A data structure is an organizational scheme for efficiently storing, accessing, and manipulating data, encompassing arrays, trees, graphs, hash tables, and tensors that underpin algorithmic computation and machine learning systems.
Semantic Classification
Content
Key Characteristics
-
Optimized for parallel processing on GPUs and TPUs
-
Supports efficient indexing and retrieval operations
-
Enables memory-efficient representation of sparse data
-
Facilitates vectorized operations and SIMD instructions
-
Incorporates cache-friendly layouts for performance
Overview
Data Structures in AI represent the organizational schemes for efficiently storing, accessing, and manipulating data used in machine learning algorithms. Key structures include tensors (multi-dimensional arrays for neural networks), graphs (for knowledge graphs and GNNs), trees (decision trees, search trees), hash tables (for feature indexing), and specialized structures like attention mechanisms’ key-value stores. Efficient data structures are crucial for algorithmic complexity, memory utilization, and computational performance in AI systems. Modern implementations leverage GPU-optimized data layouts and distributed data structures for large-scale ML.
Related Concepts
-
References
-
Cormen, T. et al. (2022). Introduction to Algorithms (4th ed.). MIT Press.
-
Nickolls, J. & Dally, W. (2010). The GPU Computing Era. IEEE Micro, 30(2), 56-69.
-
Kipf, T. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017.