Covers the basics of parallelism, including physical examples, historical context, multicore era, and parallel collections in Scala.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Covers the basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.
Explores parallelism in programming, emphasizing trade-offs between programmability and performance, and introduces shared memory parallel programming using OpenMP.
Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.
Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.
Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.
Covers the implementation of combiners in parallel programming in Scala, including efficient combine methods and set data structures.
Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.
Explores accelerating complex analytics through speculation, parallelizing queries, handling mispredictions, and transitioning to speculative plans.
