Contiguous memory layout
A contiguous memory layout stores logically adjacent data elements at adjacent memory addresses, without gaps between them.
Fast Python: High performance techniques for large datasets
- How it is used
- Uses memory layout to explain performance differences between matrix access patterns and motivate cache-aware NumPy processing.
- How deep it goes
- Explains cache reuse through matrix traversal and relates array views to strides, but leaves allocation details for later.
- Examples
- Compares doubling adjacent matrix elements with doubling elements separated across rows, and demonstrates reshaped and transposed NumPy views.
- What you can do after
- Understand how data layout and traversal order affect cache reuse when accessing matrices and NumPy arrays.
Idiomatic Rust
Contiguous memory is a region whose elements occupy adjacent memory addresses.
- How it is used
- Motivates Vec as a common choice by linking contiguous storage to performance and straightforward element movement.
- How deep it goes
- Explains cache locality, compiler optimization opportunities, and element shifting, while noting that alternatives can win some workloads.
- Examples
- Compares Vec, HashSet, and LinkedList benchmarks for appending, searching, and removing elements.
- What you can do after
- Evaluate Vec against other structures and benchmark before adopting a more complex data structure.
Related concepts
- arrayused by · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
- vectorproduced by · A vector is an ordered, indexed sequence of elements, often numerical, represented as a mathematical quantity or a computational collection.
- data localityproduces · Data locality describes the relationship between where data is located and where or how it is accessed or processed, including proximity, reuse, and location constraints.
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