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120
CacheConscious Structure Layout
, 1999
"... Hardware trends have produced an increasing disparity between processor speeds and memory access times. While a variety of techniques for tolerating or reducing memory latency have been proposed, these are rarely successful for pointermanipulating programs. This paper explores a complementary appro ..."
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Cited by 184 (9 self)
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Hardware trends have produced an increasing disparity between processor speeds and memory access times. While a variety of techniques for tolerating or reducing memory latency have been proposed, these are rarely successful for pointermanipulating programs. This paper explores a complementary approach that attacks the source (poor reference locality) of the problem rather than its manifestation (memory latency). It demonstrates that careful data organization and layout provides an essential mechanism to improve the cache locality of pointermanipulating programs and consequently, their performance. It explores two placement techniquelustering and colorinet improve cache performance by increasing a pointer structure’s spatial and temporal locality, and by reducing cacheconflicts. To reduce the cost of applying these techniques, this paper discusses two strategiescacheconscious reorganization and cacheconscious allocationand describes two semiautomatic toolsccmorph and ccmallocthat use these strategies to produce cacheconscious pointer structure layouts. ccmorph is a transparent tree reorganizer that utilizes topology information to cluster and color the structure. ccmalloc is a cacheconscious heap allocator that attempts to colocate contemporaneously accessed data elements in the same physical cache block. Our evaluations, with microbenchmarks, several small benchmarks, and a couple of large realworld applications, demonstrate that the cacheconscious structure layouts produced by ccmorph and ccmalloc offer large performance benefitn most cases, significantly outperforming stateoftheart prefetching.
Cacheoblivious Btrees
, 2000
"... Abstract. This paper presents two dynamic search trees attaining nearoptimal performance on any hierarchical memory. The data structures are independent of the parameters of the memory hierarchy, e.g., the number of memory levels, the blocktransfer size at each level, and the relative speeds of me ..."
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Cited by 139 (22 self)
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Abstract. This paper presents two dynamic search trees attaining nearoptimal performance on any hierarchical memory. The data structures are independent of the parameters of the memory hierarchy, e.g., the number of memory levels, the blocktransfer size at each level, and the relative speeds of memory levels. The performance is analyzed in terms of the number of memory transfers between two memory levels with an arbitrary blocktransfer size of B; this analysis can then be applied to every adjacent pair of levels in a multilevel memory hierarchy. Both search trees match the optimal search bound of Θ(1+logB+1 N) memory transfers. This bound is also achieved by the classic Btree data structure on a twolevel memory hierarchy with a known blocktransfer size B. The first search tree supports insertions and deletions in Θ(1 + logB+1 N) amortized memory transfers, which matches the Btree’s worstcase bounds. The second search tree supports scanning S consecutive elements optimally in Θ(1 + S/B) memory transfers and supports insertions and deletions in Θ(1 + logB+1 N + log2 N) amortized memory transfers, matching the performance of the Btree for B = B Ω(log N log log N).
GPUTeraSort: High Performance Graphics Coprocessor Sorting for Large Database Management
, 2006
"... We present a new algorithm, GPUTeraSort, to sort billionrecord widekey databases using a graphics processing unit (GPU) Our algorithm uses the data and task parallelism on the GPU to perform memoryintensive and computeintensive tasks while the CPU is used to perform I/O and resource management. We ..."
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Cited by 106 (10 self)
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We present a new algorithm, GPUTeraSort, to sort billionrecord widekey databases using a graphics processing unit (GPU) Our algorithm uses the data and task parallelism on the GPU to perform memoryintensive and computeintensive tasks while the CPU is used to perform I/O and resource management. We therefore exploit both the highbandwidth GPU memory interface and the lowerbandwidth CPU main memory interface and achieve higher memory bandwidth than purely CPUbased algorithms. GPUTeraSort is a twophase task pipeline: (1) read disk, build keys, sort using the GPU, generate runs, write disk, and (2) read, merge, write. It also pipelines disk transfers and achieves nearpeak I/O performance. We have tested the performance of GPUTeraSort on billionrecord files using the standard Sort benchmark. In practice, a 3 GHz Pentium IV PC with $265 NVIDIA 7800 GT GPU is significantly faster than optimized CPUbased algorithms on much faster processors, sorting 60GB for a penny; the best reported PennySort priceperformance. These results suggest that a GPU coprocessor can significantly improve performance on large data processing tasks. 1.
Cache Conscious Indexing for DecisionSupport in Main Memory
, 1999
"... We study indexing techniques for main memory, including hash indexes, binary search trees, Ttrees, B+trees, interpolation search, and binary search on arrays. In a ..."
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Cited by 85 (9 self)
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We study indexing techniques for main memory, including hash indexes, binary search trees, Ttrees, B+trees, interpolation search, and binary search on arrays. In a
Towards a theory of cacheefficient algorithms
 PROCEEDINGS OF THE SYMPOSIUM ON DISCRETE
, 2000
"... We present a model that enables us to analyze the running time of an algorithm on a computer with a memory hierarchy with limited associativity, in terms of various cache parameters. Our cache model, an extension of Aggarwal and Vitter’s I/O model, enables us to establish useful relationships betw ..."
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Cited by 49 (3 self)
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We present a model that enables us to analyze the running time of an algorithm on a computer with a memory hierarchy with limited associativity, in terms of various cache parameters. Our cache model, an extension of Aggarwal and Vitter’s I/O model, enables us to establish useful relationships between the cache complexity and the I/O complexity of computations. As a corollary, we obtain cacheefficient algorithms in the singlelevel cache model for fundamental problems like sorting, FFT, and an important subclass of permutations. We also analyze the averagecase cache behavior of mergesort, show that ignoring associativity concerns could lead to inferior performance, and present supporting experimental evidence. We further extend our model to multiple levels of cache with limited associativity and present optimal algorithms for matrix transpose and sorting. Our techniques may be used for systematic
Fast Priority Queues for Cached Memory
 ACM Journal of Experimental Algorithmics
, 1999
"... This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on kway merging. It improves previous external memory algorithm ..."
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Cited by 48 (8 self)
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This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on kway merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4ary heaps for large inputs
A Dynamically Tuned Sorting Library
, 2004
"... Empirical search is a strategy used during the installation of library generators such as ATLAS, FFTW, and SPIRAL to identify the algorithm or the version of an algorithm that delivers the best performance. In the past, empirical search has been applied almost exclusively to scientific problems. In ..."
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Cited by 44 (7 self)
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Empirical search is a strategy used during the installation of library generators such as ATLAS, FFTW, and SPIRAL to identify the algorithm or the version of an algorithm that delivers the best performance. In the past, empirical search has been applied almost exclusively to scientific problems. In this paper, we discuss the application of empirical search to sorting, which is one of the best understood symbolic computing problems. When contrasted with the dense numerical computations of ATLAS, FFTW, and SPIRAL, sorting presents a new challenge, namely that the relative performance of the algorithms depend not only on the characteristics of the target machine and the size of the input data but also on the distribution of values in the input data set. Empirical search is applied in the study reported here as part of a sorting library generator. The resulting routines dynamically adapt to the characteristics of the input data by selecting the best sorting algorithm from a small set of alternatives. To generate the run time selection mechanism our generator makes use of machine learning to predict the best algorithm as a function of the characteristics of the input data set and the performance of the different algorithms on the target machine. This prediction is based on the data obtained through empirical search at installation time. Our results show that our approach is quite effective. When sorting data inputs of 12M keys with various standard deviations, our adaptive approach selected the best algorithm for all the input data sets and all platforms that we tried in our experiments. The wrong decision could have introduced a performance degradation of up to 133%, with an average value of 44%.
Relational joins on graphics processors
, 2007
"... We present our novel design and implementation of relational join algorithms for newgeneration graphics processing units (GPUs). The new features of such GPUs include support for writes to random memory locations, efficient interprocessor communication through fast shared memory, and a programming ..."
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Cited by 39 (5 self)
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We present our novel design and implementation of relational join algorithms for newgeneration graphics processing units (GPUs). The new features of such GPUs include support for writes to random memory locations, efficient interprocessor communication through fast shared memory, and a programming model for generalpurpose computing. Taking advantage of these new features, we design a set of dataparallel primitives such as scan, scatter and split, and use these primitives to implement indexed or nonindexed nestedloop, sortmerge and hash joins. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU and use parallel computation to effectively hide the memory latency. We have implemented our algorithms on a PC with an NVIDIA G80 GPU and an Intel P4 dualcore CPU. Our GPUbased algorithms are able to achieve 220 times higher performance than their CPUbased counterparts. 1.
WorstCase Efficient ExternalMemory Priority Queues
 In Proc. Scandinavian Workshop on Algorithms Theory, LNCS 1432
, 1998
"... . A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from ..."
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Cited by 37 (3 self)
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. A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from Q. In this paper a priorityqueue implementation is given which is efficient with respect to the number of block transfers or I/Os performed between the internal and external memories of a computer. Let B and M denote the respective capacity of a block and the internal memory measured in elements. The developed data structure handles any intermixed sequence of Insert and DeleteMin operations such that in every disjoint interval of B consecutive priorityqueue operations at most c log M=B N M I/Os are performed, for some positive constant c. These I/Os are divided evenly among the operations: if B c log M=B N M , one I/O is necessary for every B=(c log M=B N M )th operation ...
Introspective Sorting and Selection Algorithms
 Software Practice and Experience
, 1997
"... Quicksort is the preferred inplace sorting algorithm in many contexts, since its average computing time on uniformly distributed inputs is \Theta(N log N) and it is in fact faster than most other sorting algorithms on most inputs. Its drawback is that its worstcase time bound is \Theta(N ). Previo ..."
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Cited by 37 (1 self)
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Quicksort is the preferred inplace sorting algorithm in many contexts, since its average computing time on uniformly distributed inputs is \Theta(N log N) and it is in fact faster than most other sorting algorithms on most inputs. Its drawback is that its worstcase time bound is \Theta(N ). Previous attempts to protect against the worst case by improving the way quicksort chooses pivot elements for partitioning have increased the average computing time too muchone might as well use heapsort, which has a \Theta(N log N) worstcase time bound but is on the average 2 to 5 times slower than quicksort. A similar dilemma exists with selection algorithms (for finding the ith largest element) based on partitioning. This paper describes a simple solution to this dilemma: limit the depth of partitioning, and for subproblems that exceed the limit switch to another algorithm with a better worstcase bound. Using heapsort as the "stopper" yields a sorting algorithm that is just as fast as quicksort in the average case but also has an \Theta(N log N) worst case time bound. For selection, a hybrid of Hoare's find algorithm, which is linear on average but quadratic in the worst case, and the BlumFloydPrattRivestTarjan algorithm is as fast as Hoare's algorithm in practice, yet has a linear worstcase time bound. Also discussed are issues of implementing the new algorithms as generic algorithms and accurately measuring their performance in the framework of the C++ Standard Template Library.