What are the best practices for designing parallel and distributed algorithms in computer graphics?

1 Problem decomposition

The first step in designing a parallel or distributed algorithm is to identify the parts of the problem that can be executed independently or concurrently, and how to divide the input data and the output results among the processing units. Depending on the nature of the problem, you may use different strategies, such as spatial decomposition, where you split the data by regions or domains, temporal decomposition, where you split the data by time steps or frames, or functional decomposition, where you split the data by tasks or features. You should also consider the granularity of the decomposition, which affects the balance between computation and communication, and the scalability of the algorithm.

2 Data structures and representations

The choice of data structures and representations can have a significant impact on the performance and memory usage of parallel and distributed algorithms. You should use data structures and representations that are suitable for the problem domain, the hardware architecture, and the communication model. For example, you may use arrays, matrices, or vectors for regular and dense data, or graphs, trees, or meshes for irregular and sparse data. You should also use data structures and representations that support efficient operations, such as traversal, search, update, or transformation, and that minimize the data dependencies and conflicts among the processing units.

3 Communication and synchronization

Communication and synchronization are essential aspects of parallel and distributed algorithms, as they enable the coordination and exchange of information among the processing units. However, they also introduce overheads and bottlenecks that can limit the performance and scalability of the algorithm. Therefore, you should design communication and synchronization schemes that are appropriate for the problem domain, the hardware architecture, and the data structures and representations. For example, you may use message passing, shared memory, or remote procedure calls for communication, and barriers, locks, or atomic operations for synchronization. You should also use communication and synchronization schemes that reduce the frequency, size, and latency of the data transfers, and that avoid or resolve the data dependencies and conflicts among the processing units.

4 Load balancing and fault tolerance

Load balancing and fault tolerance are important aspects of parallel and distributed algorithms, as they ensure the efficient and reliable execution of the algorithm across the processing units. Load balancing refers to the distribution of the work load among the processing units, such that each unit has a fair and optimal amount of work to do. Fault tolerance refers to the detection and recovery of errors or failures that may occur during the execution of the algorithm. You should design load balancing and fault tolerance schemes that are suitable for the problem domain, the hardware architecture, and the communication and synchronization model. For example, you may use static or dynamic load balancing, depending on the predictability and variability of the work load, and you may use checkpointing, replication, or redundancy for fault tolerance, depending on the severity and frequency of the errors or failures.

5 Performance evaluation and optimization

The final step in designing a parallel or distributed algorithm is to evaluate and optimize its performance, based on the metrics and criteria that are relevant for the problem domain and the application scenario. You should use appropriate tools and methods to measure and analyze the performance of the algorithm, such as benchmarks, profilers, or simulators. You should also use appropriate techniques and strategies to improve and optimize the performance of the algorithm, such as parallelization, vectorization, or pipelining. You should also consider the trade-offs and limitations that may arise from the performance optimization, such as accuracy, quality, or complexity.

By following these best practices, you can design parallel and distributed algorithms that can achieve high performance and scalability for computer graphics applications, while also addressing the challenges and issues that are inherent to parallel and distributed computing.

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?