  Maximum weight matching is one of the most fundamental combinatorial
optimization problems with a wide range of applications in data mining and
bioinformatics. Developing distributed weighted matching algorithms is
challenging due to the sequential nature of efficient algorithms for this
problem. In this paper, we develop a simple distributed algorithm for the
problem on general graphs with approximation guarantee of $2+\varepsilon$ that
(nearly) matches that of the sequential greedy algorithm. A key advantage of
this algorithm is that it can be easily implemented in only two rounds of
computation in modern parallel computation frameworks such as MapReduce. We
also demonstrate the efficiency of our algorithm in practice on various graphs
(some with half a trillion edges) by achieving objective values always close to
what is achievable in the centralized setting.
