Domain-Specific Multi-Level IR Rewriting for GPU

Most compilers have a single core intermediate representation (IR) (e.g., LLVM) sometimes complemented with vaguely defined IR-like data structures. This IR is commonly low-level and close to machine instructions. As a result, optimizations relying on domain-specific information are either not possible or require complex analysis to recover the missing information. In contrast, multi-level rewriting instantiates a hierarchy of dialects (IRs), lowers programs level-by-level, and performs code transformations at the most suitable level. We demonstrate the effectiveness of this approach for the weather and climate domain. In particular, we develop a prototype compiler and design stencil- and GPU-specific dialects based on a set of newly introduced design principles. We find that two domain-specific optimizations (500 lines of code) realized on top of LLVM’s extensible MLIR compiler infrastructure suffice to outperform state-of-the-art solutions. In essence, multi-level rewriting promises to herald the age of specialized compilers composed from domain- and target-specific dialects implemented on top of a shared infrastructure.

Regression verification for automated evaluation of students programs

Regression verification is a form of software verification based on formal static analysis of code, which is used, since recently, in several domains. In this paper we examine potentials of using it in one novel domain ? in automated evaluation of students programs. We propose an approach that provides precise assessment of functional correctness of student programs (while it does not address nor affect the teaching methodology). We describe our open-source, publicly available implementation of the approach, which is built on top of the compiler infrastructure LLVM and the software verification tool LAV. The results of evaluating the proposed approach on two real-world corpora of student programs and on a number of classic algorithms show that the proposed approach can be used as a precise and reliable supplementary technique in grading of student programs at introductory programming courses, algorithms courses and programming competitions.

Dynamic Compilation of User-Defined Functions in PL/pgSQL Language

Many modern RDBMS provide procedural extensions for SQL programming language, which allow users to perform server-side complex computations. Use of such extensions improves modularity and code reuse, simplifies programming of application logic, and helps developers to avoid network overhead and enhance performance. Interpretation is mostly used to execute SQL queries and procedural extensions code, resulting in significant computational overhead because of indirect function calls and performing of generic checks. Moreover, most RDBMS use different engines for SQL queries execution and procedural extensions code execution, and it is necessary to perform additional computations to switch between different engines. Thus, interpretation of SQL queries combined with interpretation of procedural extensions code may drastically degrade performance of RDBMS. One solution is to use a dynamic compilation technique. In this paper, we describe the technique of dynamic compilation of PL/pgSQL procedural language for the PostgreSQL database system using LLVM compiler infrastructure. Dynamic compiler of PL/pgSQL procedural language is developed as part of PostgreSQL queries dynamic compiler. Proposed technique helps to get rid of computational overhead caused by interpretation usage. Synthetic performance tests show that the developed solution speeds up SQL queries execution by several times.