PGI Release 2010 Enables Scientists and Engineers to Harness the Computational Power of GPUs

Latest compilers from The Portland Group extend support for the PGI Accelerator
Programming Model and PGI CUDA Fortran


The Portland Group, a wholly-owned subsidiary of STMicroelectronics, today
announced that release 2010 of the PGI line of high-performance parallelizing
compilers and development tools for Linux, Mac OS X and Windows will be
available on Tuesday, November 17. PGI 2010 is the first general release to
include full support for the PGI Accelerator Programming model v1.0 standard on
x64 processor-based systems incorporating NVIDIA CUDA-enabled Graphical
Processing Units (GPUs). In addition to supporting high-level programming of
accelerators using the PGI Accelerator programming model, the PGI Release 2010
also includes PGI CUDA Fortran, an explicit GPU programming model and
application programming interface (API) that gives expert programmers direct
control of all aspects of programming NVIDIA GPUs.

The PGI Accelerator programming model is a collection of compiler directives
used to specify regions of code in Fortran and C programs that can be offloaded
from a host CPU to an attached accelerator to enhance performance. Applications
optimized using the PGI Accelerator directives remain 100% portable to other
compilers and platforms, and execute on systems with or without a GPU

PGI 2010 offers full support for the PGI Accelerator programming model
including the following new features:

  • GPU device-resident data – the ability to define and leave data on the GPU
    across accelerator regions and subroutine boundaries
  • Support for COMPLEX and DOUBLE COMPLEX data types in Fortran
  • Support for C structs and Fortran derived types
  • Automatic GPU-side loop unrolling for improved performance
  • Support for Accelerator regions nested within OpenMP parallel regions
  • Support for Linux, Mac OS X (including Snow Leopard) and Windows (including
    Windows 7).

"Within five years, most HPC systems will include both x86 CPUs and
accelerators in some form," said Douglas Miles, director, The Portland Group.
"The PGI 2010 compilers will play a role in establishing accelerated computing
as the mainstream HPC architecture."

PGI CUDA Fortran includes a Fortran 95/03 compiler and tool chain for native
programming of NVIDIA GPUs using Fortran. CUDA Fortran subroutines can launch
and execute in parallel on the hundreds of cores in an NVIDIA GPU under control
of an x64 host CPU. Developed in collaboration with NVIDIA, PGI CUDA Fortran
extensions supported in the PGI 2010 Fortran 95/03 compiler enable HPC
developers to explicitly control all aspects of data movement, memory
utilization and computation on CUDA GPUs.

Additional new features in the PGI 2010 compilers and tools include support
for more Fortran 2003 incremental features, the latest EDG 4.1 C++ front-end
with enhanced GNU and Microsoft compatibility, OpenMP parallel programming
support for up to 256 cores, and AVX code generation. PGI 2010 also includes a
major update to the PGPROF performance profiler, which now supports performance
profiling of binary executables without re-compiling or any special software
privileges, uniform operation and features on Linux, Mac OS X and Windows,
support for PGI Accelerator and PGI CUDA Fortran GPU-side performance
statistics, and an updated graphical user interface. Finally, PGI 2010 supports
the latest operating system releases including Red Hat Fedora 10/11, SuSE 11.1
and Ubuntu 9, Mac OS X Snow Leopard and Windows 7.

Enhancements in the PGI 2010 release of PGI Visual Fortran for Microsoft
Visual Studio include full support for the PGI Accelerator Programming model and
PGI CUDA Fortran on NVIDIA CUDA-enabled GPUs, and the addition of a new
standalone version of the PGPROF performance profiler for x64 and GPUs with
support for the Common Compiler Feedback Format (CCFF). CCFF is a draft standard
published by PGI that defines what compiler information is stored and how the
information is formatted. CCFF enables HPC tools providers to offer more and
better information about optimizing performance.


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