UPIR: Toward the Design of Unified Parallel Intermediate Representation for Parallel Programming Models

The complexity of heterogeneous computing architectures, as well as the demand for productive and portable parallel application development, have driven the evolution of parallel programming models to become more comprehensive and complex than before. Enhancing the conventional compilation technologies and software infrastructure to be parallelism-aware has become one of the main goals of recent compiler development. In this paper, we propose the design of unified parallel intermediate representation (UPIR) for multiple parallel programming models and for enabling unified compiler transformation for the models. UPIR specifies three commonly used parallelism patterns (SPMD, data and task parallelism), data attributes and explicit data movement and memory management, and synchronization operations used in parallel programming. We demonstrate UPIR via a prototype implementation in the ROSE compiler for unifying IR for both OpenMP and OpenACC and in both C/C++ and Fortran, for unifying the transformation that lowers both OpenMP and OpenACC code to LLVM runtime, and for exporting UPIR to LLVM MLIR dialect.Comment: Typos corrected. Format update

LLOV: A Fast Static Data-Race Checker for OpenMP Programs

In the era of Exascale computing, writing efficient parallel programs is indispensable and at the same time, writing sound parallel programs is highly difficult. While parallel programming is easier with frameworks such as OpenMP, the possibility of data races in these programs still persists. In this paper, we propose a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare our tool with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of our tool is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, this work is the only tool among the state-of-the-art data race checkers that can verify a FORTRAN program to be data race free

LLOV: A Fast Static Data-Race Checker for OpenMP Programs

In the era of Exascale computing, writing efficient parallel programs is indispensable and at the same time, writing sound parallel programs is very difficult. Specifying parallelism with frameworks such as OpenMP is relatively easy, but data races in these programs are an important source of bugs. In this paper, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare LLOV with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of LLOV is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, LLOV is the only tool among the state-of-the-art data race checkers that can verify a C/C++ or FORTRAN program to be data race free.Comment: Accepted in ACM TACO, August 202

Restoring the Broken Covenant Between Compilers and Deep Learning Accelerators

Deep learning accelerators address the computational demands of Deep Neural Networks (DNNs), departing from the traditional Von Neumann execution model. They leverage specialized hardware to align with the application domain's structure. Compilers for these accelerators face distinct challenges compared to those for general-purpose processors. These challenges include exposing and managing more micro-architectural features, handling software-managed scratch pads for on-chip storage, explicitly managing data movement, and matching DNN layers with varying hardware capabilities. These complexities necessitate a new approach to compiler design, as traditional compilers mainly focused on generating fine-grained instruction sequences while abstracting micro-architecture details. This paper introduces the Architecture Covenant Graph (ACG), an abstract representation of an architectural structure's components and their programmable capabilities. By enabling the compiler to work with the ACG, it allows for adaptable compilation workflows when making changes to accelerator design, reducing the need for a complete compiler redevelopment. Codelets, which express DNN operation functionality and evolve into execution mappings on the ACG, are key to this process. The Covenant compiler efficiently targets diverse deep learning accelerators, achieving 93.8% performance compared to state-of-the-art, hand-tuned DNN layer implementations when compiling 14 DNN layers from various models on two different architectures