Join processing in database systems with large main memories
ACM Transactions on Database Systems (TODS)
On the power of the frame buffer
ACM Transactions on Graphics (TOG)
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
RAID: high-performance, reliable secondary storage
ACM Computing Surveys (CSUR)
A case for intelligent disks (IDISKs)
ACM SIGMOD Record
Active disks: programming model, algorithms and evaluation
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
A cost-effective, high-bandwidth storage architecture
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
Design and evaluation of a smart disk cluster for DSS commercial workloads
Journal of Parallel and Distributed Computing - Special issue on cluster and network-based computing
The Gamma Database Machine Project
IEEE Transactions on Knowledge and Data Engineering
Active Storage for Large-Scale Data Mining and Multimedia
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
Contemporary long-term storage devices feature powerful embedded processors and sizeable memory buffers. Active Storage Devices (ASD) is the hard disk technology that makes use of these significant resources to not only manage the disk operation but also to execute custom application code on large amounts of data. While prior research has shown that ASDs perform exceedingly well with filter-type algorithms, the evaluation of binary-relational operators has been limited. In this paper, we analyze and evaluate inter-operator parallelism of GRACE-based join algorithms that function atop ASDs. We derive accurate cost expressions for existing algorithms and expose performance bottlenecks; upon these findings we propose Active Hash Join, a new algorithm that exploits all system resources. Through experimentation, we confirm that existing algorithms are best suited for systems with either small or large numbers of ASDs. However, we find that the "adaptive" nature of Active Hash Join yields enhanced parallelism in all cases, especially when the aggregate ASD resources are comparable to the main CPU and main memory.