A08: Virtualized Big Data: Reproducing Simulation Output
on Demand
Author
Event Type
ACM Student Research Competition
Poster
TimeWednesday, November 15th3:30pm -
3:40pm
Location701
Description
Scientific simulations are being pushed to the extreme
in terms of size and complexity of the addressed
problems, producing astonishing amount of data. If the
data is stored on disk, analysis applications can
randomly access simulation output. Yet, storing the
massive amounts simulation data is challenging. This is
primarily due to the high storage costs and the fact
that compute capabilities grow faster than storage
capacities and bandwidths. In-situ analysis removes the
storage costs but applications lose random access.
We propose to not store the full simulation output data but to produce it on demand. Our system intercepts I/O requests of both analysis tools and simulators, enabling data virtualization. This new paradigm allows us to explore the computation-storage tradeoff, by trading computation power for storage space. Overall, SDaVi offers a viable path towards exa-scale scientific simulations, by exploiting the growing computing power and relaxing the storage capacity requirements.
We propose to not store the full simulation output data but to produce it on demand. Our system intercepts I/O requests of both analysis tools and simulators, enabling data virtualization. This new paradigm allows us to explore the computation-storage tradeoff, by trading computation power for storage space. Overall, SDaVi offers a viable path towards exa-scale scientific simulations, by exploiting the growing computing power and relaxing the storage capacity requirements.
Author
