Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Hoard: a scalable memory allocator for multithreaded applications
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
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Intel threading building blocks
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IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
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Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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Proceedings of the 19th international conference on Parallel architectures and compilation techniques
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Proceedings of the second international workshop on MapReduce and its applications
MapReduce in MPI for Large-scale graph algorithms
Parallel Computing
MR-search: massively parallel heuristic search
Concurrency and Computation: Practice & Experience
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The standard memory allocators of shared memory systems (SMPs) often provide poor performance, because they do not sufficiently reflect the access latencies of deep NUMA architectures with their on-chip, off-chip, and off-blade communication. We analyze memory allocation strategies for data-intensive MapReduce applications on SMPs with up to 512 cores and 2TB memory. We compare the efficiency of the MapReduce frameworks MR-Search and Phoenix++ and provide performance results on two benchmark applications, k-means and shortest-path search. Already on small SMPs with 128 cores a 6-fold speedup can be achieved by replacing the standard glibc by allocators with pooling strategies. These savings become more pronounced on larger SMPs. We identify two types of overhead: (1) the cost for executing the malloc/free operations and (2) the poor memory locality caused by an ineffective mapping to the underlying memory hierarchy. We give detailed results on the NUMA traffic and show how the cost increases on large SMPs with many cores and a deep NUMA hierarchy. For verification, we run hybrid MPI/OpenMP implementations of the same benchmarks on systems with explicit message passing. The results reveal that neither the hardware nor the Linux kernel constitutes a bottleneck, but only the poor locality of the allocated memory pages.