Distributive Program Parallelization Using a Suggestion Language

Most computing users today have access to clusters of multi-core computers. To fully utilize a cluster, a programmer must combine two levels of parallelism: shared-memory parallelism within a machine and distributed memory parallelism across machines. Such programming is difficult. Either a user has to mix two programming languages in a single program and use fixed computation and data partitioning between the two, or the user has to rewrite a program from scratch. Even after careful programming, a program may still have hidden concurrency bugs. Users who are accustomed to sequential programming do not find the same level of debugging and performance analysis support especially for a distributed environment. The paper presents a suggestion-based language that enables a user to parallelize a sequential program for distributed execution by inserting hints. The hints are safe against any type of misuse and expressive enough to specify independent, pipelined, and speculative parallel execution on a cluster of multi-core computers. The research is supported by the National Science Foundation (Contract No. CNS-0720796, CNS0834566), IBM CAS Faculty Fellowship, and a gift from Microsoft Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.