Errors Classification and Static Detection Techniques for Dual-Programming Model (OpenMP and OpenACC)

Abstract

Recently, incorporating more than one programming model into a system designed for high performance computing (HPC) has become a popular solution to implementing parallel systems. Since traditional programming languages, such as C, C++, and Fortran, do not support parallelism, many programmers add one or more programming models to achieve parallelism and accelerate computation efficiently. These models include Open Accelerators (OpenACC) and Open Multi-Processing (OpenMP), which have recently been used with various models, including Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA). Due to the difficulty of predicting the behavior of threads, runtime errors cannot be predicted. The compiler cannot identify runtime errors such as data races, race conditions, deadlocks, or livelocks. Many studies have been conducted on the development of testing tools to detect runtime errors when using programming models, such as the combinations of OpenACC with MPI models and OpenMP with MPI. Although more applications use OpenACC and OpenMP together, no testing tools have been developed to test these applications to date. This paper presents a testing tool for detecting runtime using a static testing technique. This tool can detect actual and potential runtime errors during the integration of the OpenACC and OpenMP models into systems developed in C++. This tool implement error dependency graphs, which are proposed in this paper. Additionally, a dependency graph of the errors is provided, along with a classification of runtime errors that result from combining the two programming models mentioned earlier.

Temporal-Logic-Based Testing Tool Architecture for Dual-Programming Model Systems

“…In addition, the probability of runtime errors is even higher, and their causes vary when using programming models integrated with a programming language. Various papers discuss the common runtime errors that arise when using single- [43], dual- [2], or tri-level programming models [44,45]. This section focuses on the cases in which runtime errors may occur in MPI and OpenMP programming models integrated with C++ programs.…”

“…Exascale systems, the latest HPC technologies, operate at speeds of 10 quintillion (10 18 ) floating-point operations per second (FLOPS) [1]. The high speed of the Exascale systems can be achieved by adding more parallelism to the system using multi-level programming models integrated with the programming language to exploit the available hardware resources effectively [2].…”