Performance Engineering Techniques Research Articles

PHIST

The increasing complexity of hardware and software environments in high-performance computing poses big challenges on the development of sustainable and hardware-efficient numerical software. This article addresses these challenges in the context of sparse solvers. Existing solutions typically target sustainability, flexibility, or performance, but rarely all of them. Our new library PHIST provides implementations of solvers for sparse linear systems and eigenvalue problems. It is a productivity platform for performance-aware developers of algorithms and application software with abstractions that do not obscure the view on hardware-software interaction. The PHIST software architecture and the PHIST development process were designed to overcome shortcomings of existing packages. An interface layer for basic sparse linear algebra functionality that can be provided by multiple backends ensures sustainability, and PHIST supports common techniques for improving scalability and performance of algorithms such as blocking and kernel fusion. We showcase these concepts using the PHIST implementation of a block Jacobi-Davidson solver for non-Hermitian and generalized eigenproblems. We study its performance on a multi-core CPU, a GPU, and a large-scale many-core system. Furthermore, we show how an existing implementation of a block Krylov-Schur method in the Trilinos package Anasazi can benefit from the performance engineering techniques used in PHIST.

Performance measures for supporting project manager decisions

AbstractSoftware measurement is a crucial technology along the software development process, and notably for project management. Decision‐making in project management requires in fact, accurate estimations and the support of metrics to monitor and control the varying factors affecting the development process.Managers must make reliable schedule predictions and optimize personnel utilization. They therefore must be able to dynamically evaluate whether the resources assigned to a job are sufficient and whether the organization structure is adequate to meet the scheduled deadlines.To support managers in these tasks, we propose a method called Propean (for PROject PErformance ANalysis) based on the combination of classical performance engineering techniques and the Unified Modelling Language (UML). The combined application of these disciplines guarantees on one hand, a validated approach for modelling and estimating the ‘Quality of Service’ parameters when they come to the development process. Performance engineering techniques are in fact based on rigorous mathematical formalisms such as queueing networks, stochastic Petri nets and Markov models. On the other hand, the adoption of UML provides managers with an easy‐to‐use front‐end representation of the process closer to the design notations they routinely employ. To illustrate Propean application, in this article, we model the case of a manager who must decide a release date for a product undergoing the testing phase. Copyright © 2007 John Wiley & Sons, Ltd.

Performance engineering in data Grids

AbstractThe vision of Grid computing is to facilitate worldwide resource sharing among distributed collaborations. With the help of numerous national and international Grid projects, this vision is becoming reality and Grid systems are attracting an ever increasing user base. However, Grids are still quite complex software systems whose efficient use is a difficult and error‐prone task. In this paper we present performance engineering techniques that aim to facilitate an efficient use of Grid systems, in particular systems that deal with the management of large‐scale data sets in the tera‐ and petabyte range (also referred to as data Grids). These techniques are applicable at different layers of a Grid architecture and we discuss the tools required at each of these layers to implement them. Having discussed important performance engineering techniques, we investigate how major Grid projects deal with performance issues particularly related to data Grids and how they implement the techniques presented. Copyright © 2005 John Wiley & Sons, Ltd.

The role of performance engineering techniques in the context of the Grid

AbstractPerformance engineering can be described as a collection of techniques and methodologies whose aim is to provide reliable prediction, measurement and validation of the performance of applications on a variety of computing platforms. This paper reviews techniques for performance estimation and performance engineering developed at the University of Southampton and presents application case studies in task scheduling for engineering meta‐applications, and capacity engineering for a financial transaction processing system. These show that it is important to describe performance in terms of a resource model, and that the choice of models may have to trade accuracy for utility in addressing the operational issues. We then present work from the on‐going EU funded Grid project GRIA, and show how lessons learned from the earlier work have been applied to support a viable business model for Grid service delivery to a specified quality of service level. The key in this case is to accept the limitations of performance estimation methods, and design business models that take these limitations into account rather than attempting to provide hard guarantees over performance. We conclude by identifying some of the key lessons learned in the course of our work over many years and suggest possible directions for future investigations. Copyright © 2005 John Wiley & Sons, Ltd.

Performance Modelling — What, Why, When and How

Management of performance risk is achieved by using performance engineering techniques. A major weapon in the performance engineering armoury is performance modelling. Modelling means prediction — estimating the performance of a new system, estimating the impact of change on an existing system or estimating the impact of a change of workload on an existing system. This paper describes how IT systems performance modelling fits into the overall performance engineering approach and its role in performance prediction and risk management. It then focuses on a recent case study illustrating why a particular modelling technique was used, how it was used, and the outcome of the modelling exercise.

UML-based performance engineering possibilities and techniques

An information system's performance in relation to time and resources is largely determined by the design selected in software development. Performance engineering's core concept accounts for a software system's performance from the earliest development phases. Unfortunately, these measures are not performed explicitly during software development, primarily because of deadline pressure, the information system's complexity, or lack of knowledge of what PE means. The authors overview UML-based PE, analyze corresponding approaches, and examine the advantages of the tools available.