I study how to build automatic tools to improve performance and flexibility, with a focus on graphs and machine learning. My broad philosophy is that the combination of statistical methods, code analysis, and domain knowledge leads to better approaches for understanding and building fast, scalable systems. My recent work includes the MachSuite and Fathom benchmarks for hardware accelerators and deep learning, resepctively, the Minerva optimization framework for designing neural network processors, and a variety of performance analysis and optimization tools for deep learning models. Between 2018 and 2021, I also led the technical strategy for Trovares, Inc., a graph analytics startup.
My past work was largely on supercomputers and graph analytics. I spent about a decade in the Department of Defense and Department of Energy looking at what goes wrong when software scales up, and much of that time involved platforms for graph pattern analysis. A lot of my early work involved doing this by hand for specific applications on unconventional architectures, and that time taught me the importance of automated tools for democratizing computational software.
If you're interested in what else I've done over the years, I can send you a CV. Just ask me via email.
You can also ask Google what I've written.
|Book . "Deep Learning for Computer Architects." Morgan-Claypool, 2017.|
|PDF . "A Case for Efficient Accelerator Design Space Exploration via Bayesian Optimization." International Symposium on Low-Power Electronics and Design (ISLPED). July 2017, Taipei, Taiwan.|
|PDF . "Designing Neural Network Hardware Accelerators with Decoupled Objective Evaluations." NIPS Workshop on Bayesian Optimization (BayesOpt). December 2016, Barcelona, Spain.|
|PDF arXiv Code . "Fathom: Reference Workloads for Modern Deep Learning Methods." Proceedings of the IEEE International Symposium on Workload Characterization (IISWC). September 2016, Providence, RI.|
|PDF . "Minerva: Enabling Low-Power, High-Accuracy Deep Neural Network Accelerators." Proceedings of the International Symposium on Computer Architecture (ISCA). June 2016, Seoul, Korea.|
|PDF Code . "MachSuite: Benchmarks for Accelerator Design and Customized Architectures." International Symposium on Workload Characterization (IISWC). October 2014, Raleigh, NC.|
|PDF . "Scalable, Multithreaded, Partially-in-place Sorting." Proceedings of the Seventh Workshop on Multithreaded Architectures and Applications. May 2013, Boston, MA.|
|PDF . "Towards Efficient N-x Contingency Selection Using Group Betweenness Centrality." Proceedings of the Second International Workshop on High-Performance Computing, Networking, and Analytics for the Power Grid. November 2012, Salt Lake City, UT.|
|PDF . "Techniques for Improving Filters in Power Grid Contingency Analysis." 7th International Conference on Machine Learning and Data Mining. August 2011, New York.|
|PDF . "High-Performance Descriptive Semantic Analysis of Semantic Graph Databases." 1st Workshop on High-Performance Computing for the Semantic Web. May 2011, Crete.|
|PDF . "High-performance Computing Applied to Semantic Databases." 8th Extended Semantic Web Conference. May 2011, Crete.|
|PDF . "The Design and Evolution of Deep Learning Workloads." IEEE Micro. Vol. 37, No. 1, 2017.|
|PDF , S. Borkar, N. DeBardeleben, M. Elnozahy, M. Heroux, D. Rogers, R. Ross, V. Sarkar, M. Schulz, M. Snir, and P. Woodward. "Inter-Agency Workshop on HPC Resilience at Extreme Scale." July 2012, Catonsville, MD.|
|PDF . "Materialization is Evil." Invited Position Paper, Workshop on Scalable Graph Libraries. June 2011, Atlanta.|
|PDF . "Graph Analysis for the Semantic Web." Invited Position Paper, Workshop on Scalable Graph Libraries. June 2011, Atlanta.|
|PDF . "Report on April, 2011, Workshop on Semantic Graph Database Search Patterns." Invited Report, 1st Workshop on High-Performance Computing for the Semantic Web. May 2011, Crete.|
|PDF . "High Performance Semantic Factoring." Runner-up Submission, Semantic Web Challenge 2010, Billion Triples Track. November 2010, Shanghai.|