Success Stories

Star-P® software allows scientists, engineers and analysts to accelerate "time-to-insight" across a broad set of industries and applications using familiar interactive tools such as MATLAB®, Python, and others. The following application areas lead you to case studies and customer stories highlighting the challenges and successes of Star-P customers around the world.

Image Processing of MRI Brain Scans	Learn how Star-P's data parallel mode can be applied to processing of large images, and the task parallel mode can be applied to independent processing of multiple moderately-sized images. 9x Gain: 5 minutes vs 45 minutes
Radar System Design	For Air Force Research Labs, Star-P combines the critical parallel approaches in one environment: task and data parallelism, backend support and compilation. Star-P integrates a wide range of linear algebra and other routines seamlessly with MATLAB® for the user. 50-Second FFTs on 200GB Matrix with 13 Billion Elements
Linear Algebra	With the native MATLAB® linear algebra compatibility, Star-P readily enables simple conversion of existing serial MATLAB® code for parallel computation shaving precious execution and development time. Dramatic Speed-Up on 64 Core Server
Sparse Matrices & Large Graphs	Star-P provides a simple way to express graphs as sparse matrices and provides the infrastructure to manipulate large graphs in the same way as in MATLAB®. Graph algorithm users can use Star-P to explore their graphs, e.g., looking at partitionings or connected components. Enables Analysis of TERAbyte-sized Graphs
Financial Modeling - Monte Carlo Analysis	Analysts can quickly take advantage of the power and speed of task parallel computation to implement Monte Carlo analyses. By maintaining existing Monte Carlo MATLAB® code, quantitative analysts can parallelize their simulation with minimal code modification avoiding the need to recode in C/C++ or FORTRAN with MPI extensions. 6.5x Speed-Up on 8-P Server
Genomic Correlation at National Cancer Institute	National Cancer Institute succeeded in migrating from previously used—but no longer sufficient—serial computation in MATLAB® to parallel computation in the combined Star-P + MATLAB® environment. As a result, the NCI researches obtain their result up to 200 times faster than previously possible. 200x Speed-Up on 8-P Server
Pattern Matching	Star-P enables interactive optimization of evolving pattern matching algorithms, and enables simple conversion of pre-existing serial code for parallel computing, deals effectively with supercomputing-scale data sets, and delivers supercomputing-class performance. 21-Second FFTs on 40-megapixel Images
Singular Value Decomposition (SVD) Data Processing	Scientists and engineers can quickly take advantage of both data and task-parallel SVD computation. Star-P lets users develop and optimize their applications in the familiar MATLAB® environment, while seamlessly computing interactively on a multiprocessor server. 25x Gain on 32 Core, 4 Node Cluster
Computational Ecology at University of California at Santa Barbara	Researchers at the University of California, Santa Barbara (UCSB) are harnessing supercomputers and electronic circuit theory to help save wildlife from ever-shrinking habitats in an emerging scientific field called "computational ecology." Star-P helps decipher threatened wildlife migration. Compute Time Reduces from Days to Minutes
Finite Element Analysis	Star-P's task- and data-parallel modes lend themselves very well to the FEA workflow. The task-parallel mode is ideally-suited to carrying out in parallel the operations that do not depend on each other—such as the creation of the various sparse matrices (e.g., calculating the stiffness matrix). 10-100x Gain on 8-P Server