Hardware-Oriented Implementation of Cache Oblivious Matrix Operations Based on Space-Filling Curves. - Michael Bader; Robert Franz; Stephan Günther; Alexander Heinecke (PPAM, 2007)

Porting existing cache-oblivious linear algebra HPC modules to larrabee architecture. - Alexander Heinecke; Carsten Trinitis; Josef Weidendorfer (Conf. Computing Frontiers, 2010)

Multi- and many-core data mining with adaptive sparse grids. - Alexander Heinecke; Dirk Pflüger (Conf. Computing Frontiers, 2011)

Extending a Highly Parallel Data Mining Algorithm to the Intel ® Many Integrated Core Architecture. - Alexander Heinecke; Michael Klemm; Dirk Pflüger; Arndt Bode; Hans-Joachim Bungartz (Euro-Par Workshops (2), 2011)

Towards High-Performance Implementations of a Custom HPC Kernel Using ® Array Building Blocks. - Alexander Heinecke; Michael Klemm; Hans Pabst; Dirk Pflüger (Facing the Multicore-Challenge, 2011)

Making TifaMMy fit for tomorrow: Towards future shared memory systems and beyond. - Alexander Heinecke; Carsten Trinitis (HPCS, 2011)

An efficient vectorization of linked-cell particle simulations. - Wolfgang Eckhardt; Alexander Heinecke (Conf. Computing Frontiers, 2012)

Solving High-Dimensional Problems on Processors with Integrated GPU. - Alexander Heinecke (Facing the Multicore-Challenge, 2012)

Sparse grid classifiers as base learners for AdaBoost. - Alexander Heinecke; Benjamin Peherstorfer; Dirk Pflüger; Zhongwen Song (HPCS, 2012)

HPCS 2012 panels: Panel I: Energy efficient systems in next generation high performance data and compute centers. - Laurent Lefèvre; Vicente Martin; Miguel A. Ordonez; Johnatan E. Pecero; Jean-Marc Pierson; Jesús Carretero 001; Pascal Bouvry; David R. C. Hill; Jesús Labarta; Reinhard Schneider 002; James C. Sexton; Mads Nygård; et al. (HPCS, 2012)

Exploiting State-of-the-Art x86 Architectures in Scientific Computing. - Alexander Heinecke; Thomas Auckenthaler; Carsten Trinitis (ISPDC, 2012)

From GPGPU to Many-Core: Nvidia Fermi and Intel Many Integrated Core Architecture. - Alexander Heinecke; Michael Klemm; Hans-Joachim Bungartz (Comput. Sci. Eng., 2012)

A highly parallel Black-Scholes solver based on adaptive sparse grids. - Alexander Heinecke; Stefanie Schraufstetter; Hans-Joachim Bungartz (Int. J. Comput. Math., 2012)

Option pricing with a direct adaptive sparse grid approach. - Hans-Joachim Bungartz; Alexander Heinecke; Dirk Pflüger; Stefanie Schraufstetter (J. Comput. Appl. Math., 2012)

Accelerators in scientific computing is it worth the effort? - Alexander Heinecke (HPCS, 2013)

Design and Implementation of the Linpack Benchmark for Single and Multi-node Systems Based on Intel® Xeon Phi Coprocessor. - Alexander Heinecke; Karthikeyan Vaidyanathan; Mikhail Smelyanskiy; Alexander Kobotov; Roman Dubtsov; Greg Henry; Aniruddha G. Shet; George Chrysos; Pradeep Dubey (IPDPS, 2013)

Accelerating SeisSol by Generating Vectorized Code for Sparse Matrix Operators. - Alexander Breuer; Alexander Heinecke; Michael Bader; Christian Pelties (PARCO, 2013)

Many-core architectures boost the pricing of basket options on adaptive sparse grids. - Alexander Heinecke; Jacob Jepsen; Hans-Joachim Bungartz (WHPCF@SC, 2013)

591 TFLOPS Multi-trillion Particles Simulation on SuperMUC. - Wolfgang Eckhardt; Alexander Heinecke; Reinhold Bader; Matthias Brehm; Nicolay Hammer; Herbert Huber; Hans-Georg Kleinhenz; Jadran Vrabec; Hans Hasse; Martin Horsch; Martin Bernreuther; Colin W. Glass; et al. (ISC, 2013)

Emerging Architectures Enable to Boost Massively Parallel Data Mining Using Adaptive Sparse Grids. - Alexander Heinecke; Dirk Pflüger (Int. J. Parallel Program., 2013)

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters. - Karthikeyan Vaidyanathan; Kiran Pamnany; Dhiraj D. Kalamkar; Alexander Heinecke; Mikhail Smelyanskiy; Jongsoo Park; Daehyun Kim 001; Aniruddha G. Shet; Bharat Kaul; Bálint Joó; Pradeep Dubey (IPDPS, 2014)

Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers. - Alexander Heinecke; Alexander Breuer; Sebastian Rettenberger; Michael Bader; Alice-Agnes Gabriel; Christian Pelties; Arndt Bode; William Barth; Xiangke Liao; Karthikeyan Vaidyanathan; Mikhail Smelyanskiy; Pradeep Dubey (SC, 2014)

Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices. - Jongsoo Park; Mikhail Smelyanskiy; Karthikeyan Vaidyanathan; Alexander Heinecke; Dhiraj D. Kalamkar; Xing Liu; Md. Mostofa Ali Patwary; Yutong Lu; Pradeep Dubey (SC, 2014)

Sustained Petascale Performance of Seismic Simulations with SeisSol on SuperMUC. - Alexander Breuer; Alexander Heinecke; Sebastian Rettenberger; Michael Bader; Alice-Agnes Gabriel; Christian Pelties (ISC, 2014)

Parallelizing a Black-Scholes solver based on finite elements and sparse grids. - Hans-Joachim Bungartz; Alexander Heinecke; Dirk Pflüger; Stefanie Schraufstetter (Concurr. Comput. Pract. Exp., 2014)

ls1 mardyn: The massively parallel molecular dynamics code for large systems. - Christoph Niethammer; Stefan Becker; Martin Bernreuther; Martin Buchholz; Wolfgang Eckhardt; Alexander Heinecke; Stephan Werth; Hans-Joachim Bungartz; Colin W. Glass; Hans Hasse; Jadran Vrabec; Martin Horsch (CoRR, 2014)

Boosting Scientific Computing Applications through Leveraging Data Parallel Architectures. - Alexander Heinecke (Verlag Dr. Hut, 2014)

Optimized Force Calculation in Molecular Dynamics Simulations for the Intel Xeon Phi. - Nikola Tchipev; Amer Wafai; Colin W. Glass; Wolfgang Eckhardt; Alexander Heinecke; Hans-Joachim Bungartz; Philipp Neumann (Euro-Par Workshops, 2015)

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. - Dheevatsa Mudigere; Srinivas Sridharan 002; Anand M. Deshpande; Jongsoo Park; Alexander Heinecke; Mikhail Smelyanskiy; Bharat Kaul; Pradeep Dubey; Dinesh K. Kaushik; David E. Keyes (IPDPS, 2015)

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. - Yida Wang 003; Michael J. Anderson; Jonathan D. Cohen 003; Alexander Heinecke; Kai Li 001; Nadathur Satish; Narayanan Sundaram; Nicholas B. Turk-Browne; Theodore L. Willke (SC, 2015)

High-Order ADER-DG Minimizes Energy- and Time-to-Solution of SeisSol. - Alexander Breuer; Alexander Heinecke; Leonhard Rannabauer; Michael Bader (ISC, 2015)

Cache-oblivious matrix algorithms in the age of multicores and many cores. - Alexander Heinecke; Carsten Trinitis (Concurr. Comput. Pract. Exp., 2015)

Beacon: Deployment and Application of Intel Xeon Phi Coprocessorsfor Scientific Computing. - R. Glenn Brook; Alexander Heinecke; Anthony B. Costa; Paul Peltz Jr.; Vincent C. Betro; Troy Baer; Michael Bader; Pradeep Dubey (Comput. Sci. Eng., 2015)

Supercomputing for Molecular Dynamics Simulations - Handling Multi-Trillion Particles in Nanofluidics - Alexander Heinecke; Wolfgang Eckhardt; Martin Horsch; Hans-Joachim Bungartz (Springer, 2015)

Petascale Local Time Stepping for the ADER-DG Finite Element Method. - Alexander Breuer; Alexander Heinecke; Michael Bader (IPDPS, 2016)

LIBXSMM: accelerating small matrix multiplications by runtime code generation. - Alexander Heinecke; Greg Henry; Maxwell Hutchinson; Hans Pabst (SC, 2016)

High Order Seismic Simulations on the Intel Xeon Phi Processor (Knights Landing). - Alexander Heinecke; Alexander Breuer; Michael Bader; Pradeep Dubey (ISC, 2016)

Efficiency of High Order Spectral Element Methods on Petascale Architectures. - Maxwell Hutchinson; Alexander Heinecke; Hans Pabst; Greg Henry; Matteo Parsani; David E. Keyes (ISC, 2016)

Data mining on vast data sets as a cluster system benchmark. - Alexander Heinecke; Roman Karlstetter; Dirk Pflüger; Hans-Joachim Bungartz (Concurr. Comput. Pract. Exp., 2016)

Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors. - Jongsoo Park; Mikhail Smelyanskiy; Karthikeyan Vaidyanathan; Alexander Heinecke; Dhiraj D. Kalamkar; Md. Mostofa Ali Patwary; Vadim O. Pirogov; Pradeep Dubey; Xing Liu; Carlos Rosales; Cyril Mazauric; Christopher S. Daley (Int. J. High Perform. Comput. Appl., 2016)

EDGE: Extreme Scale Fused Seismic Simulations with the Discontinuous Galerkin Method. - Alexander Breuer; Alexander Heinecke; Yifeng Cui (ISC, 2017)

Accelerating Seismic Simulations Using the Intel Xeon Phi Knights Landing Processor. - Josh Tobin; Alexander Breuer; Alexander Heinecke; Charles Yount; Yifeng Cui (ISC, 2017)

Mixed Precision Training of Convolutional Neural Networks using Integer Operations. - Dipankar Das 002; Naveen Mellempudi; Dheevatsa Mudigere; Dhiraj D. Kalamkar; Sasikanth Avancha; Kunal Banerjee 001; Srinivas Sridharan 002; Karthik Vaidyanathan; Bharat Kaul; Evangelos Georganas; Alexander Heinecke; Pradeep Dubey; et al. (ICLR (Poster), 2018)

Anatomy of high-performance deep learning convolutions on SIMD architectures. - Evangelos Georganas; Sasikanth Avancha; Kunal Banerjee 001; Dhiraj D. Kalamkar; Greg Henry; Hans Pabst; Alexander Heinecke (SC, 2018)

Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations. - Greg Henry; Ping Tak Peter Tang; Alexander Heinecke (ARITH, 2019)

ISA mapper: a compute and hardware agnostic deep learning compiler. - Matthew Sotoudeh; Anand Venkat; Michael J. Anderson; Evangelos Georganas; Alexander Heinecke; Jason Knight (CF, 2019)

Training Google Neural Machine Translation on an Intel CPU Cluster. - Dhiraj D. Kalamkar; Kunal Banerjee 001; Sudarshan Srinivasan; Srinivas Sridharan 002; Evangelos Georganas; Mikhail E. Smorkalov; Cong Xu; Alexander Heinecke (CLUSTER, 2019)

Petaflop Seismic Simulations in the Public Cloud. - Alexander Breuer; Yifeng Cui; Alexander Heinecke (ISC, 2019)

A Study of BFLOAT16 for Deep Learning Training. - Dhiraj D. Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; et al. (CoRR, 2019)

High-Performance Deep Learning via a Single Building Block. - Evangelos Georganas; Kunal Banerjee 001; Dhiraj D. Kalamkar; Sasikanth Avancha; Anand Venkat; Michael J. Anderson; Greg Henry; Hans Pabst; Alexander Heinecke (CoRR, 2019)

Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F). - Amelia Drew; Alexander Heinecke (CoRR, 2019)

Tensor-optimized hardware accelerates fused discontinuous Galerkin simulations. - Alexander Heinecke; Alexander Breuer; Yifeng Cui (Parallel Comput., 2019)

Optimizing Deep Learning RNN Topologies on Intel Architecture. - Kunal Banerjee 001; Evangelos Georganas; Dhiraj D. Kalamkar; Barukh Ziv; Eden Segal; Cristina Anderson; Alexander Heinecke (Supercomput. Front. Innov., 2019)

Harnessing Deep Learning via a Single Building Block. - Evangelos Georganas; Kunal Banerjee 001; Dhiraj D. Kalamkar; Sasikanth Avancha; Anand Venkat; Michael J. Anderson; Greg Henry; Hans Pabst; Alexander Heinecke (IPDPS, 2020)

Optimizing deep learning recommender systems training on CPU cluster architectures. - Dhiraj D. Kalamkar; Evangelos Georganas; Sudarshan Srinivasan; Jianping Chen; Mikhail Shiryaev; Alexander Heinecke (SC, 2020)

Performance study of sustained petascale direct numerical simulation on Cray XC40 systems. - Bilel Hadri; Matteo Parsani; Maxwell Hutchinson; Alexander Heinecke; Lisandro Dalcín; David E. Keyes (Concurr. Comput. Pract. Exp., 2020)

PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives. - Sanket Tavarageri; Alexander Heinecke; Sasikanth Avancha; Gagandeep Goyal; Ramakrishna Upadrasta; Bharat Kaul (CoRR, 2020)

Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads. - Evangelos Georganas; Dhiraj D. Kalamkar; Sasikanth Avancha; Menachem Adelman; Cristina Anderson; Alexander Breuer; Jeremy Bruestle; Narendra Chaudhary; Abhisek Kundu; Denise Kutnick; Frank Laub; Md. Vasimuddin; et al. (SC, 2021)

DistGNN: scalable distributed training for large-scale graph neural networks. - Md. Vasimuddin; Sanchit Misra; Guixiang Ma; Ramanarayan Mohanty; Evangelos Georganas; Alexander Heinecke; Dhiraj D. Kalamkar; Nesreen K. Ahmed; Sasikanth Avancha (SC, 2021)

Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads. - Evangelos Georganas; Dhiraj D. Kalamkar; Sasikanth Avancha; Menachem Adelman; Cristina Anderson; Alexander Breuer; Narendra Chaudhary; Abhisek Kundu; Md. Vasimuddin; Sanchit Misra; Ramanarayan Mohanty; Hans Pabst; et al. (CoRR, 2021)

Efficient and Generic 1D Dilated Convolution Layer for Deep Learning. - Narendra Chaudhary; Sanchit Misra; Dhiraj D. Kalamkar; Alexander Heinecke; Evangelos Georganas; Barukh Ziv; Menachem Adelman; Bharat Kaul (CoRR, 2021)

PolyDL: Polyhedral Optimizations for Creation of High-performance DL Primitives. - Sanket Tavarageri; Alexander Heinecke; Sasikanth Avancha; Bharat Kaul; Gagandeep Goyal; Ramakrishna Upadrasta (ACM Trans. Archit. Code Optim., 2021)

Next-Generation Local Time Stepping for the ADER-DG Finite Element Method. - Alexander Breuer; Alexander Heinecke (IPDPS, 2022)

Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data. - Narendra Chaudhary; Sanchit Misra; Dhiraj D. Kalamkar; Alexander Heinecke; Evangelos Georganas; Barukh Ziv; Menachem Adelman; Bharat Kaul (IPDPS Workshops, 2022)

FPGA-Based AI Smart NICs for Scalable Distributed AI Training Systems. - Rui Ma; Evangelos Georganas; Alexander Heinecke; Sergey Gribok; Andrew Boutros; Eriko Nurvitadhi (IEEE Comput. Archit. Lett., 2022)

FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems. - Rui Ma; Evangelos Georganas; Alexander Heinecke; Andrew Boutros; Eriko Nurvitadhi (CoRR, 2022)

FP8 Formats for Deep Learning. - Paulius Micikevicius; Dusan Stosic; Neil Burgess; Marius Cornea; Pradeep Dubey; Richard Grisenthwaite; Sangwon Ha; Alexander Heinecke; Patrick Judd; John Kamalu; Naveen Mellempudi; Stuart F. Oberman; et al. (CoRR, 2022)

Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads. - Evangelos Georganas; Dhiraj D. Kalamkar; Sasikanth Avancha; Menachem Adelman; Deepti Aggarwal; Cristina Anderson; Alexander Breuer; Jeremy Bruestle; Narendra Chaudhary; Abhisek Kundu; Denise Kutnick; Frank Laub; et al. (Frontiers Appl. Math. Stat., 2022)

Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. - Evangelos Georganas; Dhiraj D. Kalamkar; Kirill Voronin; Antonio Noack; Hans Pabst; Alexander Breuer; Alexander Heinecke (CoRR, 2023)

Microscaling Data Formats for Deep Learning. - Bita Darvish Rouhani; Ritchie Zhao; Ankit More; Mathew Hall; Alireza Khodamoradi; Summer Deng; Dhruv Choudhary; Marius Cornea; Eric Dellinger; Kristof Denolf; Dusan Stosic; Venmugil Elango; et al. (CoRR, 2023)

Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. - Evangelos Georganas; Dhiraj D. Kalamkar; Kirill Voronin; Abhisek Kundu; Antonio Noack; Hans Pabst; Alexander Breuer; Alexander Heinecke (IPDPS, 2024)

Towards a high-performance AI compiler with upstream MLIR. - Renato Golin; Lorenzo Chelini; Adam Siemieniuk; Kavitha Madhu; Niranjan Hasabnis; Hans Pabst; Evangelos Georganas; Alexander Heinecke (CoRR, 2024)

Einsum Trees: An Abstraction for Optimizing the Execution of Tensor Expressions. - Alexander Breuer; Mark Blacher; Max Engel; Joachim Giesen; Alexander Heinecke; Julien Klaus; Stefan Remke (ASPLOS (2), 2025)

ML-SpecQD: Multi-Level Speculative Decoding with Quantized Drafts. - Evangelos Georganas; Dhiraj D. Kalamkar; Alexander Kozlov; Alexander Heinecke (CoRR, 2025)

Pushing the Envelope of LLM Inference on AI-PC. - Evangelos Georganas; Dhiraj D. Kalamkar; Alexander Heinecke (CoRR, 2025)