scholarly journals New prospects for computational hydraulics by leveraging high-performance heterogeneous computing techniques

2016 ◽  
Vol 28 (6) ◽  
pp. 977-985 ◽  
Author(s):  
Qiuhua Liang ◽  
Luke Smith ◽  
Xilin Xia
2021 ◽  
Author(s):  
Nicholas Parkyn

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.


Author(s):  
Miguel Bordallo López

Computer vision can be used to increase the interactivity of existing and new camera-based applications. It can be used to build novel interaction methods and user interfaces. The computing and sensing needs of this kind of applications require a careful balance between quality and performance, a practical trade-off. This chapter shows the importance of using all the available resources to hide application latency and maximize computational throughput. The experience gained during the developing of interactive applications is utilized to characterize the constraints imposed by the mobile environment, discussing the most important design goals: high performance and low power consumption. In addition, this chapter discusses the use of heterogeneous computing via asymmetric multiprocessing to improve the throughput and energy efficiency of interactive vision-based applications.


1995 ◽  
Author(s):  
Howard J. Siegel ◽  
John K. Antonio ◽  
Richard C. Metzger ◽  
Min Tan ◽  
Yan A. Li

2014 ◽  
Vol 556-562 ◽  
pp. 3431-3437 ◽  
Author(s):  
Jian Jun Zhang ◽  
Tian Hong Wang ◽  
Yu Zhuo Wang

Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.


2010 ◽  
Vol 18 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Andre R. Brodtkorb ◽  
Christopher Dyken ◽  
Trond R. Hagen ◽  
Jon M. Hjelmervik ◽  
Olaf O. Storaasli

Node level heterogeneous architectures have become attractive during the last decade for several reasons: compared to traditional symmetric CPUs, they offer high peak performance and are energy and/or cost efficient. With the increase of fine-grained parallelism in high-performance computing, as well as the introduction of parallelism in workstations, there is an acute need for a good overview and understanding of these architectures. We give an overview of the state-of-the-art in heterogeneous computing, focusing on three commonly found architectures: the Cell Broadband Engine Architecture, graphics processing units (GPUs), and field programmable gate arrays (FPGAs). We present a review of hardware, available software tools, and an overview of state-of-the-art techniques and algorithms. Furthermore, we present a qualitative and quantitative comparison of the architectures, and give our view on the future of heterogeneous computing.


1995 ◽  
Vol 04 (01n02) ◽  
pp. 33-53 ◽  
Author(s):  
ARVIND K. BANSAL

Associative computation is characterized by seamless intertwining of search-by-content and data parallel computation. The search-by-content paradigm is natural to scalable high performance heterogeneous computing since the use of tagged data avoids the need for explicit addressing mechanisms. In this paper, the author presents an algebra for associative logic programming, an associative resolution scheme, and a generic framework of an associative abstract instruction set. The model is based on the integration of data alignment and the use of two types of bags: data element bags and filter bags of Boolean values to select and restrict computation on data elements. The use of filter bags integrated with data alignment reduces computation and data transfer overhead, and the use of tagged data reduces overhead of preparing data before data transmission. The abstract instruction set has been illustrated by an example. Performance results are presented for a simulation in a homogeneous address space.


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