Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Run-Time Statistical Estimation of Task Execution Times for Heterogeneous Distributed Computing
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Statistical learning techniques for costing XML queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Accelerating database operators using a network processor
DaMoN '05 Proceedings of the 1st international workshop on Data management on new hardware
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Generic database cost models for hierarchical memory systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Exploring the multiple-GPU design space
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
Modeling GPU-CPU workloads and systems
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Exploring graphics processing units as parallel coprocessors for online aggregation
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
GPU-WAH: applying GPUs to compressing bitmap indexes with word aligned hybrid
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Database compression on graphics processors
Proceedings of the VLDB Endowment
High-throughput transaction executions on graphics processors
Proceedings of the VLDB Endowment
Parallel k-Nearest Neighbor Search on Graphics Hardware
PAAP '10 Proceedings of the 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming
How soccer players would do stream joins
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
X-device query processing by bitwise distribution
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
GiST scan acceleration using coprocessors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
Automatic selection of processing units for coprocessing in databases
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
QuEval: beyond high-dimensional indexing à la carte
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Specialized processing units such as GPUs or FPGAs provide great opportunities to speed up database operations by exploiting parallelism and relieving the CPU. However, distributing a workload on suitable (co-)processors is a challenging task, because of the heterogeneous nature of a hybrid processor/co-processor system. In this paper, we present a framework that automatically learns and adapts execution models for arbitrary algorithms on any (co-)processor. Our physical optimizer uses the execution models to distribute a workload of database operators on available (co-)processing devices. We demonstrate its applicability for two common use cases in modern database systems. Additionally, we contribute an overview of GPU-co-processing approaches, an in-depth discussion of our framework's operator model, the required steps for deploying our framework in practice and the support of complex operators requiring multi-dimensional learning strategies.