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Most programmers don't need to think about allocating or deallocating memory, and they have the garbage collector to thank for that! In this presentation we'll discuss the latest developments in GC compaction as well as how the GC allocates and arranges memory internally. Once we understand this we'll explore a strange way Ruby is decreasing memory usage by allocating more memory. The GC manages memory so that you don't have to, but we're going to see how it's actually done! rubyday 2020 - Virtual edition, September 16th 2020. https://2020.rubyday.it/ Next edition: https://2021.rubyday.it/
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In Aaron Patterson's presentation entitled "Reduce Memory by Using More," delivered at Ruby Day 2020, he explores the intricacies of garbage collection (GC) in Ruby, specifically focusing on recent advancements in GC compaction. He introduces the concept of automatic concurrent compaction, emphasizing that chances are most developers are unaware of how memory management works in the languages they use daily. ### Key Points Discussed: - **Overview of Garbage Collection**: Aaron starts by explaining the role of the garbage collector, which manages memory allocation and deallocation automatically, thus sparing developers from having to manage memory themselves. - **Feature Introduction - `gc.compact`**: He highlights a feature introduced in Ruby 2.7 called `gc.compact`, which aims to reduce memory fragmentation by compacting the heap. This is likened to defragmenting a hard drive, making memory usage more efficient. - **Compaction Process**: The compaction process is detailed, involving moving live objects closer together in memory and updating references. This ensures that memory is utilized efficiently, reducing wastage. - **Goals of Compaction**: Aaron enumerates goals for future memory compaction implementations: - **Memory Savings**: To reduce memory usage effectively. - **Automatic Operation**: Developers should not have to invoke compaction manually. - **Concurrent Processing**: The GC should run while the Ruby program continues its operations, minimizing downtime. - **Speed**: Compaction procedures should be fast to maintain performance. - **Implementation Challenges**: He addresses challenges faced during implementation, particularly with regard to reference updating and the speed of the compaction process. - **Using Read Barriers**: An innovative approach using read barriers was proposed to handle reference updates without crashing the program. This involves intercepting memory read requests to ensure references remain valid after compaction. ### Significant Examples and Illustrations: - Aaron shares a graphical representation of memory layout before and after compaction, illustrating the efficiency gained through compaction. - The complexities of class relationships in Ruby are exemplified to explain how the compaction algorithm needs to manage references correctly to prevent crashes. ### Conclusion and Takeaways: Aaron concludes that while performance metrics showed the read barrier implementation to be slower, the primary objective was to ensure functional accuracy before optimizing for speed. The crucial lesson emphasized is that developers should ensure their code works correctly before attempting to enhance its speed. This talk reflects a deeper understanding of memory management in Ruby and the ongoing improvements aimed to enhance developer experience.
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