Communications of the ACM
Limiting Factors of Join Performance on Parallel Processors
Proceedings of the Fifth International Conference on Data Engineering
GAMMA - A High Performance Dataflow Database Machine
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
An Evaluation of Non-Equijoin Algorithms
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Static optimization of conjunctive queries with sliding windows over infinite streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
The VLDB Journal — The International Journal on Very Large Data Bases
Fast and approximate stream mining of quantiles and frequencies using graphics processors
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Optimizing Compiler for the CELL Processor
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Accelerating database operators using a network processor
DaMoN '05 Proceedings of the 1st international workshop on Data management on new hardware
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ViCo: an adaptive distributed video correlation system
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
CPU load shedding for binary stream joins
Knowledge and Information Systems
How soccer players would do stream joins
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
An embedded co-processor for accelerating window joins over uncertain data streams
Microprocessors & Microsystems
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Low-latency and high-throughput processing are key requirements of data stream management systems (DSMSs). Hence, multi-core processors that provide high aggregate processing capacity are ideal matches for executing costly DSMS operators. The recently developed Cell processor is a good example of a heterogeneous multi-core architecture and provides a powerful platform for executing data stream operators with high-performance. On the down side, exploiting the full potential of a multi-core processor like Cell is often challenging, mainly due to the heterogeneous nature of the processing elements, the software managed local memory at the co-processor side, and the unconventional programming model in general. In this paper, we study the problem of scalable execution of windowed stream join operators on multi-core processors, and specifically on the Cell processor. By examining various aspects of join execution flow, we determine the right set of techniques to apply in order to minimize the sequential segments and maximize parallelism. Concretely, we show that basic windows coupled with low-overhead pointer-shifting techniques can be used to achieve efficient join window partitioning, column-oriented join window organization can be used to minimize scattered data transfers, delay-optimized double buffering can be used for effective pipelining, rate-aware batching can be used to balance join throughput and tuple delay, and finally single-instruction multiple-data (SIMD) optimized operator code can be used to exploit data parallelism. Our experimental results show that, following the design guidelines and implementation techniques outlined in this paper, windowed stream joins can achieve high scalability (linear in the number of co-processors) by making efficient use of the extensive hardware parallelism provided by the Cell processor (reaching data processing rates of 驴13 GB/s) and significantly surpass the performance obtained form conventional high-end processors (supporting a combined input stream rate of 2,000 tuples/s using 15 min windows and without dropping any tuples, resulting in 驴8.3 times higher output rate compared to an SSE implementation on dual 3.2 GHz Intel Xeon).