Software Bits Newsletter

The Software Bits Newsletter focuses on optimizing software design and performance across various programming languages and environments. It covers practical advice on data structures, memory management, and code optimization techniques. The newsletter emphasizes on making software faster and more efficient by employing design patterns, algorithm optimizations, and leveraging programming language features.

Software Design Software Performance Optimization Programming Languages Data Structures and Algorithms Memory Management Code Optimization Techniques

The hottest Substack posts of Software Bits Newsletter

And their main takeaways

Growing queues faster in go.

206 implied HN points • 06 Aug 23

🕹 Technology Programming Performance Software Development

Dynamic data structures can change size at runtime, like a stack or queue.
Using % to recompute element indices in Go's queue implementation can impact performance.
Comparing copyUsingMod and copyUsingCopy functions in Go revealed potential improvements in performance.

Polymorphic vectors.

206 implied HN points • 08 Jul 23

🕹 Technology Programming Performance Benchmarks

Inheritance can impact performance negatively in C++ due to issues like indirection and virtual function dispatch.
Data-oriented design (DOD) can lead to improved performance by optimizing data organization over code organization.
Using a struct of arrays approach instead of std::variant can offer better performance and minimize memory overhead in certain scenarios.

Removing elements from vector efficiently?

206 implied HN points • 27 May 23

🕹 Technology Programming Efficiency Containers Algorithms

Removing elements from a vector can be inefficient due to shifting elements.
Removing elements from the end of a vector is faster than from the beginning.
Using std::erase_if or std::partition can efficiently remove elements based on conditions.

Reusing memory.

154 implied HN points • 15 Jul 23

🕹 Technology Database Management

Vector databases store and manage high-dimensional vectors for tasks like similarity search.
Simple changes like reusing memory can significantly improve performance in databases.
Optimizations like object pooling and thread local memory can enhance performance further.

cppyy

206 implied HN points • 30 Apr 23

🕹 Technology Programming Python C++ Integration Performance

Python is versatile for many tasks like web development and data science.
Using C++ for compute-heavy tasks in Python can boost performance.
Cppyy offers a simple way to integrate C++ code snippets into Python for performance improvements.

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C++ ranges are better than iterators?

206 implied HN points • 09 Apr 23

🕹 Technology Programming

C++ ranges in C++20 offer a concise way to compose computations.
Using compiler explorer and benchmarks is essential to determine performance in your specific use-case.
Consider design considerations over trends when selecting programming features.

Replacing functions with lookup tables.

206 implied HN points • 02 Apr 23

🕹 Technology Software Functions Performance Programming

Consider using lookup tables instead of functions for better performance.
Low-cardinality data can be efficiently handled with lookup tables.
Lookup table of strings can be significantly faster than string concatenations.

Constructing priority heap efficiently.

154 implied HN points • 17 Jun 23

🕹 Technology Coding Algorithms Data Structures

Priority heaps are commonly used in coding interview questions.
Creating a priority heap efficiently can impact time complexity.
Consider bulk operations API to optimize memory usage in algorithms and data structures.

Making numpy string processing faster.

154 implied HN points • 10 Jun 23

🕹 Technology Python Performance optimization Open Source

NumPy provides high-performance array processing in Python for data science
Consider using tuples for better performance and maintainability in open source projects
String processing in NumPy can be improved by avoiding unnecessary operations

Goto considered useful?

154 implied HN points • 03 Jun 23

🕹 Technology Programming Software Development Compilers

FAISS library uses SIMD for fast vector processing.
Compilers may not always optimize code as expected.
Using 'labels as values' and goto statements can impact performance.

Recursion vs generators vs iterative in-order traversal.

154 implied HN points • 16 Apr 23

🕹 Technology Programming Algorithm Performance Optimization

Recursion breaks down problems into smaller parts but can be inefficient.
Iterative solutions are harder to write than recursion but avoid StackOverflow issues.
Generators promise readability similar to recursion but might have performance costs.

Dictionary comprehension fusion in Python.

154 implied HN points • 05 Mar 23

🕹 Technology Python Performance Programming

List and dictionary comprehensions in Python are great for readability and performance.
Filtering out unwanted values can be done efficiently in dictionary comprehension.
Python's assignment expression (walrus operator) can improve simplicity and performance in dictionary comprehension.

Comparing vectors for equality.

154 implied HN points • 19 Feb 23

🕹 Technology Programming Data Structures Performance C++ Software Development

Consider using std::array instead of std::vector when you know the size at compile-time.
Using std::array can provide significant speed improvements over std::vector in certain scenarios.
While std::array is efficient for compile-time initialization and faster than std::vector, it may not be suitable for all cases.

Using golang bit trick for fast bucket selection.

154 implied HN points • 05 Feb 23

Hashing is important for efficient data structures like hashtables.
Golang uses bit tricks for fast bucket selection to optimize performance.
Copying code patterns across different languages may not always result in performance improvements.

Space-time tradeoff?

103 implied HN points • 07 May 23

🕹 Technology Performance optimization Memory management

Space-time tradeoff involves using less memory to reduce time in problem solving.
Strive to use the least amount of memory necessary to store results that will be reused.
Optimizing memory usage can lead to significant performance improvements, sometimes up to 3 times faster.