Scott McMillan

Courtesy Professor, Electrical and Computer Engineering

Bio

Dr. Scott McMillan is a Principal Research Engineer in the Advanced Computing Laboratory at the Software Engineering Institute at Carnegie Mellon University.  He currently leads research on hardware-software codesign, algorithm design for graph analytics, machine learning and artificial intelligence frameworks, and development of applications targeting hardware ranging from low-power edge devices to supercomputers. He is a member of the GraphBLAS Forum (graphblas.org) and an author of the GraphBLAS C and C++ API Specifications. He is also a member of the ISO C++ Standards committee and its Machine Learning study group, and is actively involved in developing a C++ Standard Library for graph computation.

Before joining Carnegie Mellon University in 2013, Dr. McMillan spent 20 years in the private sector performing research and development in high-performance computing across a wide range of application areas including scientific computing, large-scale 2D/3D geographic information systems (GIS), and massive-scale graph database systems. He also has expertise in the C++ programming language, object-oriented software design, and guiding the entire software development lifecycle using agile methodologies.

His research interests include parallel algorithms, high-performance computing, co-design, compiler technology, data analytics, graph analytics and algorithms (sparse irregular computation), numerical analysis, dynamic simulation, and autonomous systems.

Education

Post-Doc – Computer Science Department, Naval Postgraduate School

PhD 1994 – Electrical Engineering, The Ohio State University

MD 1990 – Electrical Engineering, The Ohio State University

BS 1988 – Computer Engineering, Clemson University

Publications

• U. Sridhar, N. Tukanov, E. Binder, T. Low, S. McMillan, M. Schatz “SMaLL: Software for Rapidly Instantiating Machine Learning Libraries,” in ACM Transactions on Embedded Computing Systems (TECS), Vol.23, Issue 3, May 2024
• A. Lumsdaine, L. D’Alessandro, K. Deweese, J. Firoz, X. Liu, S. McMillan, J. R. Ratzloff, and M. Zalewski, “NWGraph: A Library of Generic Graph Algorithms and Data Structures in C++20,” in European Conference on Object-Oriented Programming (ECOOP/VCOOP), Dagstuhl Publishing, June 2022.
• S. Rao, S. McMillan, and F. Franchetti, “GBTLX Code Generation: Sparse Matrix Times Sparse Vector,” in High Performance Extreme Computing Conference (HPEC), IEEE, September 2021.
• A. Azad, M. Aznaveh, S. Beamer, M. Blanco, J. Chen, L. D’Alessandro, R. Dathathri, T. Davis, K. Deweese, J. Firoz, H. Gabb, G. Gill, B. Hegyi, S. Kolodziej, T. Low, A. Lumsdaine, T. Manlaibaatar, T. Mattson, S. McMillan, R. Peri, K. Pingali, U. Sridhar, G. Szarnyas, Y. Zhang, and Y. Zhang, “Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite,” in IEEE International Symposium on Workload Characterization (IISWC), October 2020.
• U. Sridhar, M. Blanco, R. Mayurnath, D. Spampinato, T. Low, S. McMillan, “Delta-stepping SSSP: from Vertices and Edges to GraphBLAS Implementations,” in Workshop on Graphs, Architectures, Programming, and Learning (GrAPL) at IEEE Intl. Parallel and Distributed Processing Symposium, (Rio de Janeiro), 20 May 2019.
• F. Sadi, J. Sweeney, S. McMillan, T. Low, J. Hoe, L. Pileggi, and F. Franchetti, “PageRank Acceleration for Large Graphs with Scalable Hardware and Two-Step SpMV,” in High Performance Extreme Computing Conference (HPEC), IEEE, September 2018
• J. Kepner, et al., “Mathematical Foundations of the GraphBLAS,” in High Performance Extreme Computing Conference (HPEC), IEEE, September 2016.