“One size fits all” database architectures do not work for DSS
SIGMOD '95 Proceedings of the 1995 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
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Using graphics processors for high performance IR query processing
Proceedings of the 18th international conference on World wide web
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
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
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
High performance technique for database applications using a hybrid GPU/CPU platform
Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI
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
Fast updates on read-optimized databases using multi-core CPUs
Proceedings of the VLDB Endowment
GiST scan acceleration using coprocessors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
Designing a database system for modern processing architectures
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
How to exploit the device diversity and database interaction to propose a generic cost model?
Proceedings of the 17th International Database Engineering & Applications Symposium
Efficient co-processor utilization in database query processing
Information Systems
Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS
Proceedings of the VLDB Endowment
Hardware-oblivious parallelism for in-memory column-stores
Proceedings of the VLDB Endowment
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Specialized processing units such as GPUs or FPGAs provide great opportunities to speed up database operations by exploiting parallelism and relieving the CPU. But utilizing coprocessors efficiently poses major challenges to developers. Besides finding fine-granular data parallel algorithms and tuning them for the available hardware, it has to be decided at runtime which (co)processor should be chosen to execute a specific task. Depending on input parameters, wrong decisions can lead to severe performance degradations since involving coprocessors introduces a significant overhead, e.g., for data transfers. In this paper, we present a framework that automatically learns and adapts execution models for arbitrary algorithms on any (co)processor to find break-even points and support scheduling decisions. We demonstrate its applicability for three common use cases in modern database systems and show how their performance can be improved with wise scheduling decisions.