Block-Level Parallelism in Parsing Block Structured Languages

Softwares source code is becoming large and complex. Compilation of large base code is a time consuming process. Parallel compilation of code will help in reducing the time complexity. Parsing is one of the phases in compiler in which significant amount of time of compilation is spent. Techniques have already been developed to extract the parallelism available in parser. Current LR(k) parallel parsing techniques either face difficulty in creating Abstract Syntax Tree or requires modification in the grammar or are specific to less expressive grammars. Most of the programming languages like C, ALGOL are block-structured, and in most languages grammars the grammar of different blocks is independent, allowing different blocks to be parsed in parallel. We are proposing a block level parallel parser derived from Incremental Jump Shift Reduce Parser by [13]. Block Parallelized Parser (BPP) can even work as a block parallel incremental parser. We define a set of Incremental Categories and create the partitions of a grammar based on a rule. When parser reaches the start of the block symbol it will check whether the current block is related to any incremental category. If block parallel parser find the incremental category for it, parser will parse the block in parallel. Block parallel parser is developed for LR(1) grammar. Without making major changes in Shift Reduce (SR) LR(1) parsing algorithm, block parallel parser can create an Abstract Syntax tree easily. We believe this parser can be easily extended to LR (k) grammars and also be converted to an LALR (1) parser. We implemented BPP and SR LR(1) parsing algorithm for C Programming Language. We evaluated performance of both techniques by parsing 10 random files from Linux Kernel source. BPP showed 28% and 52% improvement in the case of including header files and excluding header files respectively.