix computations on GPUs. 59–69. 10 indexed citations

16.

Kozhevnikov, Anton, et al.. (2015). Efficient implementation of quantum materials simulations on distributed CPU-GPU systems. 1–12. 8 indexed citations

17.

Dong, Tingxing, Azzam Haidar, Piotr Łuszczek, et al.. (2014). LU Factorization of Small Matrices: Accelerating Batched DGETRF on the GPU. 157–160. 31 indexed citations

18.

Bosilca, George, Aurélien Bouteiller, Anthony Danalis, et al.. (2011). Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA. 1432–1441. 73 indexed citations

19.

Haidar, Azzam, Hatem Ltaief, Asim YarKhan, & Jack Dongarra. (2011). Analysis of dynamically scheduled tile algorithms for dense linear algebra on multicore architectures. Concurrency and Computation Practice and Experience. 24(3). 305–321. 22 indexed citations

20.

Ben‐Hadj‐Ali, Hafedh, et al.. (2009). Seismic wave modeling for seismic imaging. The Leading Edge. 28(5). 538–544. 58 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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