Predictive analysis and optimisation of pipelined wavefront applications using reusable analytic models

Pipelined wavefront computations are an ubiquitous class of high performance parallel algorithms used for the solution of many scientific and engineering applications. In order to aid the design and optimisation of these applications, and to ensure that during procurement platforms are chosen best suited to these codes, there has been considerable research in analysing and evaluating their operational performance. Wavefront codes exhibit complex computation, communication, synchronisation patterns, and as a result there exist a large variety of such codes and possible optimisations. The problem is compounded by each new generation of high performance computing system, which has often introduced a previously unexplored architectural trait, requiring previous performance models to be rewritten and reevaluated. In this thesis, we address the performance modelling and optimisation of this class of application, as a whole. This differs from previous studies in which bespoke models are applied to specific applications. The analytic performance models are generalised and reusable, and we demonstrate their application to the predictive analysis and optimisation of pipelined wavefront computations running on modern high performance computing systems. The performance model is based on the LogGP parameterisation, and uses a small number of input parameters to specify the particular behaviour of most wavefront codes. The new parameters and model equations capture the key structural and behavioural differences among different wavefront application codes, providing a succinct summary of the operations for each application and insights into alternative wavefront application design. The models are applied to three industry-strength wavefront codes and are validated on several systems including a Cray XT3/XT4 and an InfiniBand commodity cluster. Model predictions show high quantitative accuracy (less than 20% error) for all high performance configurations and excellent qualitative accuracy. The thesis presents applications, projections and insights for optimisations using the model, which show the utility of reusable analytic models for performance engineering of high performance computing codes. In particular, we demonstrate the use of the model for: (1) evaluating application configuration and resulting performance; (2) evaluating hardware platform issues including platform sizing, configuration; (3) exploring hardware platform design alternatives and system procurement and, (4) considering possible code and algorithmic optimisations

Parallelising wavefront applications on general-purpose GPU devices

Pipelined wavefront applications form a large portion of the high performance scientific computing workloads at supercomputing centres. This paper investigates the viability of graphics processing units (GPUs) for the acceleration of these codes, using NVIDIA's Compute Unified Device Architecture (CUDA). We identify the optimisations suitable for this new architecture and quantify the characteristics of those wavefront codes that are likely to experience speedups

Bitwise Reproducible task execution on unstructured mesh applications

Many mesh applications use floating point arithmetic which do not necessarily hold the associative laws of algebra. This could cause the application to become unreproducible. In this paper we present some work on generating a method for unstructured mesh applications to provide bitwise reproducibility between separate runs, even if they are started with different number of MPI processes. We implement our work in the OP2 domain-specific library, which provides an API that abstracts the solution of unstructured mesh computations. We carry out a performance analysis of our method applied on two applications: a simple airfoil application, and a more complex Aero application which uses a finite element method and a conjugate-gradient algorithm. We show a 2.37×to 1.49× slowdown on this applications as a price for full bitwise reproducibility

Experiences with porting and modelling wavefront algorithms on many-core architectures

We are currently investigating the viability of many-core architectures for the acceleration of wavefront applications and this report focuses on graphics processing units (GPUs) in particular. To this end, we have implemented NASA’s LU benchmark – a real world production-grade application – on GPUs employing NVIDIA’s Compute Unified Device Architecture (CUDA). This GPU implementation of the benchmark has been used to investigate the performance of a selection of GPUs, ranging from workstation-grade commodity GPUs to the HPC "Tesla” and "Fermi” GPUs. We have also compared the performance of the GPU solution at scale to that of traditional high perfor- mance computing (HPC) clusters based on a range of multi- core CPUs from a number of major vendors, including Intel (Nehalem), AMD (Opteron) and IBM (PowerPC). In previous work we have developed a predictive “plug-and-play” performance model of this class of application running on such clusters, in which CPUs communicate via the Message Passing Interface (MPI). By extending this model to also capture the performance behaviour of GPUs, we are able to: (1) comment on the effects that architectural changes will have on the performance of single-GPU solutions, and (2) make projections regarding the performance of multi-GPU solutions at larger scale

Productivity, performance, and portability for computational fluid dynamics applications

Hardware trends over the last decade show increasing complexity and heterogeneity in high performance computing architectures, which presents developers of CFD applications with three key challenges; the need for achieving good performance, being able to utilise current and future hardware by being portable, and doing so in a productive manner. These three appear to contradict each other when using traditional programming approaches, but in recent years, several strategies such as template libraries and Domain Specific Languages have emerged as a potential solution; by giving up generality and focusing on a narrower domain of problems, all three can be achieved. This paper gives an overview of the state-of-the-art for delivering performance, portability, and productivity to CFD applications, ranging from high-level libraries that allow the symbolic description of PDEs to low-level techniques that target individual algorithmic patterns. We discuss advantages and challenges in using each approach, and review the performance benchmarking literature that compares implementations for hardware architectures and their programming methods, giving an overview of key applications and their comparative performance

On the acceleration of wavefront applications using distributed many-core architectures

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures