Automatic parallelization of representative-based clustering algorithms for multicore cluster systems

Abstract

Ease of programming and optimal parallel performance have historically been on the opposite side of a trade-off, forcing the user to choose. With the advent of the Big Data era and the rapid evolution of sequential algorithms, the data analytics community can no longer afford the trade-off. We observed that several clustering algorithms often share common traits—particularly, algorithms belonging to the same class of clustering exhibit significant overlap in processing steps. Here, we present our observation on domain patterns in representative-based clustering algorithms and how they manifest as clearly identifiable programming patterns when mapped to a Domain Specific Language (DSL). We have integrated the signatures of these patterns in the DSL compiler for parallelism identification and automatic parallel code generation. The compiler either generates MPI C++ code for distributed memory parallel processing or MPI–OpenMP C++ code for hybrid memory parallel processing, depending upon the target architecture. Our experiments on different state-of-the-art parallelization frameworks show that our system can achieve near-optimal speedup while requiring a fraction of the programming effort, making it an ideal choice for the data analytics community. Results are presented for both distributed and hybrid memory systems.