Autotuning of Exascale Applications With Anomalies Detection

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.

[1]  Domenico Talia,et al.  Parallel extraction of Regions‐of‐Interest from social media data , 2020 .

[2]  Radu Prodan,et al.  M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications , 2020, 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).

[3]  Vincenzo De Maio,et al.  Multi-objective scheduling of extreme data scientific workflows in Fog , 2020, Future Gener. Comput. Syst..

[4]  Francisco Javier García Blas,et al.  A Novel Data-Centric Programming Model for Large-Scale Parallel Systems , 2019, Euro-Par Workshops.

[5]  Matthias Sax.  Apache Kafka , 2019, Encyclopedia of Big Data Technologies.

[6]  Domenico Talia,et al.  ParSoDA: high-level parallel programming for social data mining , 2018, Social Network Analysis and Mining.

[7]  Prasanna Balaprakash,et al.  Autotuning in High-Performance Computing Applications , 2018, Proceedings of the IEEE.

[8]  Domenico Talia,et al.  G-RoI , 2018, ACM Trans. Knowl. Discov. Data.

[9]  Eugenio Cesario,et al.  SMA4TD: A social media analysis methodology for trajectory discovery in large-scale events , 2017, Online Soc. Networks Media.

[10]  Luis Miguel Sánchez,et al.  A C++ Generic Parallel Pattern Interface for Stream Processing , 2016, ICA3PP.

[11]  Radu Prodan,et al.  Multi-objective Middleware for Distributed VMI Repositories in Federated Cloud Environment , 2016, Scalable Comput. Pract. Exp..

[12]  Luca Benini,et al.  Autotuning and adaptivity approach for energy efficient Exascale HPC systems: The ANTAREX approach , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[13]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[14]  Eugenio Cesario,et al.  Following soccer fans from geotagged tweets at FIFA World Cup 2014 , 2015, 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM).

[15]  Thomas Fahringer,et al.  Multi-Objective Auto-Tuning with Insieme: Optimization and Trade-Off Analysis for Time, Energy and Resource Usage , 2014, Euro-Par.

[16]  Shoaib Kamil,et al.  OpenTuner: An extensible framework for program autotuning , 2014, 2014 23rd International Conference on Parallel Architecture and Compilation (PACT).

[17]  Michael Garland,et al.  Nitro: A Framework for Adaptive Code Variant Tuning , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[18]  Thomas Fahringer,et al.  From single- to multi-objective auto-tuning of programs: Advantages and implications , 2014, Sci. Program..

[19]  Prasanna Balaprakash,et al.  Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy , 2013, PMBS@SC.

[20]  Thomas Fahringer,et al.  A multi-objective auto-tuning framework for parallel codes , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[21]  Ananta Tiwari,et al.  Auto-tuning for Energy Usage in Scientific Applications , 2011, Euro-Par Workshops.

[22]  Cy P. Chan,et al.  PetaBricks , 2010, XRDS.

[23]  Richard W. Vuduc,et al.  Model-driven autotuning of sparse matrix-vector multiply on GPUs , 2010, PPoPP '10.

[24]  José Salvador Sánchez,et al.  Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions , 2009, IbPRIA.

[25]  Chun Chen,et al.  A scalable auto-tuning framework for compiler optimization , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[26]  William J. Dally,et al.  A tuning framework for software-managed memory hierarchies , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[27]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[28]  Du Zhang.  A Fix-Point Semantics for Rule-Base Anomalies , 2007, Int. J. Cogn. Informatics Nat. Intell..

[29]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[30]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[31]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[32]  I-Hsin Chung,et al.  Active Harmony: Towards Automated Performance Tuning , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[33]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[34]  Sylvan Dissoubray,et al.  Simulation of High Performance Computer Systems , 1997, HPCN Europe.

[35]  Ronald Rousseau,et al.  Similarity measures in scientometric research: The Jaccard index versus Salton's cosine formula , 1989, Inf. Process. Manag..