6,547 research outputs found
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Human Performance Engineering
Ph.D. students are challenged to discover new ideas, invent new products or break through barriers on existing problems. As a Ph.D. student I am leading a new area of research in the STEM discipline. As an industrial engineer, I am attempting to extend the reach of engineering methods and tools traditionally applied in manufacturing and service-related settings to the area of human performance. Human Performance Engineering, IE 402 008, is a new creative inquiry class that Dr. Kevin Taaffe and I have created. The research includes many focus areas such as quality, decision making, perception, game theory, biology, simulation, and disciplines from engineering to psychology to management and the sciences can all potentially play a role. For the last two semesters I have guided undergraduate students in investigating the cause and effect relationships in human performance in individual or team sports. As a research group, we are challenged to learn materials that are beyond our current knowledge base and to examine psychological and biological factors that affect decisions people make in a competitive environment. Moreover, we aim to quantify the extent to which changes to our mental and physical abilities translate into an increased performance during the sporting event
Best practices for HPM-assisted performance engineering on modern multicore processors
Many tools and libraries employ hardware performance monitoring (HPM) on
modern processors, and using this data for performance assessment and as a
starting point for code optimizations is very popular. However, such data is
only useful if it is interpreted with care, and if the right metrics are chosen
for the right purpose. We demonstrate the sensible use of hardware performance
counters in the context of a structured performance engineering approach for
applications in computational science. Typical performance patterns and their
respective metric signatures are defined, and some of them are illustrated
using case studies. Although these generic concepts do not depend on specific
tools or environments, we restrict ourselves to modern x86-based multicore
processors and use the likwid-perfctr tool under the Linux OS.Comment: 10 pages, 2 figure
Integrated modeling tool for performance engineering of complex computer systems
This report summarizes Advanced System Technologies' accomplishments on the Phase 2 SBIR contract NAS7-995. The technical objectives of the report are: (1) to develop an evaluation version of a graphical, integrated modeling language according to the specification resulting from the Phase 2 research; and (2) to determine the degree to which the language meets its objectives by evaluating ease of use, utility of two sets of performance predictions, and the power of the language constructs. The technical approach followed to meet these objectives was to design, develop, and test an evaluation prototype of a graphical, performance prediction tool. The utility of the prototype was then evaluated by applying it to a variety of test cases found in the literature and in AST case histories. Numerous models were constructed and successfully tested. The major conclusion of this Phase 2 SBIR research and development effort is that complex, real-time computer systems can be specified in a non-procedural manner using combinations of icons, windows, menus, and dialogs. Such a specification technique provides an interface that system designers and architects find natural and easy to use. In addition, PEDESTAL's multiview approach provides system engineers with the capability to perform the trade-offs necessary to produce a design that meets timing performance requirements. Sample system designs analyzed during the development effort showed that models could be constructed in a fraction of the time required by non-visual system design capture tools
Performance Engineering for Real and Complex Tall & Skinny Matrix Multiplication Kernels on GPUs
General matrix-matrix multiplications with double-precision real and complex
entries (DGEMM and ZGEMM) in vendor-supplied BLAS libraries are best optimized
for square matrices but often show bad performance for tall & skinny matrices,
which are much taller than wide. NVIDIA's current CUBLAS implementation
delivers only a fraction of the potential performance as indicated by the
roofline model in this case. We describe the challenges and key characteristics
of an implementation that can achieve close to optimal performance. We further
evaluate different strategies of parallelization and thread distribution, and
devise a flexible, configurable mapping scheme. To ensure flexibility and allow
for highly tailored implementations we use code generation combined with
autotuning. For a large range of matrix sizes in the domain of interest we
achieve at least 2/3 of the roofline performance and often substantially
outperform state-of-the art CUBLAS results on an NVIDIA Volta GPGPU.Comment: 12 pages, 22 figures. Extended version of arXiv:1905.03136v1 for
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Creating Responsive Information Systems with the Help of SSADM
In this paper, a program for a research is outlined. Firstly, the concept of responsive information systems is defined and then the notion of the capacity planning and software performance engineering is clarified. Secondly, the purpose of the proposed methodology of capacity planning, the interface to information systems analysis and development methodologies (SSADM), the advantage of knowledge-based approach is discussed. The interfaces to CASE tools more precisely to data dictionaries or repositories (IRDS) are examined in the context of a certain systems analysis and design methodology (e.g. SSADM)
Filling the Gap: a Tool to Automate Parameter Estimation for Software Performance Models
© 2015 ACM.Software performance engineering heavily relies on application and resource models that enable the prediction of Quality-of-Service metrics. Critical to these models is the accuracy of their parameters, the value of which can change with the application and the resources where it is deployed. In this paper we introduce the Filling-the-gap (FG) tool, which automates the parameter estimation of application performance models. This tool implements a set of statistical routines to estimate the parameters of performance models, which are automatically executed using monitoring information kept in a local database
Filling the Gap: a Tool to Automate Parameter Estimation for Software Performance Models
© 2015 ACM.Software performance engineering heavily relies on application and resource models that enable the prediction of Quality-of-Service metrics. Critical to these models is the accuracy of their parameters, the value of which can change with the application and the resources where it is deployed. In this paper we introduce the Filling-the-gap (FG) tool, which automates the parameter estimation of application performance models. This tool implements a set of statistical routines to estimate the parameters of performance models, which are automatically executed using monitoring information kept in a local database
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