Scientific Programming - Volume 22, issue 4

Authors: Benkner, Siegfried | Franchetti, Franz | Gerndt, Hans Michael | Hollingsworth, Jeffrey K.

Article Type: Other

Abstract: High Performance Computing architectures have become incredibly complex and exploiting their full potential is becoming more and more challenging. As a consequence, automatic performance tuning (autotuning) of HPC applications is of growing interest and many research groups around the world are currently involved. Autotuning is still a rapidly evolving research field with many different approaches being taken. This special issue features selected papers presented at the Dagstuhl seminar on “Automatic Application Tuning for HPC Architectures” in October 2013, which brought together researchers from the areas of autotuning and performance analysis in order to exchange ideas and steer future collaborations.

DOI: 10.3233/SPR-140391

Citation: Scientific Programming, vol. 22, no. 4, pp. 259-260, 2014

Authors: Martínez, Andrea | Sikora, Anna | César, Eduardo | Sorribes, Joan

Article Type: Research Article

Abstract: The spectacular growth in the number of cores in current supercomputers poses design challenges for the development of performance analysis and tuning tools. To be effective, such analysis and tuning tools must be scalable and be able to manage the dynamic behaviour of parallel applications. In this work, we present ELASTIC, an environment for dynamic tuning of large-scale parallel applications. To be scalable, the architecture of ELASTIC takes the form of a hierarchical tuning network of nodes that perform a distributed analysis and tuning process. Moreover, the tuning network topology can be configured to adapt itself to the size of …the parallel application. To guide the dynamic tuning process, ELASTIC supports a plugin architecture. These plugins, called ELASTIC packages, allow the integration of different tuning strategies into ELASTIC. We also present experimental tests conducted using ELASTIC, showing its effectiveness to improve the performance of large-scale parallel applications. Show more

Keywords: Dynamic tuning, scalability, tuning network, performance tools

DOI: 10.3233/SPR-140392

Citation: Scientific Programming, vol. 22, no. 4, pp. 261-271, 2014

Authors: Schöne, Robert | Treibig, Jan | Dolz, Manuel F. | Guillen, Carla | Navarrete, Carmen | Knobloch, Michael | Rountree, Barry

Article Type: Research Article

Abstract: Energy costs nowadays represent a significant share of the total costs of ownership of High Performance Computing (HPC) systems. In this paper we provide an overview on different aspects of energy efficiency measurement and optimization. This includes metrics that define energy efficiency and a description of common power and energy measurement tools. We discuss performance measurement and analysis suites that use these tools and provide users the possibility to analyze energy efficiency weaknesses in their code. We also demonstrate how the obtained power and performance data can be used to locate inefficient resource usage or to create a model to …predict optimal operation points. We further present interfaces in these suites that allow an automated tuning for energy efficiency and how these interfaces are used. We finally discuss how a hard power limit will change our view on energy efficient HPC in the future. Show more

Keywords: Energy efficiency, energy and performance measurement, HPC, high performance computing, energy optimization tools, energy models, energy-efficiency tuning

DOI: 10.3233/SPR-140393

Citation: Scientific Programming, vol. 22, no. 4, pp. 273-283, 2014

Authors: Durillo, Juan | Fahringer, Thomas

Article Type: Research Article

Abstract: Automatic tuning (auto-tuning) of software has emerged in recent years as a promising method that tries to automatically adapt the behaviour of a program to attain different performance objectives on a given computing system. This method is gaining momentum due to the increasing complexity of modern multicore-based hardware architectures. Many solutions to auto-tuning have been explored ranging from simple random search to more sophisticate methods like machine learning or evolutionary search. To this day, it is still unclear whether these approaches are general enough to encompass all the complexities of the problem (e.g. search space, parameters influencing the search space, …input data sensitivity, etc.), or which approach is best suited for a given problem. Furthermore, the growing interest in auto-tuning a program for several objectives is increasing this confusion even further. The goal of this paper is to formally describe the problem addressed by auto-tuning programs and review existing solutions highlighting the advantages and drawbacks of different techniques for single-objective as well as multi-objective auto-tuning approaches. Show more

Keywords: Program auto-tuning, single-objective optimization, multi-objective optimization

DOI: 10.3233/SPR-140394

Citation: Scientific Programming, vol. 22, no. 4, pp. 285-297, 2014

Authors: Tanaka, Teruo | Otsuka, Ryo | Fujii, Akihiro | Katagiri, Takahiro | Imamura, Toshiyuki

Article Type: Research Article

Abstract: In automatic performance tuning (AT), a primary aim is to optimize performance parameters that are suitable for certain computational environments in ordinary mathematical libraries. For AT, an important issue is to reduce the estimation time required for optimizing performance parameters. To reduce the estimation time, we previously proposed the Incremental Performance Parameter Estimation method (IPPE method). This method estimates optimal performance parameters by inserting suitable sampling points that are based on computational results for a fitting function. As the fitting function, we introduced d-Spline, which is highly adaptable and requires little estimation time. In this paper, we report the implementation …of the IPPE method with ppOpen-AT, which is a scripting language (set of directives) with features that reduce the workload of the developers of mathematical libraries that have AT features. To confirm the effectiveness of the IPPE method for the runtime phase AT, we applied the method to sparse matrix–vector multiplication (SpMV), in which the block size of the sparse matrix structure blocked compressed row storage (BCRS) was used for the performance parameter. The results from the experiment show that the cost was negligibly small for AT using the IPPE method in the runtime phase. Moreover, using the obtained optimal value, the execution time for the mathematical library SpMV was reduced by 44% on comparing the compressed row storage and BCRS (block size 8). Show more

Keywords: Automatic performance tuning, fitting function, SpMV, performance parameter estimation, mathematical library

DOI: 10.3233/SPR-140395

Citation: Scientific Programming, vol. 22, no. 4, pp. 299-307, 2014

Authors: Fursin, Grigori | Miceli, Renato | Lokhmotov, Anton | Gerndt, Michael | Baboulin, Marc | Malony, Allen D. | Chamski, Zbigniew | Novillo, Diego | Del Vento, Davide

Article Type: Research Article

Abstract: Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. …In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material at c-mind.org/repo to set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community. Show more

Keywords: High performance computing, systematic auto-tuning, systematic benchmarking, big data driven optimization, modeling of computer behavior, performance prediction, collaborative knowledge management, public repository of knowledge, NoSQL repository, code and data sharing, specification sharing, collaborative experimentation, machine learning, data mining, multi-objective optimization, model driven optimization, agile development, plugin-based tuning, performance regression buildbot, open access publication model, reproducible research

DOI: 10.3233/SPR-140396

Citation: Scientific Programming, vol. 22, no. 4, pp. 309-329, 2014

Article Type: Other

Citation: Scientific Programming, vol. 22, no. 4, pp. 331-332, 2014