|Author||Sinha, Ranjan ♦ Zobel, Justin|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Computer programming, programs & data|
|Subject Keyword||Sorting ♦ Cache-aware ♦ Cache-conscious ♦ Data structure ♦ In-memory ♦ String|
|Abstract||Algorithms for sorting large datasets can be made more efficient with careful use of memory hierarchies and reduction in the number of costly memory accesses. In earlier work, we introduced burstsort, a new string-sorting algorithm that on large sets of strings is almost twice as fast as previous algorithms, primarily because it is more cache efficient. Burstsort dynamically builds a small trie that is used to rapidly allocate each string to a bucket. In this paper, we introduce new variants of our algorithm: SR-burstsort, DR-burstsort, and DRL-burstsort. These algorithms use a random sample of the strings to construct an approximation to the trie prior to sorting. Our experimental results with sets of over 30 million strings show that the new variants reduce, by up to 37%, cache misses further than did the original burstsort, while simultaneously reducing instruction counts by up to 24%. In pathological cases, even further savings can be obtained.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Journal of Experimental Algorithmics (JEA)|
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