One of the very first GPU sorting algorithms, an adaptation of bitonic sort, was developed by Govindraju et al. [12]. Since this algorithm was developed before the advent of CUDA, the algorithm was implemented using GPU pixel shaders. Zachmann et al. [13] improved on this sort algorithm by using BitonicT rees to reduce the number of comparisons while merging the bitonic sequences. Cederman et al. [7] have adapted quick sort for GPUs. Their adaptation first partitions the sequence to be sorted into subsequences, sorts these subsequences in parallel, and then merges the sorted subsequences in parallel. A hybrid sort algorithm that splits the data using bucket sort and then merges the data using a vectorized version of merge sort is proposed by Sintron et al. [28]. Satish et al. [26] have developed an even faster merge sort. In this merge sort, two sorted sequences A and B are merged by a thread block to produce the sequence C when A and B have less than 256 elements each. Each thread reads an element of A and then does a binary search on the sequence B with that element to determine where it should be placed in the merged sequence C. When the number of elements in a sequence is more than 256, A and B are divided into a set of subsequences by using a set of splitters. The splitters are chosen from the two sequences in such a way that the interval between successive splitters is small enough to be merged by a thread block. The fastest GPU merge sort algorithm known at this time is Warpsort [31]. Warpsort first creates sorted sequences using bitonic sort; each sorted sequence being created by a thread warp. The sorted sequences are merged in pairs until only a small number of sequences remain. The remaining sequences are partitioned into subsequences that can be pairwise merged independently and finally this pairwise merging is done with each warp merging a pair of subsequences. Experimental results reported in [31] indicate that Warpsort is about 30% faster than the merge sort algorithm of [26]. Another comparison-based sort for GPUs-GPU sample sort-was developed by Leischner et al. [20]. Sample sort is reported to be about 30% faster than the merge sort of [26], on average, when the keys are 32-bit integers. This would make sample sort competitive with Warpsort for 32-bit keys. For 64-bit keys, sample sort is twice as fast, on average, as the merge sort of [26].<\/p>\n","protected":false},"excerpt":{"rendered":"
One of the very first GPU sorting algorithms, an adaptation of bitonic sort, was developed by Govindraju et al. [12]. Since this algorithm was developed before the advent of CUDA, the algorithm was implemented using GPU pixel shaders. Zachmann et al. [13] improved on this sort algorithm by using BitonicT rees to reduce the number […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,20,9,199],"class_list":["post-9610","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-sorting","tag-tesla-c1060"],"views":2536,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9610","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9610"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9610\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9610"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}