Ravi Reddy Manumachu

542 total citations
30 papers, 332 citations indexed

About

Ravi Reddy Manumachu is a scholar working on Hardware and Architecture, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Ravi Reddy Manumachu has authored 30 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Hardware and Architecture, 20 papers in Computer Networks and Communications and 17 papers in Information Systems. Recurrent topics in Ravi Reddy Manumachu's work include Parallel Computing and Optimization Techniques (27 papers), Cloud Computing and Resource Management (17 papers) and Distributed and Parallel Computing Systems (15 papers). Ravi Reddy Manumachu is often cited by papers focused on Parallel Computing and Optimization Techniques (27 papers), Cloud Computing and Resource Management (17 papers) and Distributed and Parallel Computing Systems (15 papers). Ravi Reddy Manumachu collaborates with scholars based in Ireland, Spain and United Kingdom. Ravi Reddy Manumachu's co-authors include Alexey Lastovetsky, Arsalan Shahid, Pedro Alonso, Thomas Gruber, Laura Antonelli, Mahendra K. Verma, Claudio Schifanella, Sangyoon Oh, Christian Boehme and Emmanuel Jeannot and has published in prestigious journals such as Applied Energy, IEEE Access and ACM Computing Surveys.

In The Last Decade

Ravi Reddy Manumachu

29 papers receiving 319 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ravi Reddy Manumachu Ireland 10 221 216 168 72 26 30 332
Nikita Mishra United States 11 147 0.7× 188 0.9× 114 0.7× 68 0.9× 86 3.3× 18 302
Umair Ullah Tariq Australia 11 154 0.7× 262 1.2× 69 0.4× 96 1.3× 34 1.3× 26 340
David Black-Schaffer Sweden 17 606 2.7× 529 2.4× 201 1.2× 142 2.0× 34 1.3× 59 688
Mohammad Banikazemi United States 11 201 0.9× 474 2.2× 164 1.0× 71 1.0× 36 1.4× 29 499
Christian Le United States 5 272 1.2× 259 1.2× 208 1.2× 132 1.8× 54 2.1× 8 429
Hidenori Nakazato Japan 9 46 0.2× 280 1.3× 105 0.6× 61 0.8× 29 1.1× 69 338
Ahmed ElTantawy Canada 5 531 2.4× 451 2.1× 112 0.7× 185 2.6× 40 1.5× 6 602
Polychronis Xekalakis United Kingdom 12 314 1.4× 246 1.1× 60 0.4× 124 1.7× 34 1.3× 22 387
George Amvrosiadis United States 10 143 0.6× 466 2.2× 278 1.7× 72 1.0× 57 2.2× 26 540

Countries citing papers authored by Ravi Reddy Manumachu

Since Specialization
Citations

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

Fields of papers citing papers by Ravi Reddy Manumachu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ravi Reddy Manumachu. 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 Ravi Reddy Manumachu. The network helps show where Ravi Reddy Manumachu may publish in the future.

Co-authorship network of co-authors of Ravi Reddy Manumachu

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

All Works

20 of 20 papers shown
1.
Manumachu, Ravi Reddy, et al.. (2024). Accurate and Reliable Energy Measurement and Modelling of Data Transfer Between CPU and GPU in Parallel Applications on Heterogeneous Hybrid Platforms. IEEE Transactions on Computers. 74(3). 1011–1024. 1 indexed citations
2.
Manumachu, Ravi Reddy, et al.. (2024). OpenH: A Novel Programming Model and API for Developing Portable Parallel Programs on Heterogeneous Hybrid Servers. IEEE Access. 12. 23666–23694. 2 indexed citations
3.
Lastovetsky, Alexey & Ravi Reddy Manumachu. (2023). Energy-Efficient Parallel Computing: Challenges to Scaling. Information. 14(4). 248–248. 5 indexed citations
4.
Manumachu, Ravi Reddy, et al.. (2023). SUARA: A scalable universal allreduce communication algorithm for acceleration of parallel deep learning applications. Journal of Parallel and Distributed Computing. 183. 104767–104767.
5.
6.
Shahid, Arsalan, et al.. (2021). Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors. IEEE Access. 9. 63149–63172. 9 indexed citations
7.
Shahid, Arsalan, et al.. (2020). A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs. IEEE Access. 8. 143306–143332. 5 indexed citations
8.
Lastovetsky, Alexey & Ravi Reddy Manumachu. (2020). The 27th International Heterogeneity in Computing Workshop and the 16th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms. Concurrency and Computation Practice and Experience. 32(15). 1 indexed citations
9.
Boehme, Christian, Dora B. Heras, Emmanuel Jeannot, et al.. (2020). Euro-Par 2019: Parallel Processing Workshops. Lecture notes in computer science. 4 indexed citations
10.
Manumachu, Ravi Reddy, et al.. (2020). A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high‐performance computing platforms. Concurrency and Computation Practice and Experience. 32(21). 4 indexed citations
11.
Manumachu, Ravi Reddy, et al.. (2020). Multicore processor computing is not energy proportional: An opportunity for bi-objective optimization for energy and performance. Applied Energy. 268. 114957–114957. 8 indexed citations
12.
Shahid, Arsalan, et al.. (2019). A Comparative Study of Methods for Measurement of Energy of Computing. Energies. 12(11). 2204–2204. 44 indexed citations
13.
14.
Manumachu, Ravi Reddy, et al.. (2018). Performance Optimization of Multithreaded 2D FFT on Multicore Processors: Challenges and Solution Approaches. 74. 8–17. 2 indexed citations
15.
Manumachu, Ravi Reddy, et al.. (2018). A Novel Data-Partitioning Algorithm for Performance Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms. IEEE Transactions on Parallel and Distributed Systems. 29(10). 2176–2190. 30 indexed citations
16.
17.
Manumachu, Ravi Reddy & Alexey Lastovetsky. (2018). Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution. Concurrency and Computation Practice and Experience. 31(4). 9 indexed citations
18.
Manumachu, Ravi Reddy & Alexey Lastovetsky. (2017). Bi-Objective Optimization of Data-Parallel Applications on Homogeneous Multicore Clusters for Performance and Energy. IEEE Transactions on Computers. 67(2). 160–177. 40 indexed citations
19.
Lastovetsky, Alexey & Ravi Reddy Manumachu. (2016). New Model-Based Methods and Algorithms for Performance and Energy Optimization of Data Parallel Applications on Homogeneous Multicore Clusters. IEEE Transactions on Parallel and Distributed Systems. 28(4). 1119–1133. 41 indexed citations
20.
Manumachu, Ravi Reddy, Alexey Lastovetsky, & Pedro Alonso. (2008). Scalable Dense Factorizations for Heterogeneous Computational Clusters. 66. 49–56. 2 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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