Szczegóły publikacji
Opis bibliograficzny
Distributed parallel analysis engine for high-energy physics using AWS Lambda / Jacek Kuśnierz, Maciej MALAWSKI, Vincenzo Eduardo Padulano, Enric Tejedor Saavedra, Pedro Alonso-Jordá // W: HiPS'21 [Dokument elektroniczny] : proceedings of the 1st workshop on High Performance Serverless computing : 25 June 2021, Sweden : virtual event / Association for Computing Machinery. — Wersja do Windows. — Dane tekstowe. — New York : ACM, 2021. — Workshop jest częścią konferencji HPDC '21 : the 30th International Symposium on High-Performance Parallel and Distributed Computing. — e-ISBN: 978-1-4503-8388-2. — S. 13-16. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3452413.3464788 [2021-10-05]. — Bibliogr. s. 16, Abstr.
Autorzy (5)
- AGHKuśnierz Jacek
- AGHMalawski Maciej
- Padulano Vincenzo Eduardo
- Saavedra Enric Tejedor
- Alonso-Jordá Pedro
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 136807 |
|---|---|
| Data dodania do BaDAP | 2021-10-13 |
| DOI | 10.1145/3452413.3464788 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | ACM International Symposium on High Performance Distributed Computing 2021 |
Abstract
The High-Energy Physics experiments at CERN produce a high volume of data. It is not possible to analyze big chunks of it within a reasonable time by any single machine. The ROOT framework was recently extended with the distributed computing capabilities for massively parallelized RDataFrame applications. This approach, using the MapReduce pattern underneath, made the heavy computations much more approachable even for the newcomers. This paper explores the possibility of running such analyses on serverless services in public cloud using a purely stateless environment. So far, the distributed approaches used by RDataFrame relied on stateful, fully managed computing frameworks like Apache Spark. Here we show that our newly developed tool is able to use perfectly stateless cloud functions, demonstrating the excellent speedup in parallel stage of processing in our benchmarks.
