We consider the design of efficient algorithms for a multicore computing environment with a global shared memory and p cores, each having a cache of size M, and with data organized in blocks of size B. We characterize the class of `Hierarchical Balanced Parallel (HBP)' multithreaded computations for multicores. HBP computations are similar to the hierarchical divide & conquer algorithms considered in recent work, but have some additional features that guarantee good performance even when accounting for the cache misses due to false sharing. Most of our HBP algorithms are derived from known cache-oblivious algorithms with high parallelism, however we incorporate new techniques that reduce the effect of false-sharing. Our approach to addressing false sharing costs (or more generally, block misses) is to ensure that any task that can be stolen shares O(1) blocks with other tasks. We use a gapping technique for computations that have larger than O(1) block sharing. We also incorporate the property of limited access writes analyzed in a companion paper, and we bound the cost of accessing shared blocks on the execution stacks of tasks. We present the Priority Work Stealing (PWS) scheduler, and we establish that, given a sufficiently `tall' cache, PWS deterministically schedules several highly parallel HBP algorithms, including those for scans, matrix computations and FFT, with cache misses bounded by the sequential complexity, when accounting for both traditional cache misses and for false sharing. We also present a list ranking algorithm with almost optimal bounds. PWS schedules without using cache or block size information, and uses knowledge of processors only to the extent of determining the available locations from which tasks may be stolen; thus it schedules resource-obliviously
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