SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications

Abstract

Abstract. HPC application developers, including OpenMP-based application developers, have stepped forward to endeavor the future design trends of exa-scale machines, such as, increased number of threads/cores, heterogeneous architectures, multiple levels of memories, and so forth; and, they have initiated procedures to address application level challenges, such as, data-driven scalability issues, energy consumption requirements, data availability needs, and so forth. Despite the existence of manual performance tuning solutions, users still deem it to be an intricate process. This paper proposes a scalability aware autotuning framework (SCALE-EA) that automatically identifies an efficient number of threads for OpenMP parallel regions using a Firefly Algorithm ( 1. Introduction. High Performance Computing (HPC) application developments are invariably cropping up among various scientific domains, such as, High Energy Physics (HEP), bioinformatics, eyewear computing, visualizations, electronic automation, graph-based machine learning, and so forth. OpenMP based programming model is indeed reaching out to become a prominent programming model among a sector of HPC application developers owing to the adequate doctrine of standards (OpenMP 4.0 and 4.5), ease of use, controlled programming support, smooth applicability to programmers belonging to various scientific disciplines, and due to the notion of having millions of cores in future exascale machines.However, the realization of efficiently utilizing HPC applications in its present form for future large scale machines requires innovative approaches to mitigate the following possible risky scenarios:1. the performance of applications becomes more sensitive to data movement, data availability, data provenance, data management policies, and so forth -a future software-cum-hardware computing system must consider the massive storage options of machines, resiliency nature of applications, dynamic computing behavior of applications, and the dynamic nature of the data access patterns of applications (big data). 2. the current implementations of OpenMP applications might not have considered the design aspects of emerging memory models (including data persistence of modern memory architectures), infrastructural improvements, future parallel data structures, and so forth. 3. the scalability of applications might get an impoverished lead as applications are usually not ported and tested for scalable machines. 4. the energy efficiency of applications could exhibit a daunting scenario when executed on machines with varying degrees of parallelism -smaller or larger. 5. the current OpenMP application developers might not have quantified the possible uncertainties that might evolve due to the underlying future parallel software frameworks. In short, to mitigate these challenges, programmers or developers have to diligently write scalable and energy efficient parallel algorithms by employing the apt scalability features of programming languages and by considering the underlying require...