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PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Automatic compiler-inserted I/O prefetching for out-of-core applications
OSDI '96 Proceedings of the second USENIX symposium on Operating systems design and implementation
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MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Compiler-based I/O prefetching for out-of-core applications
ACM Transactions on Computer Systems (TOCS)
Out-of-Core and Pipeline Techniques for Wavefront Algorithms
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
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ACM SIGGRAPH 2006 Papers
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ACM SIGGRAPH 2007 papers
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IEEE Transactions on Computers
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ACM SIGGRAPH 2008 papers
A framework for efficient and scalable execution of domain-specific templates on GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Transforming the adaptive irregular out-of-core applications for hiding communication and disk I/O
OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part II
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ACM SIGGRAPH 2010 Posters
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IEEE Transactions on Computers
Cheops: a reconfigurable data-flow system for video processing
IEEE Transactions on Circuits and Systems for Video Technology
Fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units
EG PGV'09 Proceedings of the 9th Eurographics conference on Parallel Graphics and Visualization
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Atlas construction is an important technique in medical image analysis that plays a central role in understanding the variability of brain anatomy. The construction often requires applying image processing operations to multiple images (often hundreds of volumetric datasets), which is challenging in computational power as well as memory requirements. In this paper we introduce MIP, a Multi-Image Processing streaming framework to harness the processing power of heterogeneous CPU/GPU systems. In MIP we introduce specially designed streaming algorithms and data structures that provides an optimal solution for out-of-core multi-image processing problems both in terms of memory usage and computational efficiency. MIP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the MIP out-of-core solution could achieve the same performance as the in-core solution. We demonstrate the efficiency of the MIP framework on synthetic and real datasets.