#gpgpu
Live, measured metrics for the hashtag #gpgpu from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #gpgpu
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-09-28 13:55 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-09-28 13:55 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-09-28 13:55 UTCEverything below is measured over the latest 40 public posts (spanning ~23067 hours).
Posting hours (UTC) — busiest: 13:00
Languages: English (29) · Russian (10) · German (1)
Avg boosts / post: 0.8
Top of the latest posts
@VileLasagna Has a blog post on the relative speed of different #GPU compute frameworks on the same hardware and driver. Tl;dr: on an #Nvidia card, with Nvidia drivers, #CUDA is the slowest, by far. Fastest is our old stalwart #OpenCL - alm
Hot Aisle's 8x AMD #MI300X server is the fastest computer I've ever tested in #FluidX3D #CFD, achieving a peak #LBM performance of 205 GLUPs/s, and a combined VRAM bandwidth of 23 TB/s. 🖖🤯 The #RTX 5090 looks like a toy in comparison. MI3
It's out, if anyone is curious https://doi.org/10.1002/cpe.8313 This is a “how to” guide. #GPUSPH, as the name suggests, was designed from the ground up to run on #GPU (w/ #CUDA, for historical reasons). We wrote a CPU version a long time a
What “gpgpu” means
WikipediaGeneral-purpose computing on graphics processing units is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.
“General-purpose computing on graphics processing units” on Wikipedia (CC BY-SA) →#gpgpu across platforms
every network with a public tag surfaceFollow #gpgpu straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/gpgpu