Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
´ÙÁß GPU¸¦ ÀÌ¿ëÇÑ R-treeÀÇ º´·Ä ¹üÀ§ ÁúÀÇ Ã³¸® ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Parallel Range Query Processing with R-tree on Multi-GPUs |
ÀúÀÚ(Author) |
·ùÈ«¼ö
±è¹Îö
ÃÖ¿øÀÍ
Hongsu Ryu
Mincheol Kim
Wonik Choi
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 42 NO. 04 PP. 0522 ~ 0529 (2015. 04) |
Çѱ۳»¿ë (Korean Abstract) |
´ÙÂ÷¿øÀÇ µ¥ÀÌÅ͸¦ »öÀÎÇϱâ À§ÇØ Ã³À½ R-tree°¡ Á¦¾ÈµÈ ÀÌÈÄ ´Ù¾çÇÑ ¹æ¹ýÀ¸·Î ÁúÀÇ ¼º´ÉÀ» Çâ»ó½ÃÅ°±â À§ÇÑ ¸¹Àº ¿¬±¸°¡ ÀÌ·ç¾îÁ³´Ù. ±× °¡¿îµ¥ ´ÙÁßÇÁ·Î¼¼¼¸¦ ÀÌ¿ëÇÑ º´·Ä ±â¹ýÀ¸·Î ÁúÀÇ ¼º´ÉÀ» Çâ»ó½ÃŲ GPU±â¹ÝÀÇ R-tree°¡ Á¦¾ÈµÇ¾ú´Ù. ÇÏÁö¸¸ GPU°¡ °®´Â ¹°¸®Àû ¸Þ¸ð¸® Å©±âÀÇ ÇÑ°è°¡ ÀÖ¾î µ¥ÀÌÅÍÀÇ Å©±â°¡ Á¦ÇѵȴÙ. ÀÌ¿¡ º» ³í¹®¿¡¼´Â ´ÙÁß GPU¸¦ ÀÌ¿ëÇÑ R-treeÀÇ º´·Ä ¹üÀ§ ÁúÀÇ Ã³¸® ±â¹ýÀÎ MGR-tree Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â MGR-tree´Â ±âÁ¸ÀÇ GPU±â¹ÝÀÇ R-tree ÁúÀÇ Ã³¸® ±â¹ýÀ» ±â¹ÝÀ¸·Î ÇÏ¿© ´ÙÁß GPU¿¡¼ ÁúÀÇ Ã³¸®¸¦ °¡´ÉÇÏ°Ô R-treeÀÇ ³ëµå¸¦ ´ÙÁß GPU»ó¿¡ ºÐÇÒÇÏ¿© ºÐ»ê ó¸® ÇÏ¿´´Ù. ½ÇÇèÀ» ÅëÇØ MGR-tree´Â GPU¿¡¼ÀÇ ¼±Çü°Ë»ö¿¡ ºñÇØ ÃÖ´ë 9.1¹è, GPU±â¹Ý R-tree¿¡ ºñÇØ ÃÖ´ë 1.6¹è °¡·®ÀÇ ¼º´ÉÀÌ Çâ»óµÈ °ÍÀ» È®ÀÎÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Ever since the R-tree was proposed to index multi-dimensional data, many efforts have been made to improve its query performances. One common trend to improve query performance is to parallelize query processing with the use of multi-core architectures. To this end, a GPU-base R-tree has been recently proposed. However, even though a GPU-based R-tree can exhibit an improvement in query performance, it is limited in its ability to handle large volumes of data because GPUs have limited physical memory. To address this problem, we propose MGR-tree (Multi-GPU R-tree), which can manage large volumes of data by dividing nodes into multiple GPUs. Our experiments show that MGR-tree is up to 9.1 times faster than a sequential search on a GPU and up to 1.6 times faster than a conventional GPU-based R-tree. |
Å°¿öµå(Keyword) |
ÁúÀÇó¸®
º´·Äó¸®
´ÙÁß GPGPU
GPGPU
R-tree
CUDA
range query
parallel processing
multi GPGPU
|
ÆÄÀÏ÷ºÎ |
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