Tailslayer Dram Hedged Reads

If you're chasing tail latency in memory reads and DRAM refresh stalls are killing your p99s, this C++ library hedges reads across multiple DRAM channels with uncorrelated refresh schedules. You replicate data, pin workers to cores, and race all channels simultaneously so refresh on one can't block you. Built for AMD, Intel, and Graviton with undocumented channel scrambling offsets you tune via benchmarks. The API is template heavy but the pattern is clean: define a signal function that waits for your trigger, a work function that processes the value, call start_workers and let it rip. Aimed squarely at low latency trading and event driven workloads where microseconds matter.

skills.sh installs: 537
GitHub stars: 61
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/trending-skills --skill tailslayer-dram-hedged-reads --agent claude-code

Installs into .claude/skills of the current project.

Files

Tailslayer — DRAM Hedged Read Library

Skill by ara.so — Daily 2026 Skills collection.

Tailslayer is a C++ library that reduces tail latency in RAM reads caused by DRAM refresh stalls. It replicates data across multiple independent DRAM channels with uncorrelated refresh schedules, issues hedged reads across all replicas simultaneously, and returns whichever result responds first — eliminating worst-case stall spikes from DRAM refresh cycles.

Works on AMD, Intel, and AWS Graviton using undocumented channel scrambling offsets.

How It Works

Data is replicated N times, each copy placed on a different DRAM channel
Each replica is monitored by a worker pinned to a separate CPU core
When a read is triggered (via your signal function), all replicas are read simultaneously
Whichever channel responds first wins; the result is passed to your work function
DRAM refresh on one channel cannot stall all channels simultaneously → tail latency is eliminated

Installation

Copy the header into your project

git clone https://github.com/LaurieWired/tailslayer.git
cp -r tailslayer/include/tailslayer /your/project/include/

SKILL.md on GitHub

Details

Repository: github.com/aradotso/trending-skills
First seen: Jun 3, 2026