GPUTeraSort sorts billion-record wide-key databases using the data and task parallelism on the graphics processing unit (GPU) to perform memory-intensive and compute-intensive tasks while the CPU performs I/O and resource management. It exploits both the high-bandwidth GPU memory interface and the lower-bandwidth CPU main memory interface to achieve higher aggregate memory bandwidth than purely CPU-based algorithms. It also pipelines disk transfers to achieve near-peak I/O performance. GPUTera-Sort is a two-phase task pipeline: (1) read disk, build keys, sort using the GPU, generate runs, write disk, and (2) read, merge, write. We tested the performance of GPUTeraSort on billion-record files using the standard Sort benchmark. In practice, a 3 GHz Pentium IV PC with $265 NVIDIA 7800 GT GPU is significantly faster than optimized CPU-based algorithms on much faster processors, sorting 60GB for a penny; the best reported PennySort price-performance. These results suggest that a GPU co-processor can significantly improve performance on large data processing tasks. (GPUTeraSort: High Performance Graphics Coprocessor Sorting for Large Database Management. Naga K. Govindaraju, Jim Gray, Ritesh Kumar, and Dinesh Manocha. Proceedings of ACM SIGMOD 2006.)

Sudipto Guha, Shankar Krishnan and Suresh Venkatasubramanian are presenting a tutorial on the use of the GPU for data visualization and mining at the ACM International Conference on Knowledge Discovery and Data Mining (KDD 2005). (Data Visualization and Mining on the GPU)

This paper by Govindaraju et al. describes a cache-efficient bitonic sorting algorithm on GPUs. The algorithm uses the special purpose texture mapping and programmable hardware to sort IEEE 32-bit floating point data including pointers, and has been used to perform stream data mining and relational database queries. Their results indicate a significant performance improvement over prior CPU-based and GPU-based sorting algorithms. ( GPUSORT: A High Performance Sorting Library”. Also see this Tom’s Hardware article)

These works from the Database Systems Lab at UC Santa Barbara describe how a graphics processor can be effectively used to accelerate the performance of spatial database (GIS databases) operations. Spatial database operations, especially which involve polygon datasets, have been known to be computationally expensive. Sun et al. describe a novel hardware / software co-processing technique which uses basic features of a GPU to reduce the spatial query processing cost. Experimental evaluation shows that their hardware-based approach can significantly outperform leading software-based techniques. (Hardware Acceleration for Spatial Selections and Joins Chengyu Sun, Divyakant Agrawal, Amr El Abbadi. Proceedings of SIGMOD 2003.) However, this evaluation is done in a stand-alone setting where there are no indices, preprocessing or other optimizations available in a database. Bandi et al. extend Sun et al.’s work and integrate the hardware-based technique into a popular commercial database. Rigorous experimentation over real-life data sets shows that the hardware-based approach is very effective and can be complimentary to the optimizations available in a commercial database setting. (Hardware Acceleration in Commercial Databases: A Case Study of Spatial Operations Nagender Bandi, Chengyu Sun, Divyakant Agrawal, Amr El Abbadi to appear in VLDB 2004.)

This paper by Govindaraju et al. describes new algorithms for performing fast computation of several common database operations on commodity graphics processors. Specifically, the paper considers operations such as conjunctive selections, aggregations, and semi-linear queries, which are essential computational components of typical database, data warehousing, and data mining applications. The proposed algorithms take into account some of the limitations of the programming model of current GPUs and perform no data rearrangements. These algorithms have been implemented on a programmable GPU (e.g. NVIDIA’s GeForce FX 5900) and applied to databases consisting of up to a million records. The paper compares their performance with an optimized CPU-based implementation. The experiments indicate that the graphics processor available on commodity computer systems is an effective coprocessor for performing database operations. (Fast Database Operations using Graphics Processors. Naga K. Govindaraju, Brandon Lloyd, Wei Wang, Ming C. Lin, Dinesh Manocha to appear at SIGMOD 2004.)