Index, Count, Offset, Size

Wherein we make progress towards solving one of the most vexing problems of Computer Science — naming things.

Why There’s No Single Best Way To Store Information | Quanta Magazine

The math of data structures helps us understand how different storage systems come with different trade-offs between resources such as time and memory.

Optimizing Bloom Filters for Modern GPU Architectures

Bloom filters are a fundamental data structure for approximate membership queries, with applications ranging from data analytics to databases and genomics. Several variants have been proposed to accommodate parallel architectures. GPUs, with massive thread-level parallelism and high-bandwidth memory, are a natural fit for accelerating these Bloom filter variants potentially to billions of operations per second. Although CPU-optimized implementations have been well studied, GPU designs remain underexplored. We close this gap by exploring the design space on GPUs along three dimensions: vectorization, thread cooperation, and compute latency. Our evaluation shows that the combination of these optimization points strongly affects throughput, with the largest gains achieved when the filter fits within the GPU's cache domain. We examine how the hardware responds to different parameter configurations and relate these observations to measured performance trends. Crucially, our optimized design overcomes the conventional trade-off between speed and precision, delivering the throughput typically restricted to high-error variants while maintaining the superior accuracy of high-precision configurations. At iso error rate, the proposed method outperforms the state-of-the-art by $11.35\times$ ($15.4\times$) for bulk filter lookup (construction), respectively, achieving above $92\%$ of the practical speed-of-light across a wide range of configurations on a B200 GPU. We propose a modular CUDA/C++ implementation, which will be openly available soon.

MARISA: Matching Algorithm with Recursively Implemented StorAge

NFA를 이용한 정규표현식 구현

이 글은 톰슨의 구성을 사용하여 정규표현식을 구현하는 과정을 상세히 설명합니다. 정규표현식을 후위표현식으로 변환하고, 이를 바탕으로 NFA(Non-deterministic Finite Automata) state diagram을 만드는 핵심 단계를 다룹니다. Aho-corasick 알고리즘과의 유사점을 언급하며, tri 구조에서 state diagram으로의 발전된 자료 구조 사용을 강조합니다. 코드 예시와 함께 `split` 및 `match` 노드를 포함한 special node와 정규표현식 operator에 대응하는 NFA fragment를 시각적으로 제시하여 이해를 돕습니다. 특히, 파이썬으로 구현된 `post2nfa` 함수는 후위표현식을 state로 변환하는 방법을 설명하며, `re2post` 함수는 정규표현식을 후위표현식으로 변환하는 과정을 상세히 다룹니다. 마지막으로, graphviz를 사용하여 NFA diagram을 시각화하는 방법을 제시하며, 실제 정규표현식 예제에 대한 결과물을 제공합니다. 이 글을 통해 독자는 정규표현식 엔진의 핵심 원리를 이해하고, NFA를 이용한 패턴 매칭 구현에 대한 깊이 있는 통찰력을 얻을 수 있습니다.