The Star-P open software platform delivers revolutionary results to scientists, engineers and analysts by enabling them to transparently use high performance computing resources, using familiar desktop tools.

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Star-P software is a client-server parallel-computing platform that’s been designed to work with multiple Very High Level Language (VHLL) client applications such as MATLAB®, or Python and has built-in tools to expand VHLL computing capability through addition of libraries and hardware-based accelerators.

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• Explore Star-P Architecture (click here)...
  • Familiar Desktop Tools
  • Star-P Client
  • Star-P Interactive Engine
  • Star-P Computation Engine
  • Data-Parallel Computations
  • Task-Parallel Computations
  • Star-P Connect Library API Link
  • Hardware Accelerators
  • Development Utilities
  • High Speed I/O
  • Server Configurations

Star-P Client
The Star-P client connects the desktop application to the parallel server, and outsources to it the most computationally-intensive operations. It resides on the user’s desktop, intercepts calls to VHLL function libraries, and forwards them to the parallel libraries on the server if parallel computing is needed. Star-P Client also handles all Star-P specific commands.

Star-P Interactive Engine
Star-P’s interactive engine runs on top of the server operating system, and manages the multiple interactive sessions in a multi-user environment, giving client applications interactive access to the server’s processors, memory, and file system.

Star-P Computation Engine
Star-P’s computation engine consists of two key elements: Built-in parallel computing and add-on parallel computing, accessible via Star-P Connect. Both support data- and task- parallel computations.

Data-Parallel Computations are for high-level matrix and vector operations on large data sets. and involve inter-processor communication during the computation.

Task-Parallel Computations are for carrying out many independent calculations in parallel, such as Monte Carlo simulations, or "un-rolling" serial FOR loops. Task-parallel computations do not involve inter-processor communication during the computation.

Star-P Connect Library API Link enables extensions of the Star-P built-in computing functionality, based on your particular application and algorithm requirements, as described in the following section.

Star-P Connect Library API Link
Star-P Connect library API link enables you to extend the functionality of the Star-P compute engine based on your particular application and algorithm requirements. You can plug in existing serial and parallel libraries, access them via the desktop tools such as MATLAB® and Python, and execute them in a task- and data-parallel modes.

Hardware Accelerators
Hardware accelerators such as FPGAs and GPUs give technical computing users significant computation, I/O and memory bandwidth advantages over traditional CPU-only solutions. Through the Star-P Connect library API link, compute-intensive algorithms embedded in hardware appear as standard library functions, and can be easily called from the high-level desktop application.