Aleksandr Drozd

About Aleksandr Drozd

Aleksandr Drozd, With an exceptional h-index of 15 and a recent h-index of 14 (since 2020), a distinguished researcher at Tokyo Institute of Technology, specializes in the field of high performance computing, natural language processing.

His recent articles reflect a diverse array of research interests and contributions to the field:

At the locus of performance: Quantifying the effects of copious 3D-stacked cache on HPC workloads

Myths and legends in high-performance computing

The Fourth Workshop on Insights from Negative Results in NLP

The First Exascale Supercomputer Accelerating AI-for-Science and Beyond

At the locus of performance: A case study in enhancing cpus with copious 3d-stacked cache

Preparing for the Future—Rethinking Proxy Applications

Why globally re-shuffle? Revisiting data shuffling in large scale deep learning

Outliers dimensions that disrupt transformers are driven by frequency

Aleksandr Drozd Information

University

Position

Researcher

Citations(all)

1077

Citations(since 2020)

890

Cited By

508

hIndex(all)

15

hIndex(since 2020)

14

i10Index(all)

17

i10Index(since 2020)

16

Email

University Profile Page

Google Scholar

Aleksandr Drozd Skills & Research Interests

high performance computing

natural language processing

Top articles of Aleksandr Drozd

Title

Journal

Author(s)

Publication Date

At the locus of performance: Quantifying the effects of copious 3D-stacked cache on HPC workloads

ACM Transactions on Architecture and Code Optimization

Jens Domke

Emil Vatai

Balazs Gerofi

Yuetsu Kodama

Mohamed Wahib

...

2023/12/14

Myths and legends in high-performance computing

The International Journal of High Performance Computing Applications

Satoshi Matsuoka

Jens Domke

Mohamed Wahib

Aleksandr Drozd

Torsten Hoefler

2023/7

The Fourth Workshop on Insights from Negative Results in NLP

Shabnam Tafreshi

Arjun Akula

João Sedoc

Aleksandr Drozd

Anna Rogers

...

2023/5

The First Exascale Supercomputer Accelerating AI-for-Science and Beyond

Satoshi Matsuoka

Kento Sato

Mohamed Wahib

Aleksandr Drozd

2023

At the locus of performance: A case study in enhancing cpus with copious 3d-stacked cache

arXiv preprint arXiv:2204.02235

Jens Domke

Emil Vatai

Balazs Gerofi

Yuetsu Kodama

Mohamed Wahib

...

2022/4/5

Preparing for the Future—Rethinking Proxy Applications

Computing in Science & Engineering

Satoshi Matsuoka

Jens Domke

Mohamed Wahib

Aleksandr Drozd

Andrew A Chien

...

2022/6/7

Why globally re-shuffle? Revisiting data shuffling in large scale deep learning

Truong Thao Nguyen

François Trahay

Jens Domke

Aleksandr Drozd

Emil Vatai

...

2022/5/30

Outliers dimensions that disrupt transformers are driven by frequency

arXiv preprint arXiv:2205.11380

Giovanni Puccetti

Anna Rogers

Aleksandr Drozd

Felice Dell'Orletta

2022/5/23

Proceedings of the Third Workshop on Insights from Negative Results in NLP

Shabnam Tafreshi

João Sedoc

Anna Rogers

Aleksandr Drozd

Anna Rumshisky

...

2022/5

Preparing for the Future--Rethinking Proxy Apps

arXiv preprint arXiv:2204.07336

Satoshi Matsuoka

Jens Domke

Mohamed Wahib

Aleksandr Drozd

Ray Bair

...

2022/4/15

MLPerf™ HPC: A holistic benchmark suite for scientific machine learning on HPC systems

Steven Farrell

Murali Emani

Jacob Balma

Lukas Drescher

Aleksandr Drozd

...

2021/11/15

Generalization in NLI: Ways (not) to go beyond simple heuristics

arXiv preprint arXiv:2110.01518

Prajjwal Bhargava

Aleksandr Drozd

Anna Rogers

2021/10/4

Matrix engines for high performance computing: A paragon of performance or grasping at straws?

Jens Domke

Emil Vatai

Aleksandr Drozd

Peng ChenT

Yosuke Oyama

...

2021/5/17

Scaling distributed deep learning workloads beyond the memory capacity with KARMA

Mohamed Wahib

Haoyu Zhang

Truong Thao Nguyen

Aleksandr Drozd

Jens Domke

...

2020/11/9

See List of Professors in Aleksandr Drozd University(Tokyo Institute of Technology)

Co-Authors

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