@conference {492,
	title = {LightSaber: Efficient Window Aggregation on Multi-core Processors},
	booktitle = {ACM International Conference on Management of Data (SIGMOD)},
	year = {2020},
	month = {06/2020},
	publisher = {ACM},
	organization = {ACM},
	address = {Portland, OR, USA},
	abstract = {<p>Window aggregation queries are a core part of streaming applications. To support window aggregation efficiently, stream processing engines face a trade-off between exploiting parallelism (at the instruction/multi-core levels) and incremental computation (across overlapping windows and queries). Existing engines implement ad-hoc aggregation and parallelization strategies. As a result, they only achieve high performance for specific queries depending on the window definition and the type of aggregation function.</p>
  
<p>We describe a general model for the design space of window aggregation strategies. Based on this, we introduce LightSaber, a new stream processing engine that balances parallelism and incremental processing when executing window aggregation queries on multi-core CPUs. Its design generalises existing approaches: (i) for parallel processing, LightSaber constructs a parallel aggregation tree (PAT) that exploits the parallelism of modern processors. The PAT divides window aggregation into intermediate steps that enable the efficient use of both instruction-level (i.e., SIMD) and task-level (i.e., multi-core) parallelism; and (ii) to generate efficient incremental code from the PAT, LightSaber uses a generalized aggregation graph (GAG), which encodes the low-level data dependencies required to produce aggregates over the stream. A GAG thus generalizes state-of-the-art approaches for incremental window aggregation and supports work-sharing between overlapping windows. LightSaber achieves up to an order of magnitude higher throughput compared to existing systems---on a 16-core server, it processes 470 million records/s with 150μs latency.</p>},
	author = {Georgios Theodorakis and Alexandros Koliousis and Peter Pietzuch and Holger Pirk}
}
