This paper presents the system architecture of the @neurIst project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing ...
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ISBN:
(纸本)9780769531656
This paper presents the system architecture of the @neurIst project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middle-ware technologies used to realise the system and highlights the innovations in @neurIst.
Clustering algorithms are efficient tools for discovering correlations or affinities within large datasets and are the basis of several Artificial Intelligence processes based on data generated by sensor networks. Rec...
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ISBN:
(数字)9781665488020
ISBN:
(纸本)9781665488020
Clustering algorithms are efficient tools for discovering correlations or affinities within large datasets and are the basis of several Artificial Intelligence processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the sensors networks, thus avoiding the stringent requests for low-latency and large-bandwidth networks typical of the Cloud computing model. In such a context, the present work describes a new hybrid version of a clustering algorithm for the NVIDIA Jetson Nano board by integrating two different parallel strategies. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end GPU-based computing systems. The results confirm the possibility of creating intelligent sensor networks where decisions are taken at the data collection points.
For the past five years, I had the very enviable task of leading IBM's effort in DARPA's high Productivity computing Systems (HPCS) program. IBM competed successfully with other contestants in and survived two...
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ISBN:
(纸本)1424409101
For the past five years, I had the very enviable task of leading IBM's effort in DARPA's high Productivity computing Systems (HPCS) program. IBM competed successfully with other contestants in and survived two down-selects, producing along the way ground-breaking research for peta-scale systems aimed at changing the status quo in high end computing. The HPCS program is unique in that it states productivity as a broader definition of the system value than justperformance. Commercial viability is another goal, meant to add realism and produce usable systems at the end of the program with productivity and performance goals that well exceed the projected improvements using today's technology. This unprecedented mix adds interesting and challenging constraints on the research program, and the traditional ways of approaching the problem do not apply. This talk will give an overview of the challenges of running projects of this kind, and gives a forward looking statement about the future of the program and its projected impact on the industry and the academic communities.
Distributed stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to...
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ISBN:
(纸本)9781728141947
Distributed stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. In this work, we employ Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) to reassign operators during application runtime. Experimental results show that RL and MCTS algorithms perform better than traditional placement techniques. We also introduce an optimisation to a MCTS algorithm, called MCTS-Best-UCT, that achieves similar latency with fewer operator migrations and faster execution time. In certain scenarios, the time needed by MCTS-Best-UCT to find the best end-to-end latency is at least 33% smaller than the time required by the other algorithms.
In this paper we show how Grid computing can be used to improve the operation of a medical image search system. The paper introduces the basic principles of a content-based image retrieval (CBIR) system and identifies...
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ISBN:
(纸本)9780769531656
In this paper we show how Grid computing can be used to improve the operation of a medical image search system. The paper introduces the basic principles of a content-based image retrieval (CBIR) system and identifies the computationally challenging tasks in the system. For the computationally challenging tasks an efficient design is proposed that uses distributed Grid computing to carry out the image processing in a distributed and efficient way. The algorithms of the search system are executed by using a real medical image collection as input and a Grid computing infrastructure to provide the needed computing power. Finally, the results show how the image processing task that required tens of hours to complete can be processed by using only a fraction of the originally required computing time.
In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label prop...
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ISBN:
(纸本)9781728157245
In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide a detailed comparative analysis of the three algorithms' segmentation performance and properties. Our analysis identified application-specific regimes that govern the relative performance of the analyzed algorithms.
LOBPCG is a numerical eigensolver which can be parallelly implemented. In this paper, methods of optimization suitable for Sunway TaihuLight are discussed cover the main computations of LOBPCG. Two-level parallel arch...
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In the past a few years, wavelet transforms have become a hot topic of research. Discrete and continuous wavelet transforms have been widely used in signal and multimedia processing. Due to the highperformance and fl...
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Distributed in-memory key-value stores (KVSs), such as Redis and Memcached, are widely deployed in modern data centers. In Redis, the throughput of Murmurhash2 is important to the performance of the whole KVS, and fur...
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To meet the memory capacity requirement of big-data applications, a promising architecture is that NVM (Non-Volatile Memory) is used as main memory and external DRAM (off-chip) is used as cache. Compared to LLC versus...
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