Trishul Chilimbi

5.2k total citations · 3 hit papers
79 papers, 3.7k citations indexed

About

Trishul Chilimbi is a scholar working on Computer Networks and Communications, Hardware and Architecture and Information Systems. According to data from OpenAlex, Trishul Chilimbi has authored 79 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Computer Networks and Communications, 44 papers in Hardware and Architecture and 29 papers in Information Systems. Recurrent topics in Trishul Chilimbi's work include Parallel Computing and Optimization Techniques (44 papers), Advanced Data Storage Technologies (33 papers) and Cloud Computing and Resource Management (17 papers). Trishul Chilimbi is often cited by papers focused on Parallel Computing and Optimization Techniques (44 papers), Advanced Data Storage Technologies (33 papers) and Cloud Computing and Resource Management (17 papers). Trishul Chilimbi collaborates with scholars based in United States, United Kingdom and Germany. Trishul Chilimbi's co-authors include James R. Larus, Woongki Baek, Matthias Hauswirth, Mark D. Hill, Martin Hirzel, Sumit Gulwani, Karthik Kalyanaraman, Johnson Apacible, Aditya V. Nori and Kapil Vaswani and has published in prestigious journals such as Computer, Proceedings of the VLDB Endowment and ACM Transactions on Computer Systems.

In The Last Decade

Trishul Chilimbi

76 papers receiving 3.5k citations

Hit Papers

Project Adam: building an efficient and scalable deep lea... 2010 2026 2015 2020 2014 2010 2022 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Project Adam: building an efficient and scalable deep learning training system
    2014 352 citations Trishul Chilimbi, Johnson Apacible et al. Operating Systems Design and Implementation profile →
  2. Green
    2010 333 citations Trishul Chilimbi et al. profile →
  3. Vision-Language Pre-Training with Triple Contrastive Learning
    2022 170 citations Son N. Tran, Belinda Zeng et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Trishul Chilimbi United States 30 2.1k 2.1k 1.2k 1.0k 506 79 3.7k
Michael O’Boyle United Kingdom 34 2.1k 1.0× 3.0k 1.4× 869 0.7× 1.1k 1.1× 475 0.9× 145 3.7k
Madanlal Musuvathi United States 29 1.8k 0.8× 1.6k 0.7× 914 0.7× 627 0.6× 425 0.8× 93 3.1k
Dan Grossman United States 32 2.2k 1.0× 2.2k 1.1× 1.9k 1.5× 1.1k 1.1× 882 1.7× 135 4.3k
Jingling Xue Australia 32 1.6k 0.7× 1.9k 0.9× 1.2k 1.0× 865 0.8× 449 0.9× 255 3.6k
Emery D. Berger United States 30 2.3k 1.1× 2.1k 1.0× 1.3k 1.1× 774 0.8× 552 1.1× 90 3.6k
Henry Hoffmann United States 31 3.1k 1.5× 3.1k 1.5× 840 0.7× 1.1k 1.1× 1.6k 3.1× 145 4.8k
Brian N. Bershad United States 43 4.7k 2.2× 3.4k 1.6× 1.9k 1.5× 1.4k 1.3× 417 0.8× 118 6.1k
Jonathan Walpole United States 29 2.6k 1.2× 1.2k 0.6× 1.4k 1.2× 725 0.7× 400 0.8× 123 3.7k
Koen De Bosschere Belgium 32 1.7k 0.8× 2.1k 1.0× 947 0.8× 734 0.7× 477 0.9× 200 3.0k
David I. August United States 38 3.0k 1.4× 3.6k 1.7× 873 0.7× 614 0.6× 1.5k 3.0× 146 4.6k

Countries citing papers authored by Trishul Chilimbi

Since Specialization
Citations

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

Fields of papers citing papers by Trishul Chilimbi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Trishul Chilimbi. 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 Trishul Chilimbi. The network helps show where Trishul Chilimbi may publish in the future.

Co-authorship network of co-authors of Trishul Chilimbi

This figure shows the co-authorship network connecting the top 25 collaborators of Trishul Chilimbi. A scholar is included among the top collaborators of Trishul Chilimbi 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 Trishul Chilimbi. Trishul Chilimbi 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.
Hu, Kai, F. Gao, Xiaohan Nie, et al.. (2025). M-LLM Based Video Frame Selection for Efficient Video Understanding. 13702–13712.
2.
Yang, Jinyu, Ashish Tawari, Mubarak Shah, et al.. (2025). CoLLM: A Large Language Model for Composed Image Retrieval. 3994–4004.
3.
Rizve, Mamshad Nayeem, Fei Fan, Jayakrishnan Unnikrishnan, et al.. (2024). VidLA: Video-Language Alignment at Scale. 14043–14055.
4.
Zheng, Da, Jun Ma, Vassilis N. Ioannidis, et al.. (2023). Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications. PubMed. 2023. 5270–5281. 13 indexed citations
5.
Zhang, Zhen, Shuai Zheng, Yida Wang, et al.. (2022). MiCS. Proceedings of the VLDB Endowment. 16(1). 37–50. 10 indexed citations
6.
Keivanloo, Iman, et al.. (2021). CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models. 1 indexed citations
7.
Seshadri, Vivek, Gennady Pekhimenko, Olatunji Ruwase, et al.. (2015). Page overlays. 79–91. 35 indexed citations
8.
Chilimbi, Trishul, et al.. (2014). Project Adam: building an efficient and scalable deep learning training system. Operating Systems Design and Implementation. 571–582. 352 indexed citations breakdown →
9.
Risvik, Knut Magne, Trishul Chilimbi, Henry Tan, Karthik Kalyanaraman, & Chris Anderson. (2013). Maguro, a system for indexing and searching over very large text collections. 727–736. 13 indexed citations
10.
Ding, Chen & Trishul Chilimbi. (2009). A Composable Model for Analyzing Locality of Multi-threaded Programs. 9(10). 897–901. 34 indexed citations
11.
Chilimbi, Trishul, et al.. (2009). HOLMES: Effective statistical debugging via efficient path profiling. 34–44. 197 indexed citations
12.
Vaswani, Kapil, Aditya V. Nori, & Trishul Chilimbi. (2007). Preferential path profiling. ACM SIGPLAN Notices. 42(1). 351–362. 36 indexed citations
13.
Chilimbi, Trishul, Aditya V. Nori, & Kapil Vaswani. (2007). Quantifying the effectiveness of testing via efficient residual path profiling. 545–548. 5 indexed citations
14.
Chilimbi, Trishul & Vinod Ganapathy. (2006). HeapMD. 219–228. 29 indexed citations
15.
Chilimbi, Trishul & Vinod Ganapathy. (2006). HeapMD. ACM SIGARCH Computer Architecture News. 34(5). 219–228. 1 indexed citations
16.
Hauswirth, Matthias & Trishul Chilimbi. (2004). Low-overhead memory leak detection using adaptive statistical profiling. ACM SIGARCH Computer Architecture News. 32(5). 156–164. 3 indexed citations
17.
Hauswirth, Matthias & Trishul Chilimbi. (2004). Low-overhead memory leak detection using adaptive statistical profiling. ACM SIGPLAN Notices. 39(11). 156–164. 26 indexed citations
18.
Rubin, Shai, Rastislav Bodík, & Trishul Chilimbi. (2002). An efficient profile-analysis framework for data-layout optimizations. 140–153. 93 indexed citations
19.
Chilimbi, Trishul. (2001). On the Stability of Temporal Data Reference Profiles. International Conference on Parallel Architectures and Compilation Techniques. 151–160. 20 indexed citations
20.
Chilimbi, Trishul, Richard Jones, & Benjamin G. Zorn. (2000). Designing a trace format for heap allocation events. 35–49. 13 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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