2 Designing Parallel Algorithms

 

Now that we have discussed what parallel algorithms look like, we are ready to examine how they can be designed. In this chapter, we show how a problem specification is translated into an algorithm that displays concurrency, scalability, and locality. Issues relating to modularity are discussed in Chapter 4.

Parallel algorithm design is not easily reduced to simple recipes. Rather, it requires the sort of integrative thought that is commonly referred to as ``creativity.'' However, it can benefit from a methodical approach that maximizes the range of options considered, that provides mechanisms for evaluating alternatives, and that reduces the cost of backtracking from bad choices. We describe such an approach and illustrate its application to a range of problems. Our goal is to suggest a framework within which parallel algorithm design can be explored. In the process, we hope you will develop intuition as to what constitutes a good parallel algorithm.

After studying this chapter, you should be able to design simple parallel algorithms in a methodical fashion and recognize design flaws that compromise efficiency or scalability. You should be able to partition computations, using both domain and functional decomposition techniques, and know how to recognize and implement both local and global, static and dynamic, structured and unstructured, and synchronous and asynchronous communication structures. You should also be able to use agglomeration as a means of reducing communication and implementation costs and should be familiar with a range of load-balancing strategies.

• 2.1 Methodical Design
• 2.2 Partitioning
• 2.3 Communication
• 2.4 Agglomeration
• 2.5 Mapping
• 2.6 Case Study: Atmosphere Model
• 2.7 Case Study: Floorplan Optimization
• 2.8 Case Study: Computational Chemistry
• 2.9 Summary
• Exercises
• Chapter Notes