- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
// intel CPUID opcode | |
// see: https://en.wikipedia.org/wiki/CPUID | |
// clang -Wall -Wextra -std=c11 cpuinfo.c -o cpuinfo | |
#include <stdint.h> | |
#include <stdio.h> | |
#include <cpuid.h> //macro __cpuid(eaxin, eaxout, ebx, ecx, edx) | |
int main() { | |
{ |
Orthodox C++ (sometimes referred as C+) is minimal subset of C++ that improves C, but avoids all unnecessary things from so called Modern C++. It's exactly opposite of what Modern C++ suppose to be.
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post |
RethinkDB: why we failed |
Posted on Github by Slava Akhmechet
When we [announced][shutdown-announcement] that RethinkDB is shutting down, I promised to write a post-mortem. I took some time to process the experience, and I can now write about it clearly.
- Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained
- Online Controlled Experiments at Large Scale
- Seven Pitfalls to Avoid in Experiments
- 12 A/B Split Testing Mistakes
- 3 Top Mistakes that Invalidate A/B Test Results
- Seven Rules of Thumb for Web Site Experiments
- Online Experimentation at Microsoft
- [Online Controlled Experiments and A/B Tests](http://www.exp-platform.com/Documents/2015%20Online%20Controlled%20Experiments_EncyclopediaOfMLDM.pd
Hello, Rust community!
My name is Hadrien and I am a software performance engineer in a particle physics lab. My daily job is to figure out ways to make scientific software use hardware more efficiently without sacrificing its correctness, primarily by adapting old-ish codebases to the changes that occured in the software and computing landscape since the days where they were designed:
- CPU clock rates and instruction-level parallelism stopped going up, so optimizing code is now more important.
- Multi-core CPUs went from an exotic niche to a cheap commodity, so parallelism is not optional anymore.
- Core counts grow faster than RAM prices go down, so multi-processing is not enough anymore.
- SIMD vectors become wider and wider, so vectorization is not a gimmick anymore.
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