Algorithms for scalable synchronization on shared-memory multiprocessors
ACM Transactions on Computer Systems (TOCS)
Atomic snapshots of shared memory
Journal of the ACM (JACM)
Parallel Object Oriented Monte Carlo Simulations
ISCOPE '98 Proceedings of the Second International Symposium on Computing in Object-Oriented Parallel Environments
C++ Template Metaprogramming: Concepts, Tools, and Techniques from Boost and Beyond (C++ in Depth Series)
SNZI: scalable NonZero indicators
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Intel threading building blocks
Intel threading building blocks
Reducers and other Cilk++ hyperobjects
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
TLRW: return of the read-write lock
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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The Boost.Accumulators framework provides C++ template-based support for incremental computation of many important statistical functions, such as maximum, minimum, mean, count, variance, etc. Basic accumulators can be combined to build more sophisticated ones. We explore how this framework can be extended to implement lightweight parallel accumulators that allow multiple threads to Store sample data, and support concurrent GetResult operations that incrementally compute desired functions over the data. Our evaluation shows that our parallel accumulators are scalable and can effectively exploit programmer-supplied knowledge to achieve significant optimizations for some important cases.