Arithmetic Research on Data Mining Technology and Associative Rules Mining

2014 ◽  
Vol 556-562 ◽  
pp. 3949-3951
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Knowledge Engineering ◽  
Business Decisions ◽  
Depth Data ◽  
Source Data ◽  
Wide Availability ◽  
High Level ◽  
Performance Computing ◽  
Level Analysis

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

scholarly journals Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs

2021 ◽  
Author(s):  
Umar Ibrahim Minhas ◽  
Roger Woods ◽  
Georgios Karakonstantis
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
System Throughput ◽  
Design Parameters ◽  
Temporal Constraints ◽  
Shared Resources ◽  
Task Processing ◽  
High Level ◽  
Performance Computing

AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

Using Grids for Distributed Knowledge Discovery

2011 ◽  
pp. 284-298 ◽  
Author(s):  
Antonio Congiusta ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
High Performance ◽  
Data Transfer ◽  
Grid Services ◽  
Distributed Knowledge ◽  
Data Intensive ◽  
Knowledge Grid ◽  
Complex Knowledge ◽  
High Level

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.

scholarly journals PAEAN: Portable and scalable runtime support for parallel Haskell dialects

2016 ◽  
Vol 26 ◽  
Author(s):  
JOST BERTHOLD ◽  
HANS-WOLFGANG LOIDL ◽  
KEVIN HAMMOND
Keyword(s):  
Shared Memory ◽  
High Performance ◽  
Parallel Machines ◽  
State Of The Art ◽  
Computing Systems ◽  
Programming Abstraction ◽  
Work Distribution ◽  
High Level ◽  
Parallelism Model ◽  
Performance Computing

AbstractOver time, several competing approaches to parallel Haskell programming have emerged. Different approaches support parallelism at various different scales, ranging from small multicores to massively parallel high-performance computing systems. They also provide varying degrees of control, ranging from completely implicit approaches to ones providing full programmer control. Most current designs assume a shared memory model at the programmer, implementation and hardware levels. This is, however, becoming increasingly divorced from the reality at the hardware level. It also imposes significant unwanted runtime overheads in the form of garbage collection synchronisation etc. What is needed is an easy way to abstract over the implementation and hardware levels, while presenting a simple parallelism model to the programmer. The PArallEl shAred Nothing runtime system design aims to provide a portable and high-level shared-nothing implementation platform for parallel Haskell dialects. It abstracts over major issues such as work distribution and data serialisation, consolidating existing, successful designs into a single framework. It also provides an optional virtual shared-memory programming abstraction for (possibly) shared-nothing parallel machines, such as modern multicore/manycore architectures or cluster/cloud computing systems. It builds on, unifies and extends, existing well-developed support for shared-memory parallelism that is provided by the widely used GHC Haskell compiler. This paper summarises the state-of-the-art in shared-nothing parallel Haskell implementations, introduces the PArallEl shAred Nothing abstractions, shows how they can be used to implement three distinct parallel Haskell dialects, and demonstrates that good scalability can be obtained on recent parallel machines.

Next-generation geophysical modelling

2020 ◽  
Author(s):  
Roman Nuterman ◽  
Dion Häfner ◽  
Markus Jochum ◽  
Brian Vinter
Keyword(s):  
Programming Languages ◽  
High Performance ◽  
Ocean Model ◽  
User Friendliness ◽  
Model Code ◽  
Building Models ◽  
Fortran Implementation ◽  
High Level ◽  
New Generation ◽  
Performance Computing

<div>So far, our pure Python, primitive equation ocean model Veros has been</div><div>about 50% slower than a corresponding Fortran implementation. But recent</div><div>benchmarks show that, thanks to a thriving scientific and machine</div><div>learning library ecosystem, tremendous speed-ups on GPU, and to a lesser</div><div>degree CPU, are within reach. On GPU, we find that the same model code</div><div>can reach a 2-5 times higher energy efficiency compared to a traditional</div><div>Fortran model.</div><div>We thus propose a new generation of geophysical models. One that</div><div>combines high-level abstractions and user friendliness on one hand, and</div><div>that leverages modern developments in high-performance computing on the</div><div>other hand.</div><div>We discuss what there is to gain from building models in high-level</div><div>programming languages, what we have achieved, and what the future holds</div><div>for us and the modelling community.</div>

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