关键词：张量代数；并行化算法；松弛算法
摘 要：This thesis targets the design of parallelizable algorithms and communication-efficient parallel schedules for numerical linear algebra as well as computations with higher-order tensors. We model the costs associated with local computation, communication, and synchronization. We introduce a new technique for deriving lower bounds on tradeoffs between these costs and apply them to algorithms in both dense and sparse linear algebra as well as graph algorithms.