Showing papers presented at "Parallel and Distributed Processing Techniques and Applications in 2018"

Leveraging Inter-Phase Application Dynamism for Energy-Efficiency Auto-tuning

Abstract: Energy efficiency and consumption are currently the challenging issues in current Petascale and in designing future Exascale systems. The European Union Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energy-efficient Exascale computing) develops a tools-aided online approach to analyze and auto-tune HPC applications for energy efficiency on Exascale systems. It exploits dynamism that occurs due to the variation in the application behavior between iterations of the time loop as well as changing control flow within the time loop. This paper describes the readex_interphase tuning plugin, which analyzes the inter-loop dynamism. The plugin performs clustering using DBSCAN for normalized PAPI metrics, and computes the best tuning parameter settings for each cluster. It verifies the cluster analysis results, and finally computes static and dynamic savings. The inter-phase tuning strategy was evaluated for miniMD and INDEED, and the energy savings obtained validate the effectiveness of this methodology.

Towards Automatic Generation of NoSQL Document-Oriented Models

Abstract: The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.

Understanding the Performances of SMVP on Multiprocessor Platform

Abstract: Sparse Matrix Vector Product (SMVP) is an important kernel in many scientific applications. In this paper we study the performances of this kernel on multiprocessor platform using four different compression format (CSR, CSC, ELL and COO). Our aim is to extract runtime environment parameters, matrix characteristics and algorithm parameters that impact performances. This work is in the context of implementing an auto-tuner system for Optimal sparse Compression Format (OCF) selection.