Martin Maas

Staff Research Scientist, Google Brain

1600 Amphitheatre Pkwy, Mountain View, CA

E-Mail: <firstname>@martin-maas.com

I am a Staff Research Scientist in the Google Brain team. My primary research interests are in managed language runtime systems, operating systems and computer architecture. I am interested in the entire stack from the hardware to the programming systems layer. My current focus area is how to leverage machine learning to improve computer systems. An overview of my approach to this area can be found here.

Before joining Google, I completed my PhD in the Electrical Engineering and Computer Sciences department at UC Berkeley, working with Krste Asanović and John Kubiatowicz. My PhD research focused on warehouse-scale computers. I worked and collaborated across areas and built real systems that involve large system-level codebases as well as hardware-level RTL. I have applied this approach to domains ranging from security to managed languages. During my PhD, I built a secure processor that provides memory-trace obliviousness (a new security property) and can be targeted by a custom compiler, a distributed language runtime system that coordinates JVMs on different nodes in a cluster, and worked on hardware support for garbage collection. I have also built research infrastructure, including FPGA implementations of hardware based on the RISC-V ISA.

Before coming to UC Berkeley, I completed my undergraduate degree at the University of Cambridge. In my undergraduate research, I investigated the challenges and bottlenecks of implementing a Java Virtual Machine for the Barrelfish Operating System. I was supervised by Ross McIlroy and Tim Harris from Microsoft Research, Cambridge.

During my time in high-school, I was an active participant in science and programming competitions. I was on the German team for the International Olympiad of Informatics (IOI) and represented Germany at the International Science and Engineering Fair (ISEF).

Selected Publications

  • Distilling the Real Cost of Production Garbage Collectors, Zixian Cai, Steve Blackburn, Mike Bond, Martin Maas, 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS '22), May 2022 Paper

  • Adaptive Huge-Page Subrelease for Non-moving Memory Allocators in Warehouse-Scale Computers, Martin Maas, Chris Kennelly, Khanh Nguyen, Darryl Gove, Kathryn S. McKinley, Paul Turner, International Symposium on Memory Management (ISMM '21), June 2021 Paper

  • Learning on Distributed Traces for Data Center Storage Systems, Giulio Zhou, Martin Maas, Conference on Machine Learning and Systems 2021 (MLSys '21), April 2021 Paper

  • A Taxonomy of ML for Systems Problems, Martin Maas, IEEE Micro, Sept/Oct 2020 Paper

  • Learning-based Memory Allocation for C++ Server Workloads, Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, Colin Raffel, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '20), Lausanne, Switzerland, March 2020 Paper | Talk Video (SIGPLAN Research Highlight)

  • A Hardware Accelerator for Tracing Garbage Collection, Martin Maas, Krste Asanović, John Kubiatowicz, 45th International Symposium on Computer Architecture (ISCA'18), Los Angeles, California, June 2018 Paper (Selected as one of IEEE Micro's Top Picks from the 2018 Computer Architecture Conferences)

  • Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications, Martin Maas, Krste Asanović, Tim Harris, John Kubiatowicz, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '16), Atlanta, Georgia, April 2016 Paper

  • GhostRider: A Hardware-Software System for Memory Trace Oblivious Computation, Chang Liu, Austin Harris, Martin Maas, Michael Hicks, Mohit Tiwari, Elaine Shi, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '15), Istanbul, Turkey, March 2015 Paper (Winner of the Best Paper Award)

  • PHANTOM: Practical Oblivious Computation in a Secure Processor, Martin Maas, Eric Love, Emil Stefanov, Mohit Tiwari, Elaine Shi, Krste Asanović, John Kubiatowicz, Dawn Song, ACM Conference on Computer and Communications Security (CCS '13), Berlin, Germany, November 2013 Paper (Finalist for NYU-Poly (formerly AT&T) Best Applied Security Paper Award 2013)

  • GPUs as an Opportunity for Offloading Garbage Collection, Martin Maas, Philip Reames, Jeffrey Morlan, Krste Asanović, Anthony D. Joseph, John Kubiatowicz, International Symposium on Memory Management (ISMM '12), Beijing, China, June 2012 Paper