Data-intensive scientific applications require rapid access to local and geographically distributed data, however, there are significant I/O latency bottlenecks associated with storage systems and widearea networking. LambdaRAM is a high-performance, multi-dimensional, distributed cache, that takes advantage of memory from multiple clusters interconnected by ultra-high-speed networking, to provide applications with rapid access to both local and remote data. It mitigates latency bottlenecks by employing proactive latency-mitigation heuristics based on an application’s access patterns. We present results using LambdaRAM to rapidly stride through remote multi-dimensional NASA Modeling, Analysis and Prediction (MAP) 2006 project datasets, based on time and geographical coordinates, to compute wind shear for cyclone and hurricane and tropical cyclone analysis. Our current experiments have demonstrated up to a 20-fold speedup in the computation of wind shear with LambdaRAM.

Keywords: Data-intensive computing, Multi-dimensional remote data striding, Climate modeling and analysis, Distributed data caches, LambdaGrids, Tropical cyclone analysis, Hurricane analysis

© 2008 Elsevier B.V. All rights reserved.

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Vishwanath, V., Burns, R., Leigh, J., Seablom, M., Accelerating tropical cyclone analysis using LambdaRAM, a distributed data cache over wide-area ultra-fast networks, Future Generation of Computer Science, The International Journal of Grid Computing: Theory, Methods and Applications, Elsevier Science B.V., September 15th, 2008.