2026 Gennady Pekhimenko: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	33	12621	12253	487	466	126	5044

Overview

Gennady Pekhimenko is affiliated with the University of Toronto in Canada and has an extensive research profile in computer science with a focus on artificial intelligence, computer vision, and hardware architecture. Their scholarly work is reflected in a significant number of publications, particularly within the subfields of Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Electrical and Electronic Engineering, and Computer Networks and Communications.

The scientist's research topics cover a range of advanced computational and algorithmic areas, including:

Advanced Neural Network Applications
Parallel Computing and Optimization Techniques
Stochastic Gradient Optimization Techniques
Adversarial Robustness in Machine Learning
Ferroelectric and Negative Capacitance Devices
Graph Theory and Algorithms
Advanced Graph Neural Networks

Among recent papers authored or co-authored by Pekhimenko, notable works include:

"Federated benchmarking of medical artificial intelligence with MedPerf," published in 2023 in Nature Machine Intelligence
"Gretch," published in 2021 in ACM Transactions on Architecture and Code Optimization
"Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models," published in 2021 on arXiv (Cornell University)
"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices," published in 2021 on arXiv (Cornell University)
"Multi-node Bert-pretraining: Cost-efficient Approach," published in 2020 on arXiv (Cornell University)

Pekhimenko frequently publishes in venues such as:

arXiv (Cornell University)
Nature Machine Intelligence
ACM Transactions on Architecture and Code Optimization
Proceedings of the ACM on Measurement and Analysis of Computing Systems
White Rose Research Online (University of Leeds, The University of Sheffield, University of York)

The scientist collaborates regularly with several co-authors, including:

Christina Giannoula
Qidong Su
Vijay Janapa Reddi
Yu Xin Li
Mohammad Sadrosadati

Best Publications

Base-delta-immediate compression: practical data compression for on-chip caches

Gennady Pekhimenko;Vivek Seshadri;Onur Mutlu;Michael A. Kozuch

453
Citations
MLPerf inference benchmark

Vijay Janapa Reddi;Christine Cheng;David Kanter;Peter Mattson

420
Citations
RowClone: fast and energy-efficient in-DRAM bulk data copy and initialization

Vivek Seshadri;Yoongu Kim;Chris Fallin;Donghyuk Lee

413
Citations
Adaptive-latency DRAM: Optimizing DRAM timing for the common-case

Donghyuk Lee;Yoongu Kim;Gennady Pekhimenko;Samira Khan

260
Citations
Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization

Kevin K. Chang;Abhijith Kashyap;Hasan Hassan;Saugata Ghose

209
Citations
MLPerf Training Benchmark.

Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

198
Citations
Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu;Mohamed Akrout;Bojian Zheng;Andrew Pelegris

186
Citations
Linearly compressed pages: a low-complexity, low-latency main memory compression framework

Gennady Pekhimenko;Vivek Seshadri;Yoongu Kim;Hongyi Xin

181
Citations
ChargeCache: Reducing DRAM latency by exploiting row access locality

Hasan Hassan;Gennady Pekhimenko;Nandita Vijaykumar;Vivek Seshadri

180
Citations
Simultaneous Multi-Layer Access: Improving 3D-Stacked Memory Bandwidth at Low Cost

Donghyuk Lee;Saugata Ghose;Gennady Pekhimenko;Samira Khan

159
Citations
Gist: efficient data encoding for deep neural network training

Animesh Jain;Amar Phanishayee;Jason Mars;Lingjia Tang

156
Citations
SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies

Hasan Hassan;Nandita Vijaykumar;Samira Khan;Saugata Ghose

144
Citations
A case for core-assisted bottleneck acceleration in GPUs: enabling flexible data compression with assist warps

Nandita Vijaykumar;Gennady Pekhimenko;Adwait Jog;Abhishek Bhowmick

140
Citations
MLPerf Training Benchmark

Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

127
Citations
Design-Induced Latency Variation in Modern DRAM Chips: Characterization, Analysis, and Latency Reduction Mechanisms

Donghyuk Lee;Samira Khan;Lavanya Subramanian;Saugata Ghose

122
Citations
Federated benchmarking of medical artificial intelligence with MedPerf

Unknown

122
Citations
Shifted Hamming distance: a fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping

Hongyi Xin;John Greth;John Emmons;Gennady Pekhimenko

94
Citations
RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads

Amir Yazdanbakhsh;Gennady Pekhimenko;Bradley Thwaites;Hadi Esmaeilzadeh

89
Citations
A case for toggle-aware compression for GPU systems

Gennady Pekhimenko;Evgeny Bolotin;Nandita Vijaykumar;Onur Mutlu

87
Citations
Exploiting compressed block size as an indicator of future reuse

Gennady Pekhimenko;Tyler Huberty;Rui Cai;Onur Mutlu

86
Citations
Priority-based Parameter Propagation for Distributed DNN Training.

Anand Jayarajan;Jinliang Wei;Garth Gibson;Alexandra Fedorova

76
Citations

Frequent Co-Authors

Onur Mutlu ETH Zurich

Todd C. Mowry Carnegie Mellon University

Donghyuk Lee Nvidia (United States)

Saugata Ghose University of Illinois at Urbana-Champaign

Phillip B. Gibbons Carnegie Mellon University

Michael Kozuch Intel (United States)

Vijay Janapa Reddi Harvard University

Andreas Moshovos University of Toronto

Carole-Jean Wu Meta Platforms, Inc.

Hadi Esmaeilzadeh University of California, San Diego

External Links

Personal website of Gennady Pekhimenko

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