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ORCIDMartin Maas 0001
Person information
- affiliation: Google Brain, Mountain View, CA, USA
- affiliation (Ph.D.): University of California, Berkeley, CA, USA
Other persons with the same name
- Martin D. Maas
![[0000-0002-9191-4558]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-9191-4558")
(aka: Martin Maas 0002) — Instituto de Astronomía y Física del Espacio (IAFE), CONICET-UBA, Buenos Aires, Argentina - Martin Maas 0003 — CAP debis Division Industrie, Fellbach, Germany
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2020 – today
- 2024
[j3]Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, Colin Raffel:
Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and Beyond. Commun. ACM 67(4): 87-96 (2024)- [c17]Phitchaya Mangpo Phothilimthana, Saurabh Kadekodi, Soroush Ghodrati, Selene Moon, Martin Maas:
Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples. ASPLOS (3) 2024: 1016-1032 - 2023
- [c16]Martin Maas, Ulysse Beaugnon, Arun Chauhan, Berkin Ilbeyi:
TelaMalloc: Efficient On-Chip Memory Allocation for Production Machine Learning Accelerators. ASPLOS (1) 2023: 123-137 - [c15]Christian Navasca, Martin Maas, Petros Maniatis, Hyeontaek Lim, Guoqing Harry Xu:
Predicting Dynamic Properties of Heap Allocations using Neural Networks Trained on Static Code: An Intellectual Abstract. ISMM 2023: 43-57 - 2022
- [c14]Paras Jain, Safeen Huda, Martin Maas, Joseph E. Gonzalez, Ion Stoica, Azalia Mirhoseini:
Learning to Design Accurate Deep Learning Accelerators with Inaccurate Multipliers. DATE 2022: 184-189 - [c13]Zixian Cai, Stephen M. Blackburn, Michael D. Bond, Martin Maas:
Distilling the Real Cost of Production Garbage Collectors. ISPASS 2022: 46-57 - [i4]Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan:
Learning to Improve Code Efficiency. CoRR abs/2208.05297 (2022) - [i3]Ubaid Ullah Hafeez, Martin Maas, Mustafa Uysal, Richard McDougall:
Rethinking Storage Management for Data Processing Pipelines in Cloud Data Centers. CoRR abs/2211.02286 (2022) - 2021
- [c12]Martin Maas, Chris Kennelly, Khanh Nguyen, Darryl Gove, Kathryn S. McKinley, Paul Turner:
Adaptive huge-page subrelease for non-moving memory allocators in warehouse-scale computers. ISMM 2021: 28-38 - [c11]Giulio Zhou, Martin Maas:
Learning on Distributed Traces for Data Center Storage Systems. MLSys 2021 - [i2]Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas:
Analyzing a Caching Model. CoRR abs/2112.06989 (2021) - [i1]Zixian Cai, Stephen M. Blackburn, Michael D. Bond, Martin Maas:
Distilling the Real Cost of Production Garbage Collectors. CoRR abs/2112.07880 (2021) - 2020
- [j2]Martin Maas:
A Taxonomy of ML for Systems Problems. IEEE Micro 40(5): 8-16 (2020) - [c10]Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, Colin Raffel:
Learning-based Memory Allocation for C++ Server Workloads. ASPLOS 2020: 541-556 - [c9]Martin Maas:
Hardware support for managed languages: an old idea whose time has finally come? (keynote). MPLR 2020: 2 - [e1]Chen Ding, Martin Maas:
ISMM '20: 2020 ACM SIGPLAN International Symposium on Memory Management, ISMM 2020, virtual [London, UK], June 16, 2020. ACM 2020, ISBN 978-1-4503-7566-5 [contents]
2010 – 2019
- 2019
- [j1]Martin Maas, Krste Asanovic, John Kubiatowicz:
A Hardware Accelerator for Tracing Garbage Collection. IEEE Micro 39(3): 38-46 (2019) - 2018
- [c8]Martin Maas, Krste Asanovic, John Kubiatowicz:
A Hardware Accelerator for Tracing Garbage Collection. ISCA 2018: 138-151 - 2017
- [b1]Martin Maas:
Hardware and Software Support for Managed-Language Workloads in Data Centers. University of California, Berkeley, USA, 2017 - [c7]Martin Maas, Krste Asanovic, John Kubiatowicz:
Return of the Runtimes: Rethinking the Language Runtime System for the Cloud 3.0 Era. HotOS 2017: 138-143 - 2016
- [c6]Martin Maas, Krste Asanovic, Tim Harris, John Kubiatowicz:
Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications. ASPLOS 2016: 457-471 - 2015
- [c5]Chang Liu, Austin Harris, Martin Maas, Michael W. Hicks, Mohit Tiwari, Elaine Shi:
GhostRider: A Hardware-Software System for Memory Trace Oblivious Computation. ASPLOS 2015: 87-101 - [c4]Martin Maas, Tim Harris, Krste Asanovic, John Kubiatowicz:
Trash Day: Coordinating Garbage Collection in Distributed Systems. HotOS 2015 - 2014
- [c3]Tim Harris, Martin Maas, Virendra J. Marathe:
Callisto: co-scheduling parallel runtime systems. EuroSys 2014: 24:1-24:14 - 2013
- [c2]Martin Maas, Eric Love, Emil Stefanov, Mohit Tiwari, Elaine Shi, Krste Asanovic, John Kubiatowicz, Dawn Song:
PHANTOM: practical oblivious computation in a secure processor. CCS 2013: 311-324 - 2012
- [c1]Martin Maas, Philip Reames, Jeffrey Morlan, Krste Asanovic, Anthony D. Joseph, John Kubiatowicz:
GPUs as an opportunity for offloading garbage collection. ISMM 2012: 25-36
Coauthor Index
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last updated on 2024-05-14 22:56 CEST by the dblp team
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