User: eschnett Date: 2012/05/28 01:41 PM

Added: /about/releases/ opencl.html

Log: Describe CUDA/OpenCL in the release

File Changes:

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

File [added]: opencl.html Delta lines: +45 -0 =================================================================== --- about/releases/opencl.html (rev 0) +++ about/releases/opencl.html 2012-05-28 18:41:18 UTC (rev 856) @@ -0,0 +1,45 @@ +<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>