Toby C. Rodman, Betty J. Flehinger, et al.
Human Genetics
Background: The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications. Results: We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478× speedup. Conclusion: Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets. © 2007 Rao et al; licensee BioMed Central Ltd.
Toby C. Rodman, Betty J. Flehinger, et al.
Human Genetics
Dimitrios Christofidellis, Giorgio Giannone, et al.
MRS Spring Meeting 2023
Marianna Rapsomaniki, Jannis Born, et al.
AMLD EPFL 2024
S.B. Broyde, S.S. Brody
Biochemical and Biophysical Research Communications