We present a novel external sorting algorithm using graphics processors (GPUs) on large databases composed of billions of records and wide keys. Our algorithm uses the data parallelism within a GPU along with task parallelism by scheduling some of the memory-intensive and compute-intensive threads on the GPU. Our new sorting architecture provides multiple memory interfaces on the same PC - a fast and dedicated memory interface on the GPU along with the main memory interface for CPU computations. As a result, we achieve higher memory bandwidth as compared to CPU-based algorithms running on commodity PCs. Our approach takes into account the limited communication bandwidth between the CPU and the GPU, and reduces the data communication between the two processors. Our algorithm also improves the performance of disk transfers and achieves close to peak I/O performance. We have tested the performance of our algorithm on the SortBenchmark and applied it to large databases composed of a few hundred Gigabytes of data. Our results on a 3 GHz Pentium IV PC with $300 NVIDIA 7800 GT GPU indicate a significant performance improvement over optimized CPU-based algorithms on high-end PCs with 3.6 GHz Dual Xeon processors. Our implementation is able to outperform the current high-end PennySort benchmark and results in a higher performance to price ratio. Overall, our results indicate that using a GPU as a co-processor can significantly improve the performance of sorting algorithms on large databases.

@inproceedings{govindaraju2006gputerasort, author = {Govindaraju, Naga and Gray, Jim and Kumar, Ritesh and Manocha, Dinesh}, title = {GPUTeraSort: high performance graphics co-processor sorting for large database management}, booktitle = {Proceedings of the 2006 ACM SIGMOD international conference on Management of data}, series = {SIGMOD '06}, publisher = {ACM}, address = {New York, NY, USA}, year = {2006}, pages = {325--336}, url = {http://doi.acm.org/10.1145/1142473.1142511}, doi = {10.1145/1142473.1142511}, isbn = {1-59593-434-0}, keywords = {databases, gpu, gpu-terasort, graphics_procesor, kdesems2013, seminar, sorting}, abstract = {We present a novel external sorting algorithm using graphics processors (GPUs) on large databases composed of billions of records and wide keys. Our algorithm uses the data parallelism within a GPU along with task parallelism by scheduling some of the memory-intensive and compute-intensive threads on the GPU. Our new sorting architecture provides multiple memory interfaces on the same PC -- a fast and dedicated memory interface on the GPU along with the main memory interface for CPU computations. As a result, we achieve higher memory bandwidth as compared to CPU-based algorithms running on commodity PCs. Our approach takes into account the limited communication bandwidth between the CPU and the GPU, and reduces the data communication between the two processors. Our algorithm also improves the performance of disk transfers and achieves close to peak I/O performance. We have tested the performance of our algorithm on the SortBenchmark and applied it to large databases composed of a few hundred Gigabytes of data. Our results on a 3 GHz Pentium IV PC with $300 NVIDIA 7800 GT GPU indicate a significant performance improvement over optimized CPU-based algorithms on high-end PCs with 3.6 GHz Dual Xeon processors. Our implementation is able to outperform the current high-end PennySort benchmark and results in a higher performance to price ratio. Overall, our results indicate that using a GPU as a co-processor can significantly improve the performance of sorting algorithms on large databases.} }

%0 = inproceedings %A = Govindaraju, Naga and Gray, Jim and Kumar, Ritesh and Manocha, Dinesh %B = Proceedings of the 2006 ACM SIGMOD international conference on Management of data %C = New York, NY, USA %D = 2006 %I = ACM %T = GPUTeraSort: high performance graphics co-processor sorting for large database management %U = http://doi.acm.org/10.1145/1142473.1142511