Publications by Manohar Jonnalagedda

Specialising Parsers for Queries

Many software systems consist of data processing components that analyse large datasets to gather information and learn from these. Often, only part of the data is relevant for analysis. Data processing systems contain an initial preprocessing step that fi ...

EPFL2016

Fold-based fusion as a library: a generative programming pearl

Sandro Stucki, Manohar Jonnalagedda

Fusion is a program optimisation technique commonly implemented using special-purpose compiler support. In this paper, we present an alternative approach, implementing fold-based fusion as a standalone library. We use staging to compose operations on folds ...

ACM Press2015

Staged parser combinators for efficient data processing

Martin Odersky, Tiark Rompf, Sandro Stucki, Manohar Jonnalagedda, Thierry Coppey

Parsers are ubiquitous in computing, and many applications depend on their performance for decoding data efficiently. Parser combinators are an intuitive tool for writing parsers: tight integration with the host language enables grammar specifications to b ...

ACM Press2014

Accelerating parser combinators with macros

Manohar Jonnalagedda, Eric Beguet

Parser combinators provide an elegant way of writing parsers: parser implementations closely follow the structure of the underlying grammar, while accommodating interleaved host language code for data processing. However, the host language features used fo ...

ACM Press2014

Staged Parser Combinators for Efficient Data Processing

Martin Odersky, Tiark Rompf, Sandro Stucki, Manohar Jonnalagedda, Thierry Coppey

Parsers are ubiquitous in computing, and many applications depend on their performance for decoding data efficiently. Parser combinators are an intuitive tool for writing parsers: tight integration with the host language enables grammar specifications to b ...

Assoc Computing Machinery2014

What are the Odds? Probabilistic programming in Scala

Tiark Rompf, Nada Amin, Sandro Stucki, Manohar Jonnalagedda

Probabilistic programming is a powerful high-level paradigm for probabilistic modeling and inference. We present Odds, a small domain-specific language (DSL) for probabilistic programming, embedded in Scala. Odds provides first-class support for random var ...

ACM2013

Optimizing Data Structures in High-Level Programs New Directions for Extensible Compilers based on Staging

Martin Odersky, Tiark Rompf, Nada Amin, Vojin Jovanovic, Manohar Jonnalagedda

High level data structures are a cornerstone of modern programming and at the same time stand in the way of compiler optimizations. In order to reason about user or library-defined data structures, compilers need to be extensible. Common mechanisms to exte ...

Assoc Computing Machinery2013