Cache-Aware Algorithms

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I wrote a simple multithreaded code in Rust, but the performance didn’t increase much with an…

 🧠Memory Hierarchy  Content type: Blog
medium.com
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Less-relevant results

Bring-up and testing of systems with CXL Type 3 memory expanders

 🧠Memory Hierarchy
edn.com·

Operation Costs in CPU Clock Cycles (2016)

 🔀SIMD Programming  Content type: Blog
6it.dev··Hacker News

AWS Tunes Up Graviton5 For Agentic AI, Boosts Bang For The Buck Bigtime

 🧠Memory Hierarchy  Content type: News

Agarra-me se Puderes : RTPI : June 8, 2026 10:20pm-11:12pm WEST : Free Borrow & Streaming

 🧠Memory Hierarchy  Content type: Video
archive.org·

Show HN: Afterburner – Capability-Sandboxed JavaScript/TS Runtime in Rust

 🚂Cranelift Backend  Content type: Code

dod hash-trie - gardenweb

 🌿Trie Structures

Plutarch: The Rise and Fall of Athens: Nine Greek Lives (Penguin Classics 1960) [IBR2026]

 🧠Memory Hierarchy  Content type: Blog

Intel is turning the wrong clock: The Core Ultra 7 265K shows why Arrow Lake loses more at NGU than D2D can recover

 🧠Memory Hierarchy
igorslab.de·

A Database You Can See

 🧠Memory Hierarchy  Content type: Blog
nockawa.github.io·

The Bench Launches ‘FHS Living’ in Paris — Its First European Investment Platform

 🧠Memory Hierarchy  Content type: News

Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law

 Performance  Content type: Blog

Simplifying Weak Reference Processing in ZGC

 🔗Weak References
inside.java·

Inside QuestDB's Query Engine: Tracing Three Queries (8 minute read)

 📈Query Optimization  Content type: Blog
bcalza.b-cdn.net·

Release 0.17.6: Merge pull request #3782 from tigerbeetle/release-2026-06-05 · tigerbeetle/tigerbeetle

 📦Package Managers  Content type: Code
github.com·

A Case for Simulation-Driven Resilience in Agentic Data Systems

 🗄️Database Engines  Content type: Blog

A Frenchman in Dog Canyon

 🧠Memory Hierarchy
loucheleaves.com·

Release v3.3.0 · jkool702/forkrun

 🎯Bit-Vector Algorithms  Content type: Code
github.com·

bigattichouse/packed-twin-inference: PTI achieves ~2× throughput using a single quantized model (Q5_K_M or better) by running 4 generation streams in one batched decode call. The GPU loads model weights once per step and produces 4 predictions simultaneously. KV cache overhead is ~0.8 GiB total for all 4 streams. No draft model. No quality loss

 🧠Memory Hierarchy  Content type: Code
github.com··r/LocalLLaMA

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