Database Management Systems
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Making snapshot isolation serializable
ACM Transactions on Database Systems (TODS)
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Hardware acceleration in commercial databases: a case study of spatial operations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The end of an architectural era: (it's time for a complete rewrite)
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Cache-oblivious databases: Limitations and opportunities
ACM Transactions on Database Systems (TODS)
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
OLTP through the looking glass, and what we found there
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Low overhead concurrency control for partitioned main memory databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Database compression on graphics processors
Proceedings of the VLDB Endowment
Data-oriented transaction execution
Proceedings of the VLDB Endowment
GViewer: GPU-accelerated graph visualization and mining
SocInfo'11 Proceedings of the Third international conference on Social informatics
VAST-Tree: a vector-advanced and compressed structure for massive data tree traversal
Proceedings of the 15th International Conference on Extending Database Technology
Accelerating pathology image data cross-comparison on CPU-GPU hybrid systems
Proceedings of the VLDB Endowment
Transaction processing using thread-to-metadata
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Efficient co-processor utilization in database query processing
Information Systems
The Yin and Yang of processing data warehousing queries on GPU devices
Proceedings of the VLDB Endowment
Revisiting co-processing for hash joins on the coupled CPU-GPU architecture
Proceedings of the VLDB Endowment
Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS
Proceedings of the VLDB Endowment
Software Transactional Memory for GPU Architectures
Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization
Hi-index | 0.00 |
OLTP (On-Line Transaction Processing) is an important business system sector in various traditional and emerging online services. Due to the increasing number of users, OLTP systems require high throughput for executing tens of thousands of transactions in a short time period. Encouraged by the recent success of GPGPU (General-Purpose computation on Graphics Processors), we propose GPUTx, an OLTP engine performing high-throughput transaction executions on the GPU for in-memory databases. Compared with existing GPGPU studies usually optimizing a single task, transaction executions require handling many small tasks concurrently. Specifically, we propose the bulk execution model to group multiple transactions into a bulk and to execute the bulk on the GPU as a single task. The transactions within the bulk are executed concurrently on the GPU. We study three basic execution strategies (one with locks and the other two lock-free), and optimize them with the GPU features including the hardware support of atomic operations, the massive thread parallelism and the SPMD (Single Program Multiple Data) execution. We evaluate GPUTx on a recent NVIDIA GPU in comparison with its counterpart on a quad-core CPU. Our experimental results show that optimizations on GPUTx significantly improve the throughput, and the optimized GPUTx achieves 4-10 times higher throughput than its CPU-based counterpart on public transaction processing benchmarks.