Paperlog
1. 1. 1970
- DS
- An Experimental Study of Compression Methods for Dynamic Tries
- Fast Mergable Integer Maps
- RRB-Trees: Efficient Immutable Vectors
- TRASH A dynamic LC-trie and hash data structure
- Phil Bagwell, Fast And Space Efficient Trie Searches, 2000 (Understanding Clojure's Persistent Vectors, pt. 1 pt. 2)
- Phil Bagwell, Ideal Hash Trees, 2001
- HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm
- HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm
- Optimizing Data Structures in High-Level Programs - New Directions for Extensible Compilers
- Project Lancet: Surgical Precision JIT Compilers
- Scala
- Faster closures in Scala via Stack-allocation
- Bridging Islands of Specialized Code using Macros and Reified Types
- Miniboxing: Improving the Speed to Code Size Tradeoff in Parametric Polymorphism Translations
- RAY: Integrating Rx and Async for Direct-Style Reactive Streams
- HW
- What every programmer should know about memory (viz)
- Instruction latencies and throughput for AMD and Intel x86 processors
- Introduction to x64 Assembly
- Memory Barriers: a Hardware View for Software Hackers
- Instruction tables Lists of instruction latencies, throughputs and micro-operation break-downs for Intel, AMD and VIA CPUs
- Memory system compression and its benefits
- Net
- Fallacies of Distributed Computing Explained
- Consensus Protocols: Paxos
- Internet Census 2012 - Port scanning /0 using insecure embedded devices
- The Akamai Network: A Platform for High-Performance Internet Applications
- PL
- The Next 700 Programming Languages
- The Power of Interoperability: Why Objects Are Inevitable (kontext)
- koka - A language with effect inference
- A Few Useful Things to Know about Machine Learning
- Amazon.com Recommendations - Item-to-Item Collaborative Filtering
- Iterative Ranking from Pair-wise Comparisons
- Analysing and Visualizing Statistical Linked Data
- Big Data beyond MapReduce: Google's Big Data papers
- Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC