[Commits] [svn:einsteintoolkit] www/about/releases/ (Rev. 858)

[schnetter at cct.lsu.edu]

User: eschnett
Date: 2012/05/28 02:02 PM

Removed:
 /about/releases/
  opencl.html

Log:
 Remove notes again

File Changes:

Directory: /about/releases/
===========================

File [removed]: opencl.html
Delta lines: +0 -45
===================================================================
--- about/releases/opencl.html	2012-05-28 18:56:36 UTC (rev 857)
+++ about/releases/opencl.html	2012-05-28 19:02:16 UTC (rev 858)
@@ -1,45 +0,0 @@
-<h3>Accelerator Support</h3>
-
-<p>This release of the Einstein Toolkit adds support for GPUs and
-  other accelerators. This support comprises three levels of
-  abstraction, ranging from merely building and running both CUDA and
-  OpenCL code, to automated code generation targetting GPUs instead of
-  CPUs. As with any other programming paradigm (such as MPI or
-  OpenMP), the performance benefits depend on the particular
-  algorithms used and optimisations that are applied. In addition, the
-  Simulation Factory greatly aids portability to a wide range of
-  computing systems.</p>
-  
-  <!-- This additional text only for the details release notes -->
-  
-<p>At the lowest level, Cactus now supports compiling, building, and
-  running with either CUDA or OpenCL. CUDA is supported as new
-  language in addition to C, C++, and Fortran; OpenCL is supported as
-  an external library, and builds and executes compute kernels via
-  run-time calls. Details are described in the user's guide (for CUDA)
-  and in thorn <tt>ExternalLibraries/OpenCL</tt> (for OpenCL).</p>
-
-<p>Many accelerator platforms today separate between host memory and
-  device memory, and require explicit copy or map operations to
-  transfer data. An intermediate level of abstraction aids
-  transferring grid variables between host and device, using schedule
-  declarations to keep track of which data are needed where, and
-  minimising expensive data transfers. For OpenCL, there is a compact
-  API to build and execute compute kernels at run time. Details are
-  described in thorns <tt>CactusUtils/Accelerator</tt>
-  and <tt>CactusUtils/OpenCLRunTime</tt> (with example parameter
-  file).</p>
-
-<p>Finally, the code generation
-  system <a href="http://kranccode.org/"><i>Kranc</i></a> has been
-  extended to be able to produce either C++ or OpenCL code, based on
-  the infrastructure described above. This allows writing GPU code in
-  a very high-level manner. However, it needs to be stated that the
-  efficiency of the generated code depends on many variables,
-  including e.g. the finite differencing stencil radius and the number
-  of operations in the generated compute kernels. Non-trivial kernels
-  typically require system-dependent tuning to execute efficiently, as
-  GPUs and other accelerators generally show a rather unforgiving
-  performance behaviour. The thorns <tt>McLachlan/ML_WaveToy</tt> and
-  <tt>McLachlan/ML_WaveToy_CL</tt> are examples, generated from the
-  same Kranc script, showing the generated C++ and OpenCL code.</p>