Experiences on the characterization of parallel applications in embedded systems with Extrae/Paraver

Abstract

Cutting-edge functionalities in embedded systems require the use of parallel architectures to meet their performance requirements. This imposes the introduction of a new layer in the software stacks of embedded systems: the parallel programming model. Unfortunately, the tools used to analyze embedded systems fall short to characterize the performance of parallel applications at a parallel programming model level, and correlate this with information about non-functional requirements such as real-time, energy, memory usage, etc. HPC tools, like Extrae, are designed with that level of abstraction in mind, but their main focus is on performance evaluation. Overall, providing insightful information about the performance of parallel embedded applications at the parallel programming model level, and relate it to the non-functional requirements, is of paramount importance to fully exploit the performance capabilities of parallel embedded architectures. This paper contributes to the state-of-the-art of analysis tools for embedded systems by: (1) analyzing the particular constraints of embedded systems compared to HPC systems (e.g., static setting, restricted memory, limited drivers) to support HPC analysis tools; (2) porting Extrae, a powerful tracing tool from the HPC domain, to the GR740 platform, a SoC used in the space domain; and (3) augmenting Extrae with new features needed to correlate the parallel execution with the following non-functional requirements: energy, temperature and memory usage. Finally, the paper presents the usefulness of Extrae to characterize OpenMP applications and its non-functional requirements, evaluating different aspects of the applications running in the GR740. CCS CONCEPTS • Software and its engineering → Parallel programming languages; Empirical software validation; Software performance; • Computer systems organization → Embedded systems; • Hardware → Power estimation and optimization; Temperature monitoring.

From reactive to proactive load balancing for task‐based parallel applications in distributed memory machines

“…Almost all studies have been introduced in terms of cloud, 38 or cluster management 39 using historic logs or traces 40,41 from profilers, e.g., TAU, 42 Extrae. 43 Li et al introduced an online prediction model to optimize task scheduling as a master-worker model in R language. 44 The master-worker model is well-centralized, but it is irrelevant to our case.…”

“…The purpose is to predict load values based on historical or profiled data using machine learning. Almost all studies have been introduced in terms of cloud, 38 or cluster management 39 using historic logs or traces 40,41 from profilers, e.g., TAU, 42 Extrae 43 . Li et al introduced an online prediction model to optimize task scheduling as a master‐worker model in R language 44 .…”

Vector Extensions in COTS Processors to Increase Guaranteed Performance in Real-Time Systems

“…OpenMP is currently being assessed as a future approach to gain performance building on parallelization, both considering safety-related domains [35,36], and the space domain with the support of the European Space Agency [38]. Hence, while this approach is not yet generally compatible with safety-related systems, we assess its potential in the evaluation section considering both, the use of OpenMP on scalar code, and on code combining OpenMP with VExt.…”

Tracing task‐based runtime systems: Feedbacks from theStarPUcase

“…Execution traces can then be exploited with several tools or manually, as discussed in the next section. Regarding other task-based runtime systems, OMPSS 21 relies on the EXTRAE 22 library to generate traces, browsable by the PARAVER trace explorer, and PARSEC 23 uses an internal system to record events and provide a set of tools to convert resulting trace files in more convenient file formats, such as PAJÉ.…”