Distributed Systems Reading List
Introduction I often argue that the toughest thing about distributed systems is changing the way you think. The below is a collection of material I've found useful for motivating these changes. Thought Provokers Ramblings that make you think about the way you design. Not everything can be solved with big servers, databases and transactions. Harvest, Yield and Scalable Tolerant Systems - Real world
Google AI Chief Jeff Dean’s ML System Architecture Blueprint
ML has revolutionized vision, speech and language understanding and is being applied in many other fields. That’s an extraordinary achievement in the tech’s short history and even more impressive considering there is still no dedicated ML hardware. Back in January, Google AI Chief and former head of Google Brain Jeff Dean co-published the paper A New Golden Age in Computer Architecture: Empowering
DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
Large scale machine learning is playing an increasingly important role in improving the quality and monetization of Internet properties. A small number of techniques, such as regression, have proven to be widely applicable across Internet properties and applications. Sibyl is a research project that implements these primitives at scale and is widely used within Google. In this talk I will outline
大規模分散システムのレスポンスを向上させる工夫 - ワザノバ | wazanova
https://www.youtube.com/watch?v=1-3Ahy7Fxsc 1 comment | 1 point | by WazanovaNews ■ comment by Jshiike | 約2時間前 GoogleのJeff Dean（Senior Fellow, システム & インフラグループ）による、Velocity Conference 2014のキーノートスピーチです。 Jeffは、オブジェクト指向言語によるプログラムの最適化で博士号を取得。DEC/Compaqの研究所の勤務をへて、1999年にGoogleに入社。以降、BigTable / MapReduce / Spanner / Google Translate / Google Brainなど、同社の大規模分散システムの構築に一貫して携わってきています。 例えば、検索結果のレスポンスを向上させるには、そ
Inside Sibyl, Google's Massively Parallel Machine Learning Platform
Inside Sibyl, Google’s Massively Parallel Machine Learning Platform If you’ve ever wondered how your spam gets identified in Gmail or where personal video recommendations come from on YouTube, the answer is likely Sibyl, a massively parallel machine learning system that Google developed to make predictions and recommendations with user-specific data culled from its Internet applications. Dr. Tusha
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines
The Three Ages of Google - Batch, Warehouse, Instant - High Scalability -
The world has changed. And some things that should not have been forgotten, were lost. I found these words from the Lord of the Rings echoing in my head as I listened to a fascinating presentation by Luiz André Barroso, Distinguished Engineer at Google, concerning Google's legendary past, golden present, and apocryphal future. His talk, Warehouse-Scale Computing: Entering the Teenage Decade, was g
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GitHub - google/digitalbuildings: Digital Buildings (ontology and SDK) currently being used by Google internally to manage our own buildings.
https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
The Google Stack
Photon: Fault-tolerant and Scalable Joining of Continuous Data Streams