Efficient Algebraic Representations for Throughput-Oriented Algorithms

This dissertation explores the design and deployment of memory-efficient tensor product representations for throughput-oriented processors. These representations lead naturally to algorithms with excellent data-locality and can be used to construct efficient and scalable representations for hierarchical tensor-based algorithms.