# CPU Cache A hardware cache used by a computer's CPU to reduce the time or energy needed to access data from main memory. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 🍔🧠 How Cloudflare Freed 100TB RAM With 5 Cache Changes DevFeed: [🍔🧠 How Cloudflare Freed 100TB RAM With 5 Cache Changes]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-08-31T15:30:54Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Cloudflare](), [Caching](), [Cache](), [CPU Cache](), [Software Engineering]() Tags: [cache](), [cloudflare](), [cpu-cache](), [software](), [software-engineering]() ### AI overview Cloudflare optimized the storage of DNS cache entries in its Big Pineapple platform through five changes, reducing per-entry memory usage by more than 50% and freeing roughly 100 terabytes across its fleet. The reported changes also increased insert throughput by 43% and reduced lookup latency by 19%. ### Source excerpt PLUS: Anthropic hardware standard 🤖, Rust state machines 🦀, Agent context compression 💾 ## Bloom Filters vs Counting Bloom Filters: When Deletions Kill Performance DevFeed: [Bloom Filters vs Counting Bloom Filters: When Deletions Kill Performance]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Algorithms](), [Caching](), [CPU Cache]() Tags: [bloom-filter](), [cache](), [cpu](), [cpu-cache](), [data-structures](), [distributed-systems](), [false-negative](), [false-positives](), [memory](), [performance](), [probabilistic](), [systems]() ### AI overview The article explains that standard Bloom filters support membership checks but not deletion because clearing shared bits can create false negatives. Counting Bloom filters enable deletion with counters, but their larger memory footprint can push lookups out of CPU cache and increase latency through random memory access. ### Source excerpt A standard Bloom filter is append-only because bits are shared between items, so clearing a bit for one item can create a false negative for another. Counting Bloom filters fix deletion by replacing each bit with a 4-bit or 8-bit counter, which multiplies the footprint four to eight times, pushes the structure out of L3, and turns each of the k lookups into a roughly 100 ns RAM hit instead of a 10 ns cache hit. ## Cuckoo Filters: Cache-Friendly Membership Checks With Deletions DevFeed: [Cuckoo Filters: Cache-Friendly Membership Checks With Deletions]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [CPU Cache](), [Caching](), [hash]() Tags: [cache](), [capacity](), [cpu-cache](), [cuckoo-filter](), [data-structures](), [false-positive](), [hash](), [insert](), [performance](), [probabilistic](), [spatial-locality](), [systems]() ### AI overview This article explains how Cuckoo filters support deletions while improving CPU cache behavior compared with counting Bloom filters. They check two specific buckets using compact fingerprints, but insertions can fail when kick-out chains exceed their limit, requiring capacity planning or overflow handling. ### Source excerpt A Cuckoo filter stores a one-to-two byte fingerprint in a hash table and finds it by checking exactly two buckets, the primary index and its XOR-derived alternate, instead of k random bit positions scattered across a large array. That spatial locality is the whole win on real CPUs. The trade-off is a hard edge: when the kick-out chain exceeds its limit, the insert fails outright. ## Getting Friendly With CPU Caches DevFeed: [Getting Friendly With CPU Caches]() Original publisher: [Read original article]() Published: 2025-10-21T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [CPU Cache](), [Go Language](), [Data structures](), [Programming](), [Hardware](), [Caching]() Tags: [cache](), [cpu](), [data](), [data-structures](), [efficiency](), [go](), [golang](), [hardware](), [intel](), [latency](), [memory](), [performance](), [processor](), [programming](), [speed]() ### AI overview This tutorial explains how CPU cache behavior and data locality affect application performance. Using a Go case study, it shows that replacing a large embedded array in a user struct with a slice reduced cache misses and improved benchmark performance by more than 40 times. ### Source excerpt Understanding how your data structures interact with hardware is one of the most powerful ways to improve application performance. This blogpost explores how CPU caches influence speed and how thoughtful struct design in Go can yield massive gains. Through a real-world case study, it shows how replacing a large embedded array with a slice improved performance by more than 40 times by reducing cache misses and improving data locality. Originally published in July 2023, its lessons remain highly relevant today for developers optimizing for memory efficiency and cache-aware programming.