#矩陣乘法
Live, measured metrics for the hashtag #矩陣乘法 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #矩陣乘法
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-08-23 04:01 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 2026-08-23 04:01 UTCNo related tags with measured usage found for #矩陣乘法.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-08-23 04:01 UTCEverything below is measured over the latest 4 public posts (spanning ~6303 hours).
Posting hours (UTC)
Languages: Chinese (Taiwan) (4)
Avg boosts / post: 0
Top of the latest posts
🌗 奇怪的是，GPU 矩陣乘法在給予「可預測」資料時執行得更快！ ➤ 揭開 GPU 效能與資料可預測性的意外關聯：動態功耗的幕後影響 ✤ https://www.thonking.ai/p/strangely-matrix-multiplications 作者在測試 GPU 上的矩陣乘法效能時，意外發現了令人費解的現象：當輸入資料「可預測」（例如全為零或全為一）時，運算速度竟然比輸入隨機資料更快。這項發現源於他將 CUTLASS 函式庫的效能評測結果，與透過 PyTorch
🌕 CUDA-L2：以強化學習超越 cuBLAS 的矩陣乘法效能 ➤ 透過 AI 驅動，在 GPU 上實現更快的矩陣運算 ✤ https://github.com/deepreinforce-ai/CUDA-L2 CUDA-L2 是一個創新的系統，結合了大型語言模型 (LLMs) 和強化學習 (RL)，能自動化優化 Half-precision General Matrix Multiply (HGEMM) 的 CUDA 核心。此係統透過系統性的評估，展現出超越目前主流矩陣
🌘 現代多核心處理器上的進階矩陣乘法優化 ➤ 使用 C 語言與現代指令集，打造超越 BLAS 效能的矩陣乘法 ✤ https://salykova.github.io/gemm-cpu 本文詳述瞭如何在現代多核心處理器上，利用 FMA3 和 AVX2 指令集，對單一執行緒的 FP32 矩陣乘法進行優化。作者透過 C 語言實現，旨在創造一個能適用於各種 x86-64 CPU、且不依賴低階組合語言的矩陣乘法程式。文中探討了理論效能極限，並將自訂實作與 OpenBLAS 進行比較
#矩陣乘法 across platforms
every network with a public tag surfaceFollow #矩陣乘法 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/矩陣乘法