Implementation of the K-Means Algorithm on Heterogeneous Devices: A Use Case Based on an Industrial Dataset

This paper presents and analyzes a heterogeneous implementation of an industrial use case based on K-means that targets symmetric multiprocessing (SMP), GPUs and FPGAs. We present how the application can be optimized from an algorithmic point of view and how this optimization performs on two heterogeneous platforms. The presented implementation relies on the OmpSs programming model, which introduces a simplified pragma-based syntax for the communication between the main processor and the accelerators. Performance improvement can be achieved by the programmer explicitly specifying the data memory accesses or copies. As expected, the newer SMP+GPU system studied is more powerful than the older SMP+FPGA system. However the latter is enough to fulfill the requirements of our use case and we show that uses less energy when considering only the active power of the execution.This work is partially supported by the European Union H2020 project AXIOM (grant agreement n. 645496), HiPEAC (grant agreement n. 687698), and Mont-Blanc (grant agreements n. 288777, 610402 and 671697), the Spanish Government Programa Severo Ochoa (SEV-2015-0493), the Spanish Ministry of Science and Technology (TIN2015- 65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaci´o i Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

The AXIOM software layers

AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer ReviewedPostprint (author's final draft

A Short Survey on Data Clustering Algorithms

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end