How do you compare and contrast different parallel algorithms and techniques?

1 What are parallel algorithms?

Parallel algorithms are algorithms that can be executed by multiple processors or threads simultaneously, in order to solve a problem faster or with less resources than a sequential algorithm. Parallel algorithms can be classified into different models, such as shared-memory, distributed-memory, message-passing, or hybrid models, depending on how the processors communicate and access data. Parallel algorithms can also be categorized by their design patterns, such as divide-and-conquer, map-reduce, pipeline, or task parallelism, depending on how the problem is decomposed and coordinated.

2 Why are parallel algorithms important?

Parallel algorithms are important because they can exploit the parallelism inherent in many problems and applications, such as image processing, machine learning, cryptography, or scientific computing. Parallel algorithms can improve the performance, scalability, and reliability of these applications, by reducing the execution time, increasing the throughput, or enhancing the fault tolerance. Parallel algorithms can also take advantage of the advances in hardware and software technologies, such as multicore processors, cloud computing, or parallel programming languages and frameworks.

3 How to compare parallel algorithms?

One way to compare parallel algorithms is to use complexity analysis, which measures the time and space requirements of an algorithm as a function of the input size. Complexity analysis can be applied to both sequential and parallel algorithms, but for parallel algorithms, there are additional metrics to consider, such as speedup, efficiency, scalability, and overhead. Speedup is the ratio of the sequential execution time to the parallel execution time, and it indicates how much faster the parallel algorithm is. Efficiency is the ratio of speedup to the number of processors, and it indicates how well the parallel algorithm utilizes the processors. Scalability is the ability of the parallel algorithm to maintain speedup and efficiency as the input size or the number of processors increases. Overhead is the extra time or space required by the parallel algorithm due to communication, synchronization, or load balancing.

4 How to contrast parallel algorithms?

Another way to contrast parallel algorithms is to use empirical evaluation, which involves running the parallel algorithms on real or simulated hardware and measuring their performance and behavior. Empirical evaluation can provide more realistic and accurate results than complexity analysis, as it can account for the actual characteristics and constraints of the hardware and software platforms, such as processor speed, memory size, network bandwidth, or operating system. Empirical evaluation can also reveal the trade-offs and limitations of different parallel algorithms, such as load imbalance, communication bottleneck, or synchronization overhead.

5 How to choose parallel algorithms?

The choice of parallel algorithms depends on several factors, such as the nature of the problem, the characteristics of the data, the features of the hardware and software platforms, and the goals and constraints of the application. Some problems are more amenable to parallelization than others, depending on their degree of independence, granularity, regularity, or locality. Some data structures are more suitable for parallel manipulation than others, depending on their size, shape, distribution, or access pattern. Some platforms are more supportive of parallel execution than others, depending on their architecture, communication model, programming language, or framework. Some applications have more stringent requirements than others, such as accuracy, reliability, security, or energy efficiency. Therefore, choosing parallel algorithms requires a careful analysis and comparison of these factors, as well as a good understanding of the strengths and weaknesses of different parallel algorithms and techniques.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?