Parallel functional programming with mutable state

Immutability greatly simplifies the implementation of parallel languages. In the absence of mutable state the language implementation is free to perform parallel operations with fewer locks and fewer restrictions on scheduling and data replication. In the Manticore project, we have achieved nearly p...

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Main Author: Bergstrom, Lars
Format: Dissertation
Language:English
Published: ProQuest Dissertations & Theses 01.01.2013
Subjects:
Computer science
ISBN:1303228564, 9781303228568
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Abstract Immutability greatly simplifies the implementation of parallel languages. In the absence of mutable state the language implementation is free to perform parallel operations with fewer locks and fewer restrictions on scheduling and data replication. In the Manticore project, we have achieved nearly perfect speedups across both Intel and AMD manycore machines on a variety of benchmarks using this approach. There are parallel stateful algorithms, however, that exhibit significantly better performance than the corresponding parallel algorithm without mutable state. For example, in many search problems, the same problem configuration can be reached through multiple execution paths. Parallel stateful algorithms share the results of evaluating the same configuration across threads, but parallel mutation-free algorithms are required to either duplicate work or thread their state through a sequential store. Additionally, in algorithms where each parallel task mutates an independent portion of the data, non-conflicting mutations can be performed in parallel. The parallel state-free algorithm will have to merge each of those changes individually, which is a sequential operation at each step. In this dissertation, we extend Manticore with two techniques that address these problems while preserving its current scalability. Memoization , also known as function caching, is a technique that stores previously returned values from functions, making them available to parallel threads of executions that call that same function with those same values. We have taken this deterministic technique and combined it with a high-performance implementation of a dynamically sized, parallel hash table to provide scalable performance. We have also added mutable state along with two execution models — one of which is deterministic — that allow the user to share arbitrary results across parallel threads under several execution models, all of which preserve the ability to reason locally about the behavior of code. For both of these techniques, we present a detailed description of their implementations, examine a set of relevant benchmarks, and specify their semantics.
AbstractList Immutability greatly simplifies the implementation of parallel languages. In the absence of mutable state the language implementation is free to perform parallel operations with fewer locks and fewer restrictions on scheduling and data replication. In the Manticore project, we have achieved nearly perfect speedups across both Intel and AMD manycore machines on a variety of benchmarks using this approach. There are parallel stateful algorithms, however, that exhibit significantly better performance than the corresponding parallel algorithm without mutable state. For example, in many search problems, the same problem configuration can be reached through multiple execution paths. Parallel stateful algorithms share the results of evaluating the same configuration across threads, but parallel mutation-free algorithms are required to either duplicate work or thread their state through a sequential store. Additionally, in algorithms where each parallel task mutates an independent portion of the data, non-conflicting mutations can be performed in parallel. The parallel state-free algorithm will have to merge each of those changes individually, which is a sequential operation at each step. In this dissertation, we extend Manticore with two techniques that address these problems while preserving its current scalability. Memoization , also known as function caching, is a technique that stores previously returned values from functions, making them available to parallel threads of executions that call that same function with those same values. We have taken this deterministic technique and combined it with a high-performance implementation of a dynamically sized, parallel hash table to provide scalable performance. We have also added mutable state along with two execution models — one of which is deterministic — that allow the user to share arbitrary results across parallel threads under several execution models, all of which preserve the ability to reason locally about the behavior of code. For both of these techniques, we present a detailed description of their implementations, examine a set of relevant benchmarks, and specify their semantics.
Author Bergstrom, Lars
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Title Parallel functional programming with mutable state
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