Research Staff Member, IBM Almaden650 Harry Road
San Jose, CA 95120
(408) 927-1199 (office)
My resume and CV
My research interests revolve around large-scale data management from a parallel and real-time systems perspective. During the past few years I had the opportunity to "play" with several emerging parallel architectures and evaluate their use for data-intensive applications. The outcomes are a thorough understanding of (parallel) memory architectures, their implications on data-intensive applications, and new parallel algorithms to overcome the memory wall.
In 2010 I joined the Database Technologies Group at IBM Almaden Research and since 2011
I received my Ph.D. in Computer Science from the Baskin School of Engineering at the University of California Santa Cruz. My advisor was Prof. Scott Brandt and my thesis is on "Predictable High-Performance Data Management - Leveraging System Resource Characteristics to Improve Performance and Predictability". I did my undergraduate work in Business Informatics (Wirtschaftsinformatik) at Technische Universität Darmstadt in Germany. I received my Master's degree in Network Engineering from Eurecom, a research institute funded by Telecom Paris in France and EPFL in Switzerland.
Tim Kaldewey, Guy Lohman, Rene Mueller, Peter Volk.  "GPU Join Processing Revisited."   Eighth International Workshop on Data Management on New Hardware
(DaMoN '12).   Scottsdale, AZ, 2012.
Tim Kaldewey, Sandeep Tata, Eugene Shekita.  "Clydesdale: Structured Data Processing on MapReduce."   15th International Conference on Extending Database Technology (EDBT '12).   Berlin, GERMANY, 2012.
Tim Kaldewey, Andrea Di Blas.   "Large Scale GPU Search."   GPU Computing Gems - Jade Edition.  Chapter 1. Morgan Kaufmann Publishers, Waltham, MA, 2011.
Changkyu Kim, Jatin Chhugani, Nadathur Satish, Eric Sedlar, Anthony Nguyen, Tim Kaldewey, Victor Lee, Scott Brandt, Pradeep Dubey.   "FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs."   2010 ACM SIGMOD/PODS Conference (SIGMOD '10).   Indianapolis, IN, 2010.
Best Paper Award
Andrea Di Blas, Tim Kaldewey.   "Data Monster."   IEEE Spectrum.   Volume 46, Issue 9, 2009.
Tim Kaldewey.   "Programming Video Cards for Database Applications."   USENIX ;login.   Volume 34, Issue 4, 2009.
Tim Kaldewey, Theodore Wong, Richard Golding, Anna Povzner, Scott Brandt.   “Virtualizing Disk Performance.”   14th IEEE Real-Time and Embedded Technology Applications Symposium (RTAS '08).   Saint Louis, MO, 2008.
Best Paper Award