Gennady Pekhimenko
About
In The Last Decade
Gennady Pekhimenko
52 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 59
- Hardware and Architecture 1.4k
- Computer Networks and Communications 1.3k
- Electrical and Electronic Engineering 890
- Artificial Intelligence 436
- Information Systems 262
Countries citing papers authored by Gennady Pekhimenko
This map shows the geographic impact of Gennady Pekhimenko'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 Gennady Pekhimenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gennady Pekhimenko more than expected).
Fields of papers citing papers by Gennady Pekhimenko
This network shows the impact of papers produced by Gennady Pekhimenko. 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 Gennady Pekhimenko. The network helps show where Gennady Pekhimenko may publish in the future.
Co-authorship network of co-authors of Gennady Pekhimenko
This figure shows the co-authorship network connecting the top 25 collaborators of Gennady Pekhimenko. A scholar is included among the top collaborators of Gennady Pekhimenko 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 Gennady Pekhimenko. Gennady Pekhimenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick | 3 |
| 8 | Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training. | 17 |
| 9 | Daydream: Accurately Estimating the Efficacy of Optimizations for {DNN} Training | 7 |
| 10 | Scaling Back-propagation by Parallel Scan Algorithm. | 1 |
| 11 | 25 | |
| 12 | EcoRNN: Efficient Computing of LSTM RNN Training on GPUs | 3 |
| 13 | EcoRNN: Efficient Computing of LSTM RNN on GPUs | 1 |
| 14 | 2 | |
| 15 | 77 | |
| 16 | 25 | |
| 17 | 54 | |
| 18 | 91 | |
| 19 | 145 | |
| 20 | 1 |
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.