Performance Prediction Of Parallel Applications Research Articles

Performance prediction of parallel applications using artificial neuronal network and graph representation

SummaryModeling and predicting the performance of HPC applications is exceedingly important for several purposes. These incorporate scheduling and managing tasks, understanding application behavior, and estimating resource requirements. Many issues impact the performance of parallel applications, including data decomposition, load balancing, and inter processors communication. Predicting the performance of parallelism accurately is a challenging task, especially since it depends on several hardware and software parameters that are difficult to identify. The graph representation is an efficient way to model a parallel application. Based on this representation, this study purports to predict the optimal number of partitions (processors) to run efficiently a parallel application on multi‐core architectures using an artificial neural network. The numerical simulation applications are modeled as a graph of tasks and their associated data. Using the characteristics of the graphs as inputs of the ANN model, we obtained satisfactory results with an accuracy of 91% for the optimal number of processors and 93% for the maximum speedup.

Performance prediction of parallel applications: a systematic literature review

Different techniques for estimating the execution time of parallel applications have been studied for the last 25 years. These approaches have proposed different methods for predicting the performance behaviour of applications. Most of these methods rely on analysing one or more of the following aspects: system workload, application structure, platform system, and the computing resources that the application needs to perform its operations. These elements are used and applied by different methods such as analytic and non-analytic methods. However, no wide-ranging survey of these approaches exists at the time of writing. This paper presents a systematic review of performance prediction methods for parallel applications, which were published in the open literature during the period 2005–2020. We define a classification framework to categorise the reviewed approaches. In addition, we identify some directions and trends in performance prediction as well as some unsolved issues.

Contention-sensitive static performance prediction for parallel distributed applications

Performance prediction for parallel applications running in heterogeneous clusters is difficult to accomplish due to the unpredictable resource contention patterns that can be found in such environments. Typically, components of a parallel application will contend for the use of resources among themselves and with entities external to the application, such as other processes running in the computers of the cluster. The performance modeling approach should be able to represent these sources of contention and to produce an estimate of the execution time, preferably in polynomial time. This paper presents a polynomial time static performance prediction approach in which the prediction takes the form of an interval of values instead of a single value. The extra information given by an interval of values represents the variability of the underlying environment more accurately, as indicated by the practical examples presented.

PAPS — A testbed for performance prediction of parallel applications

The PAPS (Performance Analysis of Parallel Systems) toolset is a testbed for the model based performance prediction of message passing parallel applications executed on private memory multiprocessor computer systems. PAPS allows to describe the execution behavior of the computer hardware and operating system software resources up to a very detailed level. This enables very accurate performance prediction of parallel applications even in the case of substantial performance degradation due to contention for shared resources. In this paper the fundamental design principles and implementation methodologies for the development of the PAPS toolset are presented and the PAPS parallel system specification formalisms are described. A simplified performance study of a parallel Gaussian elimination application on the nCUBE 2 multiprocessor system is used to demonstrate the usage of the tool.

A petri net approach for performance oriented parallel program design

A performance orientation in the development of parallel software is motivated by outlining the misconception of current approaches where performance activities come in at the very end of the development cycle, mainly in terms of measurement or monitoring after the implementation phase. At that time the major part of the development work is already done, and performance pitfalls are very hard to repair—if this is possible at all. A development process for parallel programs that launches performance engineering in the early design phase is proposed, based on a Petri net specification methodology for the performance critical parts of a parallel system. The Petri net formalism is used to define Program Resource Mapping-net (PRM-net) models that serve as an integrated performance model of parallel processing systems, combining performance characteristics of parallel programs (P-net), parallel hardware (R-net), and the assignment of programs to hardware (Mapping) into a single performance model, while simultaneously representing the specification of a parallel application. Predictable parallel algorithm skeletons are worked out as PRM-net models to simultaneously serve as generic program templates to be instantiated according to specific needs, thus representing the starting point of the development process. The systematic integration of a set of tools supporting performance oriented parallel programming results in the CAPSE ( C omputer A ided Parallel S oftware E ngineering ) environment, which is being built around the PRM-net methodology. Specification and performance prediction of parallel applications at the algorithm structure level are demonstrated by example.