Abstract
Processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We are developing a compiler which processes data intensive applications written in a dialect of Java and compiles them for efficient execution on cluster of workstations or distributed memory machines.
In this paper, we focus on data intensive applications with two important properties: 1) data elements have spatial coordinates associated with them and the distribution of the data is not regular with respect to these coordinates, and 2) the application processes only a subset of the available data on the basis of spatial coordinates. These applications arise in many domains like satellite data-processing and medical imaging. We present a general compilation and execution strategy for this class of applications which achieves high locality in disk accesses. We then present a technique for hoisting conditionals which further improves efficiency in execution of such compiled codes.
Our preliminary experimental results showtha t the performance from our proposed execution strategy is nearly two orders of magnitude better than a naive strategy. Further, up to 30% improvement in performance is observed by applying the technique for hoisting conditionals.
This work was supported by NSF grant ACR-9982087, NSF CAREER award ACI- 9733520, and NSF grant CCR-9808522.
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Ferreira, R., Agrawal, G., Jin, R., Saltz, J. (2001). Compiling Data Intensive Applications with Spatial Coordinates. In: Midkiff, S.P., et al. Languages and Compilers for Parallel Computing. LCPC 2000. Lecture Notes in Computer Science, vol 2017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45574-4_22
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