ABSTRACT Background When sharing or logging numerical data, we must convert binary floating‐point numbers into their decimal string representations. For example, the number π might become 3.1415927. Engineers have perfected many algorithms for producing such accurate, short strings. Aims We present an empirical comparison across diverse hardware architectures and datasets. Methods We benchmarked several established and recent algorithms for converting binary floating‐point numbers (IEEE 754 double‐precision) to their decimal string representations. We executed the conversions across multiple CPU microarchitectures, including recent Intel (Alder Lake, Skylake), AMD (Zen 3, Zen 4), and ARM (Apple M1/M2, Neoverse) processors, using recent versions of GCC, Clang, and platform‐specific compilers and several datasets. Results and Conclusions Cutting‐edge techniques like Schubfach and Dragonbox achieve up to a tenfold speedup over Steele and White's Dragon4, executing as few as 210 instructions per conversion compared to Dragon4's 1500–5000 instructions. Often per their specification, none of the implementations we surveyed consistently produced the shortest possible strings—some generate outputs up to 30% longer than optimal. We find that standard library implementations in languages such as C++ and Swift execute significantly more instructions than the fastest methods, with performance gaps varying across CPU architectures and compilers. We suggest some optimization targets for future research.

ABSTRACT Objective Converting binary integers to variable‐length decimal strings is a fundamental operation in computing. Conventional fast approaches rely on recursive division and small lookup tables. The goal of this work is to develop a significantly faster method for this task. Methods We propose a SIMD‐based algorithm that leverages integer multiply‐add instructions available on recent AMD and Intel processors. Our method eliminates lookup tables entirely and computes multiple quotients and remainders in parallel. Additionally, we introduce a dual‐variant design with dynamic selection that adapts to input characteristics: a branch‐heavy variant optimized for homogeneous digit‐length distributions and a branch‐light variant for heterogeneous datasets. Results Our single‐core algorithm consistently outperforms all competing methods across the full range of integer sizes. It runs 1.4–2× faster than the closest competitor and 2–4× faster than the C++ standard library function ‘ std::to_chars ’ across tested workloads. Conclusion The proposed SIMD‐based approach with dual‐variant dynamic selection provides a substantial performance improvement for integer‐to‐decimal conversion, delivering superior speed without relying on traditional lookup tables.

Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a significant role. Hardware features such as memory hierarchy can yield substantial running time improvements when optimized. In this paper, we propose two state-reordering techniques for the Topological Value Iteration (TVI) algorithm. Our first technique organizes states in memory so that those belonging to the same Strongly Connected Component (SCC) are contiguous, while our second technique optimizes state value propagation by reordering states within each SCC. We analyze existing planning algorithms with respect to their cache efficiency and describe domain characteristics which can provide an advantage to each of them. Empirical results show that, in many instances, our new algorithms, called eTVI and eiTVI, run several times faster than traditional VI, TVI, LRTDP and ILAO* techniques.

In the last few years, several studies have considered different variants of the Electric Vehicle Journey Planning (EVJP) problem that consists in finding the shortest path (according to time) between two given points, passing by several charging stations and respecting the range of the vehicle. The total time taken by the vehicle is the sum of the driving time, the charging time and the waiting time. Unfortunately, the consideration of the waiting time has been neglected by previous studies. This study aims to fill this gap by introducing: (1) a graph relabeling technique using a probabilistic model of charging station occupancy generated using real EV stations data; (2) an alternative paths generation technique which accounts for worse than expected waiting time at various charging stations. Our empirical results indicate that the a priori consideration of charging station occupancy by graph relabeling can reduce the waiting time by more than 75%, while having a negligible impact on the driving time, and that the generation of alternative paths helps reduce the waiting (and total) time even more. For our public station network dataset and the current station occupancy (for now quite low), the mean total journey time (computed over 1000 requests) decreased by 17.3 minutes when our new technique was used.