Parallel patterns for heterogeneous CPU/GPU architectures: Structured parallelism from cluster to cloud

Campa, Sonia; Danelutto, Marco; Goli, Mehdi; González–Vélez, Horacio; Popescu, Daniela Elena; Torquati, Massimo

doi:10.1016/j.future.2013.12.038

Cited by 24 publications

(10 citation statements)

References 40 publications

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“…CPU, GPU) and infrastructure (e.g. local clusters, cloud computing) has substantially improved the computing power and raised optimization issues related to the processing of task streams across different architectures and infrastructures (Campa et al 2014). The following sections discuss various advancements in computing and processing for Big Data.…”

Section: Big Data Computing and Processing Infrastructurementioning

confidence: 99%

Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

585

277

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Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers-Big Data and cloud computingand reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

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Section: Big Data Computing and Processing Infrastructurementioning

confidence: 99%

Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

585

277

View full text Add to dashboard Cite

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“…Cole [8] introduced functional parallel patterns as algorithmic skeletons. Multiple implementations of skeleton frameworks have been developed since for clusters and multi-core CPUs, e.g., FastFlow [4] and Muesli [15]. For a comprehensive overview see [17].…”

Section: Skeleton-and Pattern-based Approachesmentioning

confidence: 99%

Runtime Code Generation and Data Management for Heterogeneous Computing in Java

Fumero

Remmelg

Steuwer

et al. 2015

Proceedings of the Principles and Practices of Programming on the Java Platform

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GPUs (Graphics Processing Unit) and other accelerators are nowadays commonly found in desktop machines, mobile devices and even data centres. While these highly parallel processors offer high raw performance, they also dramatically increase program complexity, requiring extra effort from programmers. This results in difficult-to-maintain and non-portable code due to the low-level nature of the languages used to program these devices.This paper presents a high-level parallel programming approach for the popular Java programming language. Our goal is to revitalise the old Java slogan -Write once, run anywhere -in the context of modern heterogeneous systems. To enable the use of parallel accelerators from Java we introduce a new API for heterogeneous programming based on array and functional programming. Applications written with our API can then be transparently accelerated on a device such as a GPU using our runtime OpenCL code generator.In order to ensure the highest level of performance, we present data management optimizations. Usually, data has to be translated (marshalled) between the Java representation and the representation accelerators use. This paper shows how marshal affects runtime and present a novel technique in Java to avoid this cost by implementing our own customised array data structure. Our design hides low level data management from the user making our approach applicable even for inexperienced Java programmers.We evaluated our technique using a set of applications from different domains, including mathematical finance and machine learning. We achieve speedups of up to 500× over sequential and multi-threaded Java code when using an external GPU.

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“…It is more efficient to compute these data on cloud, where Cloud users can concentrate on their own core businesses (Zhao et al, 2013). Cloud computing refers to both the applications delivered as services over the Internet as well as the hardware and system software in data centers that provide these services (Campaa et al, 2014). However, cloud computing systems or clusters are formed by heterogeneous computers.…”

Section: Lps and Swmmentioning

confidence: 99%