mpiDL

Leverage MPI and Parallel Cluster Computing Experience in IDL

Learn more about mpiDL:

• How Does mpiDL Work?
• Case Study: General Atomics Uses mpiDL for Large U.S. Fusion Experiment
• Case Study: Using mpiDL for Climate Predication and Image Processing

How Does mpiDL Work?
The MPI library of communication routines is the standard message-passing interface for distributed-memory parallel computing. Tech-X Corporation's mpiDL is an add-on library that implements MPI as native IDL function calls, helping scientists and developers familiar with parallel computing quickly leverage the power of IDL.

With mpiDL, parallel programmers can write IDL programs that call MPI functions using the same approach they would use when writing C or Fortran programs. mpiDL also gives developers access to built-in specialized parallel functionality based on collections of primitive MPI communication and data types. Developers who are new to parallel programming using explicit message passing can get up to speed quickly by modifying the mpiDL examples to create their own parallel IDL programs.

Case Study: General Atomics Uses mpiDL for Large U.S. Fusion Experiment
General Atomics, together with the Department of Energy, is conducting one of the largest U.S. fusion energy experiments. This long-running experiment, called DIII-D, requires a calculation of many aggregate quantities such as temperature and density profiles for each test shot.

By combining the convenience of interactive visualization and analysis of IDL with the power of parallel computing, mpiDL helped generate rapid analysis of experimental data during the few minutes between shots of the DIII-D plasma fusion device. Speed-up was a factor of 8X on a 20-node cluster, providing General Atomics researchers with essential real-time feedback.

General Atomics researchers saw an 8X speed-up on a 20-node Linux cluster using mpiDL.

Case Study: Using mpiDL for Climate Prediction and Image Processing
Michael White, a research scientist at Utah State University, and Mark Schwartz from the University of Wisconsin-Milwaukee, are using mpiDL to accelerate data analysis for their environmental research. Their model predicts the year-day of first bloom and first leaf for a given year based on weather patterns, and makes predictions on the scale of 1 km² for the entire continental United States. By deploying their model analysis code on a multi-node Linux cluster, White and Schwartz reused their existing IDL scripts and decreased processing times by a factor of 25X.

The map shows predicted day of year first leaf for 1981. Results produced by Michael White and Mark Schwartz on 10-node Linux Cluster using IDL parallelized with mpiDL.