Martin Maas

761 total citations
25 papers, 469 citations indexed

About

Martin Maas is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture. According to data from OpenAlex, Martin Maas has authored 25 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Networks and Communications, 13 papers in Information Systems and 13 papers in Hardware and Architecture. Recurrent topics in Martin Maas's work include Parallel Computing and Optimization Techniques (12 papers), Cloud Computing and Resource Management (9 papers) and Distributed systems and fault tolerance (7 papers). Martin Maas is often cited by papers focused on Parallel Computing and Optimization Techniques (12 papers), Cloud Computing and Resource Management (9 papers) and Distributed systems and fault tolerance (7 papers). Martin Maas collaborates with scholars based in United States, Australia and Germany. Martin Maas's co-authors include John Kubiatowicz, Krste Asanović, Tim Harris, Elaine Shi, Mohit Tiwari, Eric Love, Dawn Song, Emil Stefanov, Michael Hicks and Virendra J. Marathe and has published in prestigious journals such as Communications of the ACM, ACM SIGPLAN Notices and IEEE Micro.

In The Last Decade

Martin Maas

25 papers receiving 451 citations

Peers

Martin Maas
Scott A. Crosby United States
Shu-Chun Weng United States
Thomas Gazagnaire United Kingdom
Brad Chen United States
Tej Chajed United States
Haogang Chen China
Seongwook Jin South Korea
Juan del Cuvillo United States
Leonid Ryzhyk Australia
Michael Wei United States
Scott A. Crosby United States
Martin Maas
Citations per year, relative to Martin Maas Martin Maas (= 1×) peers Scott A. Crosby

Countries citing papers authored by Martin Maas

Since Specialization
Citations

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

Fields of papers citing papers by Martin Maas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Maas

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Maas. A scholar is included among the top collaborators of Martin Maas 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 Martin Maas. Martin Maas 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.
Maas, Martin, et al.. (2024). Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and Beyond. Communications of the ACM. 67(4). 87–96. 1 indexed citations
2.
Phothilimthana, Phitchaya Mangpo, et al.. (2024). Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples. 1016–1032. 2 indexed citations
3.
Maas, Martin, et al.. (2022). TelaMalloc: Efficient On-Chip Memory Allocation for Production Machine Learning Accelerators. 123–137. 10 indexed citations
4.
Jain, Paras, et al.. (2022). Learning to Design Accurate Deep Learning Accelerators with Inaccurate Multipliers. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). 2 indexed citations
5.
Zhou, Giulio & Martin Maas. (2021). Learning on Distributed Traces for Data Center Storage Systems. 3. 350–364. 4 indexed citations
6.
Maas, Martin, et al.. (2021). Adaptive huge-page subrelease for non-moving memory allocators in warehouse-scale computers. 28–38. 10 indexed citations
7.
Maas, Martin. (2020). A Taxonomy of ML for Systems Problems. IEEE Micro. 40(5). 8–16. 6 indexed citations
8.
Maas, Martin, Krste Asanović, & John Kubiatowicz. (2019). A Hardware Accelerator for Tracing Garbage Collection. IEEE Micro. 39(3). 38–46. 1 indexed citations
9.
Maas, Martin, Krste Asanović, & John Kubiatowicz. (2018). A Hardware Accelerator for Tracing Garbage Collection. 138–151. 25 indexed citations
10.
Maas, Martin, Krste Asanović, & John Kubiatowicz. (2017). Return of the Runtimes. 138–143. 7 indexed citations
11.
Maas, Martin, Krste Asanović, Tim Harris, & John Kubiatowicz. (2016). Taurus. ACM SIGARCH Computer Architecture News. 44(2). 457–471. 1 indexed citations
12.
Maas, Martin, Krste Asanović, Tim Harris, & John Kubiatowicz. (2016). Taurus. ACM SIGPLAN Notices. 51(4). 457–471. 6 indexed citations
13.
Maas, Martin, Krste Asanović, Tim Harris, & John Kubiatowicz. (2016). Taurus. 457–471. 34 indexed citations
14.
Maas, Martin, Tim Harris, Krste Asanović, & John Kubiatowicz. (2015). Trash day: coordinating garbage collection in distributed systems. 1–1. 45 indexed citations
15.
Maas, Martin, et al.. (2015). GhostRider. 87–101. 69 indexed citations
16.
Liu, Chang, et al.. (2015). GhostRider. ACM SIGARCH Computer Architecture News. 43(1). 87–101. 3 indexed citations
17.
Harris, Tim, Martin Maas, & Virendra J. Marathe. (2014). Callisto. 1–14. 38 indexed citations
18.
Maas, Martin, Eric Love, Emil Stefanov, et al.. (2013). PHANTOM. 311–324. 125 indexed citations
19.
Maas, Martin, et al.. (2012). GPUs as an opportunity for offloading garbage collection. 25–36. 15 indexed citations
20.
Maas, Martin, et al.. (2012). GPUs as an opportunity for offloading garbage collection. ACM SIGPLAN Notices. 47(11). 25–36. 1 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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