One Parser to Rule Them All

Despite the long history of research in parsing, constructing parsers for real programming languages remains a difficult and painful task. In the last decades, different parser generators emerged to allow the construction of parsers from a BNF-like specification. However, still today, many parsers are handwritten, or are only partly generated, and include various hacks to deal with different peculiarities in programming languages. The main problem is that current declarative syntax definition techniques are based on pure context-free grammars, while many constructs found in programming languages require context information. In this paper we propose a parsing framework that embraces context information in its core. Our framework is based on data-dependent grammars, which extend context-free grammars with arbitrary computation, variable binding and constraints. We present an implementation of our framework on top of the Generalized LL (GLL) parsing algorithm, and show how common idioms in syntax of programming languages such as (1) lexical disambiguation filters, (2) operator precedence, (3) indentation-sensitive rules, and (4) conditional preprocessor directives can be mapped to data-dependent grammars. We demonstrate the initial experience with our framework, by parsing more than 20000 Java, C#, Haskell, and OCaml source files

The FastLanes compression layout: Decoding >100 billion integers per second with scalar code

The open-source Fast Lanes project aims to improve big data formats, such as Parquet, ORC and columnar database formats, in multiple ways. In this paper, we significantly accelerate decoding of all common Light-Weight Compression (LWC) schemes: DICT, FOR, DELTA and RLE through better data-parallelism. We do so by re-designing the compression layout using two main ideas: (i) generalizing the value interleaving technique in the basic operation of bit-(un)packing by targeting a virtual 1024-bits SIMD register, (ii) reordering the tuples in all columns of a table in the same Unified Transposed Layout that puts tuple chunks in a common "104261537" order (explained in the paper); allowing for maximum independent work for all possible basic SIMD lane widths: 8, 16, 32, and 64 bits. We address the software development, maintenance and future proofness challenges of increasing hardware diversity, by defining a virtual 1024-bits instruction set that consists of simple operators supported by all SIMD dialects; and also, importantly, by scalar code. The interleaved and tuple-reordered layout actually makes scalar decoding faster, extracting more data-parallelism from today’s wide-issue CPUs. Importantly, the scalar version can be fully auto-vectorized by modern compilers, eliminating technical debt in software caused by platform-specific SIMD intrinsics. Micro-benchmarks on Intel, AMD, Apple and AWS CPUs show that Fast Lanes accelerates decoding by factors (decoding > 40 values per CPU cycle). Fast Lanes can make queries faster, as compressing the data reduces bandwidth needs, while decoding is almost free

Operator precedence for data-dependent grammars

Constructing parsers based on declarative specification of operator precedence is a very old research topic, and there are various existing approaches. However, these approaches are either tied to a particular parsing technique, or cannot deal with all corner cases found in programming languages. In this paper we present an implementation of declarative specification of operator precedence for general parsing that (1) is independent of the underlying parsing algorithm, (2) does not require any grammar transformation that increases the size of the grammar, (3) preserves the shape of parse trees of the original, natural grammar, and (4) can deal with intricate cases of operator precedence found in functional programming languages such as OCaml. Our new approach to operator precedence is formulated using data-dependent grammars, which extend context-free grammars with arbitrary computation, variable binding and constraints. We implemented our approach using Iguana, a data-dependent parsing framework, and evaluated it by parsing Java and OCaml source files. The results show that our approach is practical for parsing programming languages with complicated operator precedence rules

The FastLanes Compression Layout: Decoding >100 billion integers per second with scalar code

The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC and columnar database formats, in multiple ways. In this paper, we significantly accelerate decoding of all common Light-Weight Compression (LWC) schemes: DICT, FOR, DELTA and RLE through better data-parallelism. We do so by re-designing the compression layout using two main ideas: (i) generalizing the value interleaving technique in the basic operation of bit-(un)packing by targeting a virtual 1024-bits SIMD register, (ii) reordering the tuples in all columns of a table in the same Unified Transposed Layout that puts tuple chunks in a common “04261537” order (explained in the paper); allowing for maximum independent work for all possible basic SIMD lane widths: 8, 16, 32, and 64 bits. We address the software development, maintenance and futureproofness challenges of increasing hardware diversity, by defining a virtual 1024-bits instruction set that consists of simple operators supported by all SIMD dialects; and also, importantly, by scalar code. The interleaved and tuple-reordered layout actually makes scalar decoding faster, extracting more data-parallelism from today’s wide-issue CPUs. Importantly, the scalar version can be fully auto-vectorized by modern compilers, eliminating technical debt in software caused by platform-specific SIMD intrinsics. Micro-benchmarks on Intel, AMD, Apple and AWS CPUs show that FastLanes accelerates decoding by factors (decoding >40 values per CPU cycle). FastLanes can make queries faster, as compressing the data reduces bandwidth needs, while decoding is almost free

Safe Specification of Operator Precedence Rules

International audienceIn this paper we present an approach to specifying opera- tor precedence based on declarative disambiguation constructs and an implementation mechanism based on grammar rewriting. We identify a problem with existing generalized context-free parsing and disambigua- tion technology: generating a correct parser for a language such as OCaml using declarative precedence specification is not possible without resorting to some manual grammar transformation. Our approach provides a fully declarative solution to operator precedence specification for context-free grammars, is independent of any parsing technology, and is safe in that it guarantees that the language of the resulting grammar will be the same as the language of the specification grammar. We evaluate our new approach by specifying the precedence rules from the OCaml reference manual against the highly ambiguous reference grammar and validate the output of our generated parser

ALP: Adaptive lossless floating-point compression

IEEE 754 doubles do not exactly represent most real values, intro- ducing rounding errors in computations and [de]serialization to text. These rounding errors inhibit the use of existing lightweight compression schemes such as Delta and Frame Of Reference (FOR), but recently new schemes were proposed: Gorilla, Chimp128, Pseu- doDecimals (PDE), Elf and Patas. However, their compression ratios are not better than those of general-purpose compressors such as Zstd; while [de]compression is much slower than Delta and FOR. We propose and evaluate ALP, that significantly improves these previous schemes in both speed and compression ratio (Figure 1). We created ALP after carefully studying the datasets used to eval- uate the previous schemes. To obtain speed, ALP is designed to fit vectorized execution. This turned out to be key for also improv- ing the compression ratio, as we found in-vector commonalities to create compression opportunities. ALP is an adaptive scheme that uses a strongly enhanced version of PseudoDecimals [31 ] to losslessly encode doubles as integers if they originated as decimals, and otherwise uses vectorized compression of the doubles’ front bits. Its high speeds stem from our implementation in scalar code that auto-vectorizes, using building blocks provided by our Fast- Lanes library [ 6], and an efficient two-stage compression algorithm that first samples row-groups and then vectors.</p

InDubio: a combinator library to disambiguate ambiguous grammars

First Online: 29 September 2020To infer an abstract model from source code is one of the main tasks of most software quality analysis methods. Such abstract model is called Abstract Syntax Tree and the inference task is called parsing. A parser is usually generated from a grammar specification of a (programming) language and it converts source code of that language into said abstract tree representation. Then, several techniques traverse this tree to assess the quality of the code (for example by computing source code metrics), or by building new data structures (e.g, flow graphs) to perform further analysis (such as, code cloning, dead code, etc). Parsing is a well established technique. In recent years, however, modern languages are inherently ambiguous which can only be fully handled by ambiguous grammars. In this setting disambiguation rules, which are usually included as part of the grammar specification of the ambiguous language, need to be defined. This approach has a severe limitation: disambiguation rules are not first class citizens. Parser generators offer a small set of rules that can not be extended or changed. Thus, grammar writers are not able to manipulate nor define a new specific rule that the language he is considering requires. In this paper we present a tool, name InDubio, that consists of an extensible combinator library of disambiguation filters together with a generalized parser generator for ambiguous grammars. InDubio defines a set of basic disambiguation rules as abstract syntax tree filters that can be combined into more powerful rules. Moreover, the filters are independent of the parser generator and parsing technology, and consequently, they can be easily extended and manipulated. This paper presents InDubio in detail and also presents our first experimental results.- (undefined

Reusable Components of Semantic Specifications

Semantic specifications of programming languages typically have poor modularity. This hinders reuse of parts of the semantics of one language when specifying a different language – even when the two languages have many constructs in common – and evolution of a language may require major reformulation of its semantics. Such drawbacks have discouraged language developers from using formal semantics to document their designs. In the PLanCompS project, we have developed a component-based approach to semantics. Here, we explain its modularity aspects, and present an illustrative case study: a component-based semantics for Caml Light. We have tested the correctness of the semantics by running programs on an interpreter generated from the semantics, comparing the output with that produced on the standard implementation of the language. Our approach provides good modularity, facilitates reuse, and should support co-evolution of languages and their formal semantics. It could be particularly useful in connection with domain-specific languages and language-driven software development