- All
- Parallel Programming
Powered by AI and the LinkedIn community
1
Design for parallelism
Be the first to add your personal experience
2
Develop with parallel tools
Be the first to add your personal experience
4
Optimize for performance
Be the first to add your personal experience
5
Document and maintain
Be the first to add your personal experience
6
Deploy and monitor
Be the first to add your personal experience
7
Here’s what else to consider
Be the first to add your personal experience
Parallel programming is the art of designing and implementing software that can run on multiple processors or cores simultaneously, to achieve faster and more efficient results. However, parallelism is not a magic bullet that can be applied to any problem without careful planning and testing. In this article, you will learn how to incorporate parallelism into your software development lifecycle and best practices, from the design phase to the deployment phase.
Find expert answers in this collaborative article
Experts who add quality contributions will have a chance to be featured. Learn more
Earn a Community Top Voice badge
Add to collaborative articles to get recognized for your expertise on your profile. Learn more
1 Design for parallelism
The first step to incorporate parallelism into your software development lifecycle is to design your software with parallelism in mind. This means identifying the parts of your problem that can be divided into smaller and independent subtasks, and choosing the appropriate parallel programming model, such as shared memory, distributed memory, or hybrid. You should also consider the trade-offs between different levels of parallelism, such as task, data, or instruction level, and the potential challenges, such as synchronization, communication, load balancing, or scalability.
Help others by sharing more (125 characters min.)
2 Develop with parallel tools
The second step to incorporate parallelism into your software development lifecycle is to use parallel tools that can help you write, debug, and optimize your parallel code. Depending on your programming language and platform, you may have access to various libraries, frameworks, or APIs that can simplify the creation and management of parallel processes or threads, such as OpenMP, MPI, or Pthreads. You should also use parallel compilers, debuggers, and profilers that can detect and correct errors, performance bottlenecks, or memory leaks in your parallel code.
Help others by sharing more (125 characters min.)
3 Test for correctness and efficiency
The third step to incorporate parallelism into your software development lifecycle is to test your parallel software for correctness and efficiency. Correctness means ensuring that your parallel software produces the same results as your sequential software, or the expected results according to your specifications. Efficiency means ensuring that your parallel software utilizes the available resources effectively, and achieves a reasonable speedup and scalability compared to your sequential software. You should use various testing techniques and tools, such as unit testing, regression testing, or verification testing, to check for correctness, and performance modeling and prediction tools, such as analytical models, simulators, or benchmarks, to check for efficiency.
Help others by sharing more (125 characters min.)
4 Optimize for performance
The fourth step to incorporate parallelism into your software development lifecycle is to optimize your parallel software for performance. Performance optimization means applying various techniques and strategies to improve the speed, throughput, or resource utilization of your parallel software, without compromising its correctness or functionality. Some of the common performance optimization techniques are: reducing the overhead of parallelization, such as synchronization, communication, or scheduling; improving the locality of data access, such as cache, memory, or disk; exploiting the heterogeneity of the hardware, such as CPU, GPU, or FPGA; and tuning the parameters of the parallel programming model, such as number of processes, threads, or tasks.
Help others by sharing more (125 characters min.)
5 Document and maintain
The fifth step to incorporate parallelism into your software development lifecycle is to document and maintain your parallel software. Documentation means creating and updating clear and comprehensive information about your parallel software, such as its design, implementation, testing, optimization, usage, and limitations. Documentation can help you and other developers understand, modify, or reuse your parallel software in the future. Maintenance means fixing any bugs, errors, or issues that may arise in your parallel software due to changes in the requirements, specifications, or environment. Maintenance can help you ensure the reliability, security, and compatibility of your parallel software.
Help others by sharing more (125 characters min.)
6 Deploy and monitor
The sixth and final step to incorporate parallelism into your software development lifecycle is to deploy and monitor your parallel software. Deployment means delivering your parallel software to the end users or customers, either as a standalone product or as a part of a larger system. Deployment can involve various tasks, such as installation, configuration, licensing, or updating. Monitoring means observing and measuring the behavior and performance of your parallel software in the real-world scenarios, such as production, testing, or evaluation. Monitoring can help you identify and resolve any problems, feedbacks, or opportunities for improvement in your parallel software.
Help others by sharing more (125 characters min.)
7 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?
Help others by sharing more (125 characters min.)
Parallel Programming
Parallel Programming
+ Follow
Rate this article
We created this article with the help of AI. What do you think of it?
It’s great It’s not so great
Thanks for your feedback
Your feedback is private. Like or react to bring the conversation to your network.
Tell us more
Tell us why you didn’t like this article.
If you think something in this article goes against our Professional Community Policies, please let us know.
We appreciate you letting us know. Though we’re unable to respond directly, your feedback helps us improve this experience for everyone.
If you think this goes against our Professional Community Policies, please let us know.
More articles on Parallel Programming
No more previous content
- What are some of the challenges and limitations of MPI debugging tools? 1 contribution
- How do you compare and contrast different lock algorithms and data structures in parallel programming? 4 contributions
- How do you design and implement efficient join operations in mapreduce?
- What are the benefits and challenges of using MPI collective communication?
- How do you integrate GPU-accelerated code with other languages and tools in your workflow? 10 contributions
- What are the best practices and common pitfalls of dynamic load balancing for parallel applications? 6 contributions
No more next content
More relevant reading
- Computer Science How can you ensure robust and reliable parallel programs under heavy loads?
- Process Automation What are the best practices for using C in Process Automation?
- Computer Science How can you succeed with parallel programming?
- Computer Science What's the difference between concurrent and parallel programming?
