Parallel algorithm

Related lectures (31)

Page 2 of 4

Explores futures as a structured approach to concurrency, simplifying parallel tasks and input-output operations.

Explores hardware synchronization methods, including locks, barriers, and critical sections in parallel computing.

Covers the principles of parallel computing and introduces OpenMP for creating concurrent code from serial code.

Covers efficient GPU-accelerated join optimization for complex queries, focusing on improving optimization times and heuristic plan quality.

Explores advanced Spark topics like partitioning strategies, memory optimization, and shuffle operations for efficient job execution.

Explains lattice agreement in distributed algorithms, focusing on processes agreeing on decisions despite failures.

Introduces the MapReduce programming model for distributed computing, focusing on its vision and under-the-hood mechanisms.

Covers exercises on reliable broadcast, uniform reliable broadcast, and total order broadcast in distributed algorithms.

Explores the evolution of CPUs and storage technologies, RAM bit-flipping, Big Data applications, and technical challenges.

Explores the evolution of transistors, power constraints, and the shift towards parallel computing in modern systems.