What are the best practices for scaling parallel software?

1 Choose the right parallel model

One of the first decisions you need to make when developing parallel software is what kind of parallel model to use. There are different types of parallel models, such as shared memory, distributed memory, hybrid, or GPU computing, each with its own advantages and disadvantages. The choice of parallel model depends on several factors, such as the problem domain, the data structure, the communication patterns, the scalability requirements, and the available hardware. You should choose the parallel model that best suits your application's needs and goals, and that can leverage the features and resources of your target platform.

3 Design for scalability and portability

When designing parallel software, scalability and portability should be taken into account, as well as how the application will behave and perform on different platforms and configurations. To achieve this, you should avoid unnecessary or excessive communication and synchronization, which can introduce overhead and latency. Load balancing and dynamic scheduling techniques can be used to distribute the work evenly and efficiently among the processors or cores. Additionally, data decomposition and data locality strategies can reduce memory access and bandwidth costs. Furthermore, modular and generic code structures can facilitate code reuse and maintenance, while standard and portable interfaces and formats can ensure compatibility and interoperability.

5 Optimize and tune continuously

Scaling parallel software involves optimizing and tuning your code and hardware to achieve the best performance and efficiency. This should be done continuously and iteratively, following certain steps. Firstly, identify and prioritize the performance bottlenecks and optimization opportunities using data from testing and profiling tools. Secondly, apply the appropriate optimization techniques, such as parallelization or vectorization, to improve the speed, throughput, or scalability of your software. Thirdly, evaluate the results of the optimization process using relevant metrics for your application and platform. Lastly, adjust and adapt optimization parameters to fine-tune the performance and efficiency of your software.

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?