Tootfinder

Is there a standardized data processing term for merging/coalescing variable sized data blocks generated in parallel?
Input: set of N vectors each containing 0...M elements
Output: Single vector containing all elements, 0...M*N in size

Bergfest bei der Übertragung des Bildpools von #FotoMarburg in DSpace 🗻
Die Hälfte von ca. 3,5 Mio Dateien wurde übertragen - bezogen auf die Dateigröße (insgesamt 200 TB) sind es sogar schon 77%! Seit Mitte April laufen mehrere Ingestskripte parallel - daher liegt die Gesamtlaufzeit bereits bei über 50 Tagen.
Die Skripte stammen von @…

Oettle berichtet über einen Hundeangriff mit Schusswaffengebrauch in #Ulm – und zeigt nebenbei die Parallelen zwischen seiner persönlichen Hundeaversion und den Befindlichkeiten des deutschen Michels auf. #Satire #UlmDonau

Schusswaffengebrauch und Hundeführerschein: Hunde in den Zoo!
Die Mainstreammedien schweigen das Thema Tod – Cornelius W. M. traut sich: Lesen Sie hier die Wahrheit über Hundekriminalität.

Ordering Results between Two Extreme Order Statistics with Heterogeneous Linear Failure Rate Distributed Components
CM Revathi, Rajesh Moharana, Raju Bhakta
https://arxiv.org/abs/2506.05773

Ordering Results between Two Extreme Order Statistics with Heterogeneous Linear Failure Rate Distributed Components
Stochastic comparisons of series and parallel systems are important in many areas of engineering, operations research and reliability analysis. These comparisons allow for the evaluation of the performance and reliability of systems under different conditions, and can inform decisions related to system design, probabilities of failure, maintenance and operation. In this paper, we investigate the stochastic comparisons of the series and parallel systems under the assumption that the component li…

Flexible Operator Fusion for Fast Sparse Transformer with Diverse Masking on GPU
Wenhao Dai, Haodong Deng, Mengfei Rong, Xinyu Yang, Hongyu Liu, Fangxin Liu, Hailong Yang, Weifeng Liu, Qingxiao Sun
https://arxiv.org/abs/2506.06095

Flexible Operator Fusion for Fast Sparse Transformer with Diverse Masking on GPU
Large language models are popular around the world due to their powerful understanding capabilities. As the core component of LLMs, accelerating Transformer through parallelization has gradually become a hot research topic. Mask layers introduce sparsity into Transformer to reduce calculations. However, previous works rarely focus on the performance optimization of sparse Transformer. Moreover, rule-based mechanisms ignore the fusion opportunities of mixed-type operators and fail to adapt to va…

[2025-06-09 Mon (UTC), 4 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
#toXiv_bot_toot #toXiv_bot_new_article_summary_toot

BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
Hongyi Zhou, Weiran Liao, Xi Huang, Yucheng Tang, Fabian Otto, Xiaogang Jia, Xinkai Jiang, Simon Hilber, Ge Li, Qian Wang, \"Omer Erdin\c{c} Ya\u{g}murlu, Nils Blank, Moritz Reuss, Rudolf Lioutikov
https://arxiv.org/abs/2506.06072

BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines. In contrast to existing action tokenizers based on vector quantization or byte pair encoding, BEAST requires no separate tokenizer training and consistently produces tokens of uniform length, enabling fast action sequence generation via parallel decoding. Leveraging our B-spline formulation, BEAST inherently ensures…

MLOps with Microservices: A Case Study on the Maritime Domain
Renato Cordeiro Ferreira (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Rowanne Trapmann (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Willem-Jan van den Heuvel (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

MLOps with Microservices: A Case Study on the Maritime Domain
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard emplo…

PDHCG: A Scalable First-Order Method for Large-Scale Competitive Market Equilibrium Computation
Huikang Liu, Yicheng Huang, Hongpei Li, Dongdong Ge, Yinyu Ye
https://arxiv.org/abs/2506.06258

PDHCG: A Scalable First-Order Method for Large-Scale Competitive Market Equilibrium Computation
Large-scale competitive market equilibrium problems arise in a wide range of important applications, including economic decision-making and intelligent manufacturing. Traditional solution methods, such as interior-point algorithms and certain projection-based approaches, often fail to scale effectively to large problem instances. In this paper, we propose an efficient computational framework that integrates the primal-dual hybrid conjugate gradient (PDHCG) algorithm with GPU-based parallel comp…

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
Ruilong Wu, Xinjiao Li, Yisu Wang, Xinyu Chen, Dirk Kutscher
https://arxiv.org/abs/2506.02787

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
Hybrid parallelism techniques are essential for efficiently training large language models (LLMs). Nevertheless, current automatic parallel planning frameworks often overlook the simultaneous consideration of node heterogeneity and dynamic network topology changes, limiting their effectiveness in practical applications. In this paper, we address these limitations by modeling heterogeneous nodes within dynamically changing network environments and leveraging simulation-based strategies to determ…