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Explores memory virtualization, addressing address spaces, page tables, caching, and system design constants to optimize memory performance and reliability.
Explores Multi-Point Constraints in nonlinear structural analysis, covering methods like Master-Slave Elimination and numerical examples of small frames with enforced constraints.
Covers the fundamentals of scaling to massive data using Spark, focusing on RDDs, transformations, actions, Spark architecture, and Spark's machine learning toolkit.
