ACM Computing Surveys (CSUR)
The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Smooth view-dependent level-of-detail control and its application to terrain rendering
Proceedings of the conference on Visualization '98
PVG '01 Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Out-of-core construction and visualization of multiresolution surfaces
I3D '03 Proceedings of the 2003 symposium on Interactive 3D graphics
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Fast Lossless Compression of Scientific Floating-Point Data
DCC '06 Proceedings of the Data Compression Conference
Fast and Efficient Compression of Floating-Point Data
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Computers
Methods for querying compressed wavefields
Methods for querying compressed wavefields
| Hi-index | 0.00 |
State-of-the-art numerical solvers in Earth Sciences produce multi terabyte datasets per execution. Operating on increasingly larger datasets becomes challenging due to insufficient data bandwidth. Queries result in difficult to handle I/O access patterns. BEMC is a new mechanism that allows querying and processing wavefields in the compressed representation. This approach combines well-known spatial-indexing techniques with novel compressed representations, thus reducing I/O bandwidth requirements. A new compression approach based on boundary integral representations exploits properties of the simulated domain. Frequency domain representation further compresses the data by eliminating temporal redundancy found in wave propagation data. This representation enables the transformation of a large I/O workload into a massively-parallel CPU-intensive computation. Queries to this representation result in largely sequential I/O accesses. Although, decompression places heavy demands on the CPU, it exhibits parallelism well-suited for many-core processors. We evaluate our approach in the context of data analysis for the Earth Sciences datasets.