Showing posts from April, 2016

Paper review. GraphLab: A new Framework for Parallel Machine Learning

This GraphLab paper is from 2010. It is written elegantly and it does a good job of explaining the GraphLab abstraction and foundations. A later VLDB 2012 paper presents how to extend this basic GraphLab abstraction to a distributed GraphLab implementation. It seems like GraphLab has since took off big time. There are workshops and conferences about GraphLab. And GraphLab is now developed by the company Dato (formerly GraphLab inc.). Dato Inc. raised 6.75M$ from Madrona and New Enterprise Associates in A round, and 18.5M$ in B round from Vulcan Capital and Opus Capital, as well as Madrona and New Enterprise Associates. Introduction The motivation for GraphLab was to hit the sweetspot in development of machine learning solutions. "(Then in 2010) Existing high-level parallel abstractions like MapReduce are insufficiently expressive, while low-level tools like MPI and Pthreads leave machine learning (ML) experts repeatedly solving the same design challenges." GraphLab a

Book review: The War Of Art by Steven Pressfield

I read this book recently and liked it a lot. The book is written by Steven Pressfield. He is also the writer of "The Legend of Bagger Vance" and "Gates of Fire" (arguably the best book about Spartans, and is being used as recommended reading in Army academies). Pressfield definitely knows and respects his craft. This book is a call for all people, and creative people and writers in particular, to wake up and realize their calling. The book says: "Most of us have two lives. The life we live, and the unlived life within us." Yeah... About that... I know "self-help" books, and books that use "new-agey" language rub many people the wrong way. I am a pragmatic about that. The way I see it, if I can learn some good paradigms, tips, strategy to become more productive, effective, I can look past some of the turn-offs. The book is organized in 3 parts. The first part talks about resistance, the enemy of creating anything meaningful and wort

Consensus in the Cloud: Paxos Systems Demystified

This our most recent paper, still under submission. It is available as a technical report here. We felt we had to write this paper because we have seen misuses/abuses of Paxos-based coordination services. Glad this is off our chests. Here is the short pitch for the paper. I hope you like it and find it helpful. Coordination and consensus play an important role in datacenter and cloud computing. Examples are leader election, group membership, cluster management, service discovery, resource/access management, and consistent replication of the master nodes in services. Paxos protocols and systems provide a fault-tolerant solution to the distributed consensus problem and have attracted significant attention but they also  generated substantial confusion. Zab, Multi-Paxos, Raft are examples of Paxos protocols.  ZooKeeper, Chubby, etcd are examples of Paxos systems. Paxos systems and Paxos protocols reside in different planes, but even that doesn't prevent these two concepts to b

Paper Review. Petuum: A new platform for distributed machine learning on big data

First there was big data. Industry saw that big data was good. Industry made big data storage systems, NoSQL datastores, to store and access the big data. Industry saw they were good. Industry made big data processing systems, Map Reduce, Spark, etc., to analyze and extract information and insights (CRM, business logistics, etc.) from big data. Industry saw they were good and popular, so machine learning libraries are added to these big data processing systems to provide support for machine learning algorithms and techniques. And here is where this paper makes a case for a redesign for machine learning systems. The big data processing systems produced by the industry are general analytic systems, and are not specifically designed for machine learning from the start. Those are data analytics frameworks first, with some machine learning libraries as add on to tackle machine learning tasks. This paper considers the problem of a clean slate system design for a big data machine learning s

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