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Star-P at SDSC
— Ilya Mirman, Interactive Supercomputing
San Diego Supercomputer Center has recently installed Star-P software with goal of delivering revolutionary results to scientists, engineers, and analysts. Star-P enables them to transparently use high performance computing resources using familiar desktop tools.
Star-P software is a bridge between popular computing tools and the grids, servers, and clusters used widely in technical computing. With Star-P, you can use your favorite desktop simulation tool, with its familiar features, commands, and data types. Standard commands and functions are available and transparently perform in a parallel manner. Existing scripts can be reused to run larger problems in parallel with minimal modification. This substantially reduces the learning curve and dramatically accelerates the development of custom parallel applications.
Bringing Together Key Computing Modes
Data parallel computations are for high-level matrix and vector operations on large data sets. They involve inter-processor communication during the computation. The Star-P data-parallel libraries are high-performance optimized libraries that can be called by the client application to perform compute-intensive operations on large distributed data sets.
Task parallelism is a powerful method to carry out many independent ("embarrassingly parallel") calculations in parallel, such as Monte Carlo simulations, or "un-rolling" serial FOR loops. For example, in a medical application involving image processing on multiple brain slices, Star-P can distribute images across several processors, and simultaneously process them. A measure of parallel abstraction is that a program should execute independently of the number of processors it has access to. With Star-P, there is no need to worry about the number of available processors — Star-P takes care of distributing the data and executing the computations.
Although there are no hard and fast rules about when to use each computing mode, the following chart offers a rough guideline.
Code Profiling & Optimization
In this example of one of the built-in utilities, we see a breakdown of the total wall clock time for a particular code — starting out with operation mostly on the client, then on the server, and then the network. And on the bottom we see the cumulative picture evolve over time.
Overall, the key benefits of Star-P are increased productivity: the ability to run bigger simulations, faster, and with less parallel programming effort. The following provide some application examples from the field.
10-100X Faster Computations
10-100X Larger Data Sets
No Need for C/Fortran/MPI Re-Programming
For More Information:
For questions regarding obtaining an account or using Star-P in the new SDSC Cluster, please contact Dongju Choi at SDSC's scientific computing group.