Formal derivation of efficient parallel programs by construction of list homomorphisms
ACM Transactions on Programming Languages and Systems (TOPLAS)
Parallelization in calculational forms
POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
An Accumulative Parallel Skeleton for All
ESOP '02 Proceedings of the 11th European Symposium on Programming Languages and Systems
Towards automatic parallelization of tree reductions in dynamic programming
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
Parallel skeletons for manipulating general trees
Parallel Computing - Algorithmic skeletons
Automatic inversion generates divide-and-conquer parallel programs
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
The third homomorphism theorem on trees: downward & upward lead to divide-and-conquer
Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Generators-of-generators library with optimization capabilities in fortress
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Towards systematic parallel programming over mapreduce
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
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Parallel skeletons are designed to encourage programmers to build parallel programs from ready-made components for which efficient implementations are known to exist, making both parallel programming and parallelization process simpler. Homomorphism and mapReduce are two known parallel skeletons. Homomorphism, widely studied in the program calculation community for more than twenty years, ideally suits the divide-and-conquer parallel computation paradigm over lists, trees, and other general algebraic data types. In addition, it is also equipped with a set of useful theorems for manipulation of homomorphism. On the other hand, mapReduce is a relatively new skeleton but has emerged as one of the most widely used parallel programming platforms for processing data on terabyte and petabyte scales. It allows for easy parallelization of data intensive computations over many machines, and is used daily at companies such as Yahoo!, Google, Amazon, and Facebook. Despite simplicity of these two skeletons, it still remains as a challenge for a programmer to solve his nontrivial problems with these skeletons. Consider, as an example, the known maximum segment sum problem, whose task is to compute the largest possible sum of a consecutive sublists in a given list. It is actually far from being obvious how this problem can be efficiently solved with mapReduce. In this talk, I would like to show a calculational framework that can support systematic development of efficient parallel programs using homomorphism and mapReduce. Being more constructive, this calculational framework for parallel programming is not only helpful in design of efficient parallel programs, but also promising in construction of parallelizing compile.r