A Flexible Data Model to Support Multi-domain Performance Analysis

Performance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide‐area platform for high‐performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance‐oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service‐oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi‐experiment performance analysis and parameter studies. AKSUM provides semi‐automatic high‐level performance bottleneck detection through a special‐purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general‐purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and post‐mortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real‐world applications. Copyright © 2005 John Wiley & Sons, Ltd.

Anti-submarine operations and use of sonars in littoral waters is a challenging task. The Norwegian Navy is acquiring new frigates with a sonar suite and an ASW system adapted to the littoral. To assist the navy in optimal usage of the new system in this complex and changing environment, FFI has developed a tool for detailed simulation and analysis of sonar performance. Accurate acoustic modelling in a strongly range-dependent environment is an important element is this tool. The tool will be used during technical and operational evaluation of the new frigates and as an important part in the education of operators and in development of tactics. This paper presents the main properties of the tool and gives examples of prediction of sonar clutter in coastal waters.

Supporting source-level performance analysis of programs written in data-parallel languages requires a unique degree of integration between compilers and performance analysis tools. Compilers for languages such as High Performance Fortran infer parallelism and communication from data distribution directives, thus, performance tools cannot meaningfully relate measurements about these key aspects of execution performance to source-level constructs without substantial compiler support. This paper describes an integrated system for performance analysis of data-parallel programs based on the Rice Fortran 77D compiler and the Illinois Pablo performance analysis toolkit. During code generation, the Fortran D compiler records mapping information and semantic analysis results describing the relationship between performance instrumentation and the original source program. An integrated performance analysis system based on the Pablo toolkit uses this information to correlate the program's dynamic behavior with the data parallel source code. The integrated system provides detailed source-level performance feedback to programmers via a pair of graphical interfaces. Our strategy serves as a model for integration of data-parallel compilers and performance tools.

Applications on today's massively parallel supercomputers rely on performance analysis tools to guide them toward scalable performance on thousands of processors. However, conventional tools for parallel performance analysis have serious problems due to the large data volume that may be required. In this paper, we discuss the scalability issue for MPI performance analysis on Blue Gene/L, the world's fastest supercomputing platform. We present an experimental study of existing MPI performance tools that were ported to BG/L from other platforms. These tools can be classified into two categories: profiling tools that collect timing summaries, and tracing tools that collect a sequence of time-stamped events. Profiling tools produce small data volumes and can scale well, but tracing tools tend to scale poorly. The experimental study discusses the advantages and disadvantages for the tools in the two categories and will be helpful in the future performance tools design.

As access to supercomputing resources is becoming more and more commonplace, performance analysis tools are gaining importance in order to decrease the gap between the application performance and the supercomputers' peak performance. Performance analysis tools allow the analyst to understand the idiosyncrasies of an application in order to improve it. However, these tools require monitoring regions of the application to provide information to the analysts, leaving non-monitored regions of code unknown, which may result in lack of understanding of important regions of the application. In this paper we describe an automated methodology that reports very detailed application insights and improves the analysis experience of performance tools based on traces. We apply this methodology to three production applications and provide suggestions on how to improve their performance. Our methodology uses computation burst clustering and a mechanism called folding. While clustering automatically detects application structure, folding combines instrumentation and sampling to augment the performance analysis details. Folding provides fine grain performance information from coarse grain sampling on iterative applications. Folding results closely resemble the performance data gathered from fine grain sampling with an absolute mean difference less than 5% without overhead of fine grain.

This work conducts a validation study for the XH-59A helicopter using a rigid coaxial rotor system in order to establish the techniques of the conceptual design and performance analysis for the lift-offset compound rotorcraft. As a tool for conceptual design and performance analysis, NDARC (NASA Design and Analysis of Rotorcraft) is used for the present study. An assumed mission profile is considered for the conceptual design of the XH-59A. As a validation result of the design, the dimensions and weight of the XH-59A are appropriately designed when compared to the target values since the relative error is less than 0.5%. Then, performance analyses are conducted for the designed XH-59A model with and without auxiliary propulsion in hover and forward flight conditions. The present analyses show good validity since the prediction results compare well with both the flight test and previous analyses. Therefore, the techniques for the conceptual design and performance analysis of the lift-offset compound helicopter are overall considered to be appropriately established. In addition, this study investigates the influence of the lift-offset on the rotor effective lift-to-drag ratio of the XH-59A helicopter with auxiliary propulsion. As a result, the improvement of the rotor effective lift-to-drag ratio can be obtained by appropriately increasing the lift-offset in high-speed flight.

Graphic modelling technique for software execution time estimation

Data from the distributed control system (DCS) or supervisory control and data acquisition (SCADA) system provide useful information critical to the evaluation of the performance and transportation efficiency of a gas pipeline system with compressor stations. The pipeline performance data provide correction factors for compressors as part of the operation optimization of natural gas transmission pipelines. This paper presents methods, procedures, and an example of model validation-based performance analysis of a gas pipeline based on actual system operational data. An analysis approach based on statistical methods is demonstrated with actual DCS gas pipeline measurement data. These methods offer practical ways to validate the pipeline hydraulics model using the DCS data. The validated models are then used as performance analysis tools in assessing the pipeline hydraulics parameters that influence the pressure drop in the pipeline such as corrosion (inside diameter change), roughness changes, or basic sediment and water deposition.

Driven by fierce global competition, reducing costs while improving customer services by effective supply chain logistics management has become one main concern of most companies. This has created needs for modelling, performance analysis and optimisation tools that can help these companies to improve their logistics networks and operations. In this paper, the advantages of Petri nets as modelling and analysis tools for logistics systems are demonstrated, the recent developments of using Petri net based approaches for modelling, performance analysis, and optimisation of supply chains are reviewed, and future research directions for this important area are explored.

In this project we created a community tool infrastructure for program development tools targeting Petascale class machines and beyond. This includes tools for performance analysis, debugging, and correctness tools, as well as tuning and optimization frameworks. The developed infrastructure provides a comprehensive and extensible set of individual tool building components. We started with the basic elements necessary across all tools in such an infrastructure followed by a set of generic core modules that allow a comprehensive performance analysis at scale. Further, we developed a methodology and workflow that allows others to add or replace modules, to integrate parts into their own tools, or to customize existing solutions. In order to form the core modules, we built on the existing Open|SpeedShop infrastructure and decomposed it into individual modules that match the necessary tool components. At the same time, we addressed the challenges found in performance tools for petascale systems in each module. When assembled, this instantiation of community tool infrastructure provides an enhanced version of Open|SpeedShop, which, while completely different in its architecture, provides scalable performance analysis for petascale applications through a familiar interface. This project also built upon and enhances capabilities and reusability of project partner components as specified in the original project proposal. The overall project team’s work over the project funding cycle was focused on several areas of research, which are described in the following sections. The reminder of this report also highlights related work as well as preliminary work that supported the project. In addition to the project partners funded by the Office of Science under this grant, the project team included several collaborators who contribute to the overall design of the envisioned tool infrastructure. In particular, the project team worked closely with the other two DOE NNSA laboratories Los Alamos and Sandia leveraging co-funding for Krell by ASC’s Common Computing Environment (CCE) program as laid out in the original proposal. The ASC CCE co-funding, coordinated through LLNL, was for 50% of the total project funding, with the ASC CCE portion of the funding going entirely to Krell, while the ASCR funding itself was split between Krell and the funded partners. This report covers the entire project from both funding sources. Additionally, the team leveraged the expertise of software engineering researchers from Carnegie Mellon University, who specialize in software framework design, in order to achieve a broadly acceptable component framework. The Component Based Tool Framework (CBTF) software has been released to the community. Information related to the project and the released software can be found on the CBTF wiki page at: http://sourceforge.net/p/cbtf/wiki/Home.

A composite cylindrical model and its application in analysis of thermal response and performance for energy pile

A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.

Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly variable load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load intensity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.

Analysis and monitoring of traffic measurements can provide a useful tool for diagnostic, troubleshooting and performance analysis in networks. This paper presents a method for traffic analysis, based on the theory of extreme values. Numerical time series obtained from traffic measurements are processed to compute statistical indicators and analyze them to detect significant local features, which may point to some critical network behavior

Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly variable load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load intensity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.