Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, whi...
Bingjing Zhang, Yang Ruan, Tak-Lon Wu, Judy Qiu, A...
The proliferation of commercial cloud computing providers has generated significant interest in the scientific computing community. Much recent research has attempted to determine...
Scheduling and load-balancing techniques play an integral role in reducing the overall execution time of scientific applications on clustered multi-node systems. The increasing co...
S. P. Muszala, Gita Alaghband, Daniel A. Connors, ...
Scientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of the additional capacity offered by distributed resources and suitab...
Suraj Pandey, William Voorsluys, Mustafizur Rahman...
Scientific applications often involve computation intensive workflows and may generate large amount of derived data. In this paper we consider a life cycle, which starts when the ...
The floating point portion of the SPEC CPU suite and the HPC Challenge suite are widely recognized and utilized as benchmarks that represent scientific application behavior. In th...
—Until recently, most high-end scientific applications have been immune to performance problems caused by NonUniform Memory Access (NUMA). However, current trends in micro-proces...