Virtual memory for data-parallel computing
Virtual memory for data-parallel computing
Efficient transposition algorithms for large matrices
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
External memory algorithms and data structures: dealing with massive data
ACM Computing Surveys (CSUR)
An Efficient Algorithm for Out-of-Core Matrix Transposition
IEEE Transactions on Computers
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Performance issues of multi-dimensional data analysis
Performance issues of multi-dimensional data analysis
A Fast Computer Method for Matrix Transposing
IEEE Transactions on Computers
Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Permutation is a fundamental operator for array data, with applications in, for example, changing matrix layouts and reorganizing data cubes. We consider the problem of permuting large quantities of data stored on secondary storage that supports fast random block accesses, such as solid state drives and distributed key-value stores. Faster random accesses open up interesting new opportunities for permutation. While external merge sort has often been used for permutation, it is an overkill that fails to exploit the property of permutation fully and carries unnecessary overhead in storing and comparing keys. We propose faster algorithms with lower memory requirements for a large, useful class of permutations. We also tackle practical challenges that traditional permutation algorithms have not dealt with, such as exploiting random block accesses more aggressively, considering the cost asymmetry between reads and writes, and handling arbitrary data dimension sizes (as opposed to perfect powers often assumed by previous work). As a result, our algorithms are faster and more broadly applicable.