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@netzschleuder@social.skewed.de
2025-05-31 21:00:04

facebook_organizations: Within-organization Facebook friendships (2013)
Six networks of friendships among users on Facebook who indicated employment at one of the target corporation. Companies range in size from small to large. Only edges between employees at the same company are included in a given snapshot.
This network has 1429 nodes and 32876 edges.
Tags: Social, Online, Unweighted

facebook_organizations: Within-organization Facebook friendships (2013). 1429 nodes, 32876 edges. https://networks.skewed.de/net/facebook_organizations#M1

facebook_organizations — Within-organization Facebook friendships (2013)
Six networks of friendships among users on Facebook who indicated employment at one of the target corporation. Companies range in size from small to large. Only edges between employees at the same company are included in a given snapshot.

@cdarwin@c.im
2025-05-30 17:00:55

Trump went on a digression about his recent trip to the Middle East, where, he claimed, he made a lot of money.
“I’m only here for one thing, to see if I can end it, to save 5,000 lives a week and a lot of money, the money being much less important because I made that money,” he continued.
"I went to Saudi Arabia, Qatar, and U.A.E., and we brought back $5.1 trillion.
So, I made that money in about two hours, the money that we’re talking about.
But it’s still three…

Reporter Asks Trump Point Blank, ‘What’s Stopped You From Imposing New Sanctions on Russia?’
President Donald Trump fielded questions from reporters in the White House on Wednesday, where one member of the press corps asked why he hasn't imposed new sanctions on Russia.

@arXiv_csAR_bot@mastoxiv.page
2025-05-27 07:17:24

DECA: A Near-Core LLM Decompression Accelerator Supporting Out-of-Order Invocation
Gerasimos Gerogiannis (Intel Corporation,University of Illinois at Urbana-Champaign), Stijn Eyerman (Intel Corporation), Evangelos Georganas (Intel Labs), Wim Heirman (Intel Corporation), Josep Torrellas (University of Illinois at Urbana-Champaign)

DECA: A Near-Core LLM Decompression Accelerator Supporting Out-of-Order Invocation
To alleviate the memory bandwidth bottleneck in Large Language Model (LLM) inference workloads, weight matrices are stored in memory in quantized and sparsified formats. Hence, before tiles of these matrices can be processed by in-core generalized matrix multiplication (GeMM) hardware engines, they need to be dequantized and de-sparsified. This is currently performed in software with vector operations. Unfortunately, this approach delivers only modest performance. Moreover, it is hard to unders…