Framework for Lifecycle Enrichment of HPC Applications Towards Exascale Heterogeneous Architectures

With the advent of accelerators and architectures, researchers are faced with a daunting task to port their existing applications and algorithms to the optimal architecture and programming language. Porting existing applications or a new algorithm is both demanding and time-consuming due to the sheer number of accelerators and architectures plus the number of programming models available per architecture. This problem is further compounded for heterogeneous systems with wide availability of resources and complexity of scientific applications. In this dissertation, we focus on enriching the lifecycle of applications by providing an application to optimal architecture mapping and framework to assist in making the most effective use of resources in a heterogeneous environment. Our Application to Architecture (A2A) framework can be further divided into sub mappings: Qualitative and Quantitative. Our qualitative mapping uses benchmark application analysis to understand the application performance without in depth runtime analysis and is highly suitable for new algorithms and applications. Our quantitative mapping can provide detail numerical performance analysis for porting an application across programming models and architectures. We evaluate our overall framework using various diverse benchmark applications. Lastly, our Heterogeneous Partitioning Framework (HFP) provides existing and new applications the ability to use heterogeneous resources in an efficient manner with minor modifications to the source code in comparison to other frameworks as is shown with two case studies: Climate Earth Science Model (CESM) and GPU-based Gene Network Alignment Tool (G3NA)

Subjective versus objective: classifying analytical models for productive heterogeneous performance prediction

Heterogeneous analytical models are valuable tools that facilitate optimal application tuning via runtime prediction; however, they require several man-hours of effort to understand and employ for meaningful performance prediction. Consequently, developers face the challenge of selecting adequate performance models that best fit their design goals and level of system knowledge. In this research, we present a classification that enables users to select a set of easy-to-use and reliable analytical models for quality performance prediction. These models, which target the general-purpose graphical processing unit (GPGPU)-based systems, are categorized into two primary analytical classes: subjective-analytical and objective-analytical. The subjective-analytical models predict the computation and communication components of an application by describing the system using minimum qualitative relations among the system parameters; whereas the objective-analytical models predict these components by measuring pertinent hardware events using micro-benchmarks. We categorize, enhance, and characterize the existing analytical models for GPGPU computations, network-level, and inter-connect communications to facilitate fast and reliable application performance prediction. We also explore a suitable combination of the aforementioned analytical classes, the hybrid approach, for high-quality performance prediction and report prediction accuracy up to 95 % for several tested GPGPU cluster configurations. The research aims to ultimately provide a collection of easy-to-select analytical models that promote straightforward and accurate performance prediction prior to large-scale implementation