Single Instruction, Multiple Data (SIMD) technology is crucial for enhancing computational efficiency in High-Performance Computing (HPC). While C++ SIMD libraries abstract away low-level complexities, their proliferation has led to a fragmented set of libraries, creating significant challenges in both performance and usability for developers. To overcome these library-level limitations, this paper introduces a new collaborative concept for SIMD library design. We present HybridSIMD, a C++ library to embody this principle, resolving fragmentation through a unified interface and an operator-level collaborative back-end that leverages the collective strengths of existing libraries. A built-in auto-tuning engine, featuring a hierarchical search strategy, automatically navigates the rich optimization space created by this collaborative approach to deliver maximum performance without manual intervention. Experimental results across six real-world HPC benchmarks on AVX2, AVX512, and NEON architectures demonstrate HybridSIMD’s superiority. Notably, the highest speedups achieved are 185.34× on AVX2, 97.80× on AVX512, and 71.32× on NEON, showcasing its effectiveness in resolving fragmentation while delivering state-of-the-art performance. Our artifact is available at https://github.com/Panhaolin2001/HybridSIMD.

The potential usage scenarios of dialogue systems will be greatly expanded if they are able to collaborate more creatively with humans. Many studies have examined ways of building such systems, but most of them focus on problem-solving dialogues, and relatively little research has been done on systems that can engage in creative collaboration with users. In this study, we designed a tagline co-writing task in which two people collaborate to create taglines via text chat, created an interface for data collection, and collected dialogue logs, editing logs, and questionnaire results. In total, we collected 782 Japanese dialogues. We describe the characteristic interactions comprising the tagline co-writing task and report the results of our analysis, in which we examined the kind of utterances that appear in the dialogues as well as the most frequent expressions found in highly rated dialogues in subjective evaluations. We also analyzed the relationship between subjective evaluations and workflow utilized in the dialogues and the interplay between taglines and utterances.