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X-WR-CALDESC:Eventi per ISISLab
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DTSTART;TZID=Europe/Rome:20221021T010000
DTEND;TZID=Europe/Rome:20221021T140000
DTSTAMP:20260915T223010
CREATED:20221021T113928Z
LAST-MODIFIED:20221021T113929Z
UID:12743-1666314000-1666360800@www.isislab.it
SUMMARY:Seminario: "GraphQL nel Web Semantico" di Simone Auriemma
DESCRIPTION:Abstract: SPARQL è il principale linguaggio di interrogazione per dati più noto nel web semantico e il suo utilizzo è chiave poichè consente di estrarre informazioni dai KG. Il principale svantaggio di questo linguaggio è la difficoltà di scrittura delle query unito\, molto spesso\, a response di grandi dimensioni rendendone l'utilizzo molto ostico per chi si affaccia per la prima volta su questo argomento. Così ci siamo chiesti: esistono alternative? Una possibilità potrebbe essere GraphQL\, linguaggio di interrogazione dei grafi. In questo seminario vedremo come GraphQL è utilizzato nel web semantico.
URL:https://www.isislab.it/event/seminario-graphql-nel-web-semantico-di-simone-auriemma/
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DTSTART;TZID=Europe/Rome:20221028T120000
DTEND;TZID=Europe/Rome:20221028T120000
DTSTAMP:20260915T223010
CREATED:20221026T111959Z
LAST-MODIFIED:20221026T112113Z
UID:12910-1666958400-1666958400@www.isislab.it
SUMMARY:An Analysis of Long-tailed Network Latency Distribution and Background Traffic on Dragonfly+ of Majid SALIMIBENI
DESCRIPTION:Abstract: Modern computing systems are highly affected by large performance variability\, resulting in a long tail in the distribution of the network latency.For communication-intensive applications\, the variability comes from several factors such as the communication pattern\, job placement strategies\, routing algorithms\, and most importantly\, the network background traffic. Although recent high-performance interconnects such as Dragonfly+ try to mitigate this variability by employing advanced techniques such as adaptive routing or topological improvements\, the long tail is still there.In this study\, we have demonstrated the different sources of performance variability on a large-scale computing system with a Dragonfly+ topology. This quantitative study investigates the impact of several sources including the locality of job placement\, the communication pattern\, the message size\, and the network background traffic.Also\, to tackle the difficulty in measuring the network background traffic\, we present a novel heuristic that accurately estimates the network traffic and helps to identify those highly-varying communications that contribute to the long tail.We have experimentally validated our proposed background traffic heuristic on a collection of pattern-based microbenchmarks as well as two real-world applications\, HACC and miniAMR. Results show that the heuristic can successfully predict most of those runs in long-tail at job submission time on both microbenchmarks and real-world applications
URL:https://www.isislab.it/event/an-analysis-of-long-tailed-network-latency-distribution-and-background-traffic-on-dragonfly-of-majid-salimibeni/
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