Tootfinder

Neue Bauweise soll Petabyte-SSDs ermöglichen
Für Server kommt die neue SSD-Bauform E2. Mit 64 Speicherbausteinen ist sie für Kapazitäten bis zu einem Petabyte gerüstet.
https://www.hei…

🇺🇦 Auf radioeins läuft...
Tegan & Sara:
🎵 Back In Your Head
#NowPlaying #Tegan #Sara
https://kinack.bandcamp.com/track/tegan-and-sara-back-in-your-head-kinack-bootleg-remix
https://open.spotify.com/track/2Pdo1VvYDB9FqeuNx8D1FW

Contributions of the Petabyte Scale Sequence Search Codeathon toward efforts to scale sequence-based searches on SRA
Priyanka Ghosh, Kjiersten Fagnan, Ryan Connor, Ravinder Pannu, Travis J. Wheeler, Mihai Pop, C. Titus Brown, Tessa Pierce-Ward, Rob Patro, Jacquelyn S. Michaelis, Thomas L. Madden, Christiam Camacho, Olaitan I. Awe, Arianna I. Krinos, Ren\'e KM Xavier, Rodrigo Ortega Polo, Jack W. Roddy, Adelaide Rhodes, Alexander Sweeten, Adrian Viehweger, Bari\c{s} Ekim, Harihara S…

Contributions of the Petabyte Scale Sequence Search Codeathon toward efforts to scale sequence-based searches on SRA
The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institutes of Health's (NIH) Sequence Read Archive (SRA), which is maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a rapidly growing public database that researchers use to drive scientific discovery across all domains of life. This increase in available dat…

MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies
Matthew R. Hennefarth, Younghwan Kim, Bhavnesh Jangid, Jacob Wardzala, Matthew R. Hermes, Donald G. Truhlar, Laura Gagliardi
https://arxiv.org/abs/2506.03…

MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies
Multiconfiguration pair-density functional theory (MC-PDFT) is a post-MCSCF multireference electronic-structure method that explicitly models strong electron correlation, and linearized pair-density functional theory (L-PDFT) is a recently developed multi-state extension that can accurately model conical intersections and locally-avoided crossings. Because MC-PDFT and L-PDFT rely on an on-top energy functional, their accuracy depends on the quality of the on-top functional used. Recent work has…

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#verwechselt

Patioboater (@patioboater@mastodon.social)
Angehängt: 4 Bilder A visit to the Elephant Graveyard of West Bloomfield, Michigan: unsold Cybertrucks piling up in the parking lot of a closed Bed, Bath, & Beyond about a mile south of the Tesla dealer. May 30, 2025. #Tesla #Michigan #cybertruck

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse, Tammy Glazer, Gilles Hacheme, Anthony Ortiz, Rahul M Dodhia, Juan M Lavista Ferres
https://arxiv.org/abs/2506.06235

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot keep modern GPUs utilized when streaming GeoTIFF files directly from cloud storage. In this work, we benchmark GeoTIFF loading throughput from both cloud object storage and local SSD, systematically testing different loader configurations and data parameters. We focus on tile-aligned reads and worker thread pools, usin…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
The Go! Team:
🎵 Ladyflash
#TheGoTeam
https://thegoteam.bandcamp.com/track/ladyflash-monster-rally-remix
https://open.spotify.com/track/6pVTFOItC56aBNhMVjoGje

Modern search demands scalable personalisation. Join Piotr Kobziakowski
at this year's Berlin Buzzwords to discover how Vespa's multi-stage ranking and tensor framework can be used for hybrid queries, multimodal retrieval, and real-time machine learning. Learn how to deploy low-latency, high-relevance search systems at petabyte scale.
Learn more:

Vespa.ai’s Personalized Search: Advanced Ranking & Tensor framework
Today’s applications require search engines to unify text, vectors, and business logic with millisecond latency at petabyte scale. It’s not easy to balance speed, relevance, and personalization for a large user population and a billion scale item base. Vespa.ai, the open-source engine powering Yahoo, Perplexity, Qwant, Vinted, Spotify addresses this through multi-stage ranking with close to data tensor operations and easy to understand custom functions. Vespa’s phased architecture enables…

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
Yiyu Ni, Marine A. Denolle, Amanda M. Thomas, Alex Hamilton, Jannes M\"unchmeyer, Yinzhi Wang, Lo\"ic Bachelot, Chad Trabant, David Mencin
https://arxiv.org/abs/2505.18874

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and …

This https://arxiv.org/abs/2504.11540 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Pruning in Snowflake: Working Smarter, Not Harder
Modern cloud-based data analytics systems must efficiently process petabytes of data residing on cloud storage. A key optimization technique in state-of-the-art systems like Snowflake is partition pruning - skipping chunks of data that do not contain relevant information for computing query results. While partition pruning based on query predicates is a well-established technique, we present new pruning techniques that extend the scope of partition pruning to LIMIT, top-k, and JOIN operations…