Topic
Burstsort
About: Burstsort is a research topic. Over the lifetime, 11 publications have been published within this topic receiving 230 citations.
Papers
TL;DR: C-burstsort is introduced, which copies the unexamined tail of each key to the bucket and discards the original key to improve data locality and show that sorting is typically twice as fast as the original burstsort and four to five times faster than multikey quicksort and previous radixsorts.
Abstract: Burstsort is a cache-oriented sorting technique that uses a dynamic trie to efficiently divide large sets of string keys into related subsets small enough to sort in cache. In our original burstsort, string keys sharing a common prefix were managed via a bucket of pointers represented as a list or array; this approach was found to be up to twice as fast as the previous best string sorts, mostly because of a sharp reduction in out-of-cache references. In this paper, we introduce C-burstsort, which copies the unexamined tail of each key to the bucket and discards the original key to improve data locality. On both Intel and PowerPC architectures, and on a wide range of string types, we show that sorting is typically twice as fast as our original burstsort and four to five times faster than multikey quicksort and previous radixsorts. A variant that copies both suffixes and record pointers to buckets, CP-burstsort, uses more memory, but provides stable sorting. In current computers, where performance is limited by memory access latencies, these new algorithms can dramatically reduce the time needed for internal sorting of large numbers of strings.
56 citations
Proceedings Article•
1 Jan 2003TL;DR: This work proposes a new sorting algorithm for strings, burstsort, based on dynamic construction of a compact trie in which strings are kept in buckets, which is simple, fast, and efficient.
Abstract: Ongoing changes in computer architecture are affecting the efficiency of string-sorting algorithms. The size of main memory in typical computers continues to grow but memory accesses require increasing numbers of instruction cycles, which is a problem for the most efficient of the existing string-sorting algorithms as they do not utilize cache well for large data sets. We propose a new sorting algorithm for strings, burstsort, based on dynamic construction of a compact trie in which strings are kept in buckets. It is simple, fast, and efficient. We experimentally explore key implementation options and compare burstsort to existing string-sorting algorithms on large and small sets of strings with a range of characteristics. These experiments show that, for large sets of strings, burstsort is almost twice as fast as any previous algorithm, primarily due to a lower rate of cache miss.
43 citations
TL;DR: New variants of 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 are introduced: SR-burstsort, DR-burstort, and DRL-Burstsort.
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 37p, cache misses further than did the original burstsort, while simultaneously reducing instruction counts by up to 24p. In pathological cases, even further savings can be obtained.
33 citations
1 Jan 2003
TL;DR: It is shown that better choice of data structures further improves the efficiency, at a small additional cost in memory, of the burstsort algorithm.
Abstract: Sorting is a fundamental algorithmic task. Many general-purpose sorting algorithms have been developed, but efficiency gains can be achieved by designing algorithms for specific kinds of data, such as strings. In previous work we have shown that our burstsort, a trie-based algorithm for sorting strings, is for large data sets more efficient than all previous algorithms for this task. In this paper we re-evaluate some of the implementation details of burstsort, in particular the method for managing buckets held at leaves. We show that better choice of data structures further improves the efficiency, at a small additional cost in memory. For sets of around 30,000,000 strings, our improved burstsort is nearly twice as fast as the previous best sorting algorithm.
30 citations
TL;DR: New variants of burstsort, a new string sorting algorithm that on large sets of strings is almost twice as fast as previous algorithms, are introduced: SR-burstsort, DR-burstort, and DRL-Burstsort.
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. The approach in burstsort is to dynamically build 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 cache misses further than did the original burstsort, by up to 37%, while simultaneously reducing instruction counts by up to 24%. In pathological cases, even further savings can be obtained.
18 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2013 | 1 |
| 2010 | 1 |
| 2008 | 1 |
| 2007 | 1 |
| 2005 | 1 |
| 2004 | 3 |