Industry Solutions|Parallel Application Development for MATLAB, Python and R Users

Industry Solutions: Life Sciences

Computing with Star-P is applicable in multiple areas of research and product development in life sciences. Solutions based on Star-P relevant to the life sciences industry problems include:

Application Areas

Image processing
Genomic correlation
Drug discovery

Parallel Code for Dummies
from Genome Technology >

Case Studies

Videos & Demos

	Image Processing of MRI Brain Scans > 9x Gain: 5 Minutes vs 45 Minutes
	Genomic Correlation at the National Cancer Institute > 200x Speed-Up on 8-P Server
	Image Segmentation with the Insight Toolkit (ITK) > 3x Gain on 8-P Server
	Finite Element Analysis > 10 - 100x Gain on 8-P Server

Brain Scan Analysis >

MRI analysis of brain scans
Parameter fitting of perfusion data
Simple implementation of task-parallel computations

SDK Demonstration >

Plug in existing C functions, libraries
Monte-Carlo integration example
Leverages existing MATLAB® code
Field II untrasound imaging example

Financial Services >
Defense & Intelligence >
Semiconductor >
University & Academia >

Reprint:
"In Profile: ISC"
from Scientific Computing World (PDF) >

Whitepaper:
Life Sciences

Delivering Interactive Parallel Computing Power to the Desktop

As simulation replaces physical testing, and increasingly complex phenomena are modeled, high performance computers are growing in importance for science and engineering. Interactive desktop science and engineering tools - such as MATLAB®, Python, R, and others - are critical in new model and algorithm development. The current HPC workflow is fundamentally flawed, due to its inability to support an interactive discovery process, but a new programming model is showing promise in enabling desktop tools to operate interactively on servers, clusters, and grids.

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