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Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++

Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++
Conference:

IEEE International Conference on Big Data

Publisher:

IEEE

Location:

Washington D.C, USA

Year:

2016

Date:

5.12.-8.12.2016

Links:PDF
Author(s):

Timo Bingmann, Michael Axtmann, Emanuel Jöbstl, Sebastian Lamm, Huyen Chau Nguyen,
Alexander Noe, Sebastian Schlag, Matthias Stumpp, Tobias Sturm, and Peter Sanders

Speaker:

Timo Bingmann

We present the design and a first performance evaluation of Thrill – a prototype of a general purpose big data processing framework with a convenient data-flow style programming interface. Thrill is somewhat similar to Apache Spark and Apache Flink with at least two main differences. First, Thrill is based on C++ which enables performance advantages due to direct native code compilation, a more cache-friendly memory layout, and explicit memory management. In particular, Thrill uses template meta-programming to compile chains of subsequent local operations into a single binary routine without intermediate buffering and with minimal indirections. Second, Thrill uses arrays rather than multisets as its primary data structure which enables additional operations like sorting, prefix sums, window scans, or combining corresponding fields of several arrays (zipping).
We compare Thrill with Apache Spark and Apache Flink using five kernels from the HiBench suite. Thrill is consistently faster and often several times faster than the other frameworks. At the same time, the source codes have a similar level of simplicity and abstraction.