SABER
| Autor: | Paolo Costa, Peter Pietzuch, Alexander L. Wolf, Matthias Weidlich, Alexandros Koliousis, Raul Fernandez |
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| Přispěvatelé: | Commission of the European Communities |
| Rok vydání: | 2016 |
| Předmět: |
SERVER
Technology SQL Science & Technology Computer Science Information Systems Data parallelism Computer science JOINS Joins 02 engineering and technology Parallel computing doc-ref21 Scheduling (computing) Stream processing Hybrid Scheduling 020204 information systems Server Computer Science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General-purpose computing on graphics processing units computer SYSTEM computer.programming_language |
| Zdroj: | SIGMOD Conference 2016 ACM SIGMOD/PODS Conference |
| Popis: | Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes. |
| Databáze: | OpenAIRE |
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