What are skiplists good for?

An exotic data structure and a whole lot of JOINs.

Alpha Equivalent Hash Consing with Thinnings

Another step on a journey to a thinning egraph.

My memory isn't getting worse, it's just using exponential decay

https://github.com/friendlymatthew

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