How do you optimize generic code that uses lazy or eager evaluation for parallel or distributed computing?

1 Consider your data

The type and size of your data can influence your choice of evaluation strategy. For example, if your data is large, complex, or infinite, lazy evaluation can help you avoid loading or creating the whole data structure in memory. On the other hand, if your data is small, simple, or finite, eager evaluation can help you avoid the overhead of creating and managing lazy data structures.

2 Consider your function

The complexity and purity of your function can also affect your choice of evaluation strategy. For example, if your function is complex, recursive, or has multiple outputs, lazy evaluation can help you avoid redundant or unnecessary computations. On the other hand, if your function is simple, iterative, or has a single output, eager evaluation can help you avoid the overhead of delaying or caching computations.

3 Consider your environment

The architecture and configuration of your parallel or distributed system can also impact your choice of evaluation strategy. For example, if your system is homogeneous, synchronized, or has low communication costs, eager evaluation can help you exploit the available resources and minimize synchronization overhead. On the other hand, if your system is heterogeneous, asynchronous, or has high communication costs, lazy evaluation can help you adapt to the dynamic and unpredictable environment and reduce communication overhead.

4 Consider your goal

The objective and trade-off of your optimization can also influence your choice of evaluation strategy. For example, if your goal is to maximize speed, throughput, or scalability, eager evaluation can help you achieve higher parallelism and utilization. On the other hand, if your goal is to minimize space, latency, or energy, lazy evaluation can help you achieve lower memory consumption and power consumption.

5 Experiment and measure

The best way to optimize your generic code for parallel or distributed computing is to experiment and measure the performance and behavior of your code under different evaluation strategies. You can use tools and frameworks that support both lazy and eager evaluation, such as Haskell, Scala, Spark, or TensorFlow, to compare and contrast the results and identify the best option for your specific problem and context.

6 Refactor and test

The final step to optimize your generic code for parallel or distributed computing is to refactor and test your code to ensure that it works correctly and efficiently under your chosen evaluation strategy. You can use techniques and practices such as code reviews, unit testing, integration testing, and debugging to verify and improve the quality and reliability of your code. You can also use patterns and principles such as functional programming, immutability, and modularity to enhance the readability and maintainability of your code.

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?