panthema / 2016 / 0816-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP
A figure from the technical report

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

Posted on 2016-08-20 09:54 by Timo Bingmann at Permlink with 0 Comments. Tags: research c++ thrill

Our technical report on "Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++" is now available on arXiv as 1608.05634 or locally: 1608.05634v1.pdf 1608.05634v1.pdf with source 1608.05634v1.tar.gz 1608.05634v1.tar.gz (780 KiB).

This report is the first technical documentation about our new distributed computing prototype called Thrill. Thrill is written in modern C++14, and open source under the BSD-2 license. More information on Thrill is available from the project homepage.

Thrill's source is available from Github.

Download 1608.05634v1.pdf


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.

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