Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
The Potential Dangers of Causal Consistency
and an Explicit Solution
ACM Symposium on Cloud Computing (SOCC'12), October 2012.
Peter Bailis†, Alan Fekete*, Ali Ghodsi†;‡, Joseph M. Hellerstein†, Ion Stoica†
† University of California, Berkeley
* University of Sydney
‡ KTH/Royal Institute of Technology
Causal consistency is the strongest consistency model that is available in the presence of partitions and provides useful semantics for human-facing distributed services. Here, we expose its serious and inherent scalability limitations due to write propagation requirements and traditional dependency tracking mechanisms. As an alternative to classic potential causality, we advocate the use of explicit causality, or applicationdefined happens-before relations. Explicit causality, a subset of potential causality, tracks only relevant dependencies and reduces several of the potential dangers of causal consistency.
KEYWORDS: causality, scalability, explicit causality, data dependencies, weak
consistency, semantic knowledge, convergence
FULL PAPER: pdf