panthema / 2016 / 1206-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP
First slide of the talk

Presentation "Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++" at IEEE Big Data 2016

Posted on 2016-12-06 16:00 by Timo Bingmann at Permlink with 0 Comments. Tags: #talk #thrill

Today, I gave a presentation of our paper "Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++" at the IEEE International Conference on Big Data 2016 in Washington D.C., USA. An extended technical report of our paper is also available on this website or on arXiv.

The slides of the presentation at the IEEE conference are available here:
slides-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP-TalkAsGiven.pdf slides-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP-TalkAsGiven.pdf.

Below a longer version of the slides is available for download:
slides-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP.pdf slides-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP.pdf.
These slides contain additional figures which are useful to understand the DIA operations in Thrill, along with many extra design slides omitted from shorter talks.

Download slides-Thrill-High-Performance-Algorithmic-Distributed-Batch-Data-Processing-with-CPP.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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