Approximate Parallel Scheduling, Vol. 2: Part II: Applications to Optimal Parallel Graph Algorithms in Logarithmic Time
This scholarly work explores how to design parallel algorithms that run in logarithmic time while using an optimal amount of processors. It delves into scheduling techniques that handle uncertain task lengths and shows how these ideas apply to graph problems like connectivity and list ranking on modern parallel machines.
- Learn how to pair short tasks with efficient processor rescheduling to achieve fast, reliable parallel performance.
- See how connectivity and other graph problems can be solved in time that scales well with input size, even for sparse graphs.
- Understand the framework for building protocols that work correctly regardless of the order of task execution.
- Discover how new scheduling methods enable speed-ups on both CRCW and CREW parallel models.
Ideal for readers of advanced parallel computing, algorithm design, and graph algorithms who want practical insight into logarithmic-time solutions and their underpinnings in scheduling theory.