@proceedings{RohrmannSRM17,
Title = {Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems},
Author = {Till Rohrmann,
Sebastian Schelter,
Tilmann Rabl,
Volker Markl},
Year = {2017},
Journal = {Fachtagung für Business, Technologie und Web (BTW)},
Abstract = {In recent years, the generated and collected data is increasing at an almost exponential rate. At the same time, the data’s value has been identified in terms of insights that can be provided. However, retrieving the value requires powerful analysis tools, since valuable insights are buried deep in large amounts of noise. Unfortunately, analytic capacities did not scale well with the growing data. Many existing tools run only on a single computer and are limited in terms of data size by its memory. A very promising solution to deal with large-scale data is scaling systems and exploiting parallelism.
In this paper, we propose Gilbert, a distributed sparse linear algebra system, to decrease the imminent lack of analytic capacities. Gilbert offers a MATLAB○R -like programming language for linear algebra programs, which are automatically executed in parallel. Transparent parallelization is achieved by compiling the linear algebra operations first into an intermediate representation. This language-independent form enables high-level algebraic optimizations. Different optimization strategies are evaluated and the best one is chosen by a cost-based optimizer. The optimized result is
then transformed into a suitable format for parallel execution. Gilbert generates execution plans for Apache Spark○R and Apache Flink○R , two massively parallel dataflow systems. Distributed matrices are represented by square blocks to guarantee a well-balanced trade-off between data parallelism and data granularity.
An exhaustive evaluation indicates that Gilbert is able to process varying amounts of data exceeding the memory of a single computer on clusters of different sizes. Two well known machine learning (ML) algorithms, namely PageRank and Gaussian non-negative matrix factorization (GNMF), are implemented with Gilbert. The performance of these algorithms is compared to optimized implementations based on Spark and Flink. Even though Gilbert is not as fast as the optimized algorithms, it simplifies the development process significantly due to its high-level programming abstraction.},
Url2 = {https://dl.gi.de/handle/20.500.12116/633}
}