Designing and implementing efficient and
provably correct parallel machine learning (ML) algorithms
can be very challenging. Existing high-level parallel
abstractions like MapReduce are often insufficiently expressive
while low-level tools like MPI and Pthreads leave ML experts
repeatedly solving the same design challenges. By targeting common
patterns in ML, we developed GraphLab, which improves upon
abstractions like MapReduce by compactly expressing asynchronous
iterative algorithms with sparse computational dependencies while
ensuring data consistency and achieving a high degree of parallel
performance.
