porting work to Maxeler DFE

Cédric Augonnet c26471c512 The wrapper to allocate pinned memory allocates a generic buffer, not always a il y a 16 ans
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src c26471c512 The wrapper to allocate pinned memory allocates a generic buffer, not always a il y a 16 ans
tests c1f00e8c91 update the StarPU Gordon driver to the recent changes in Gordon il y a 16 ans
tools 5a708531e7 add some legend to the perfmodel debug output il y a 16 ans
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acinclude.m4 446f18f835 test if __sync_* GCC built-in are available il y a 16 ans
configure.ac c1f00e8c91 update the StarPU Gordon driver to the recent changes in Gordon il y a 16 ans
libstarpu.pc.in fc22dad676 create a trunk/, branches/ and a tags/ directory il y a 16 ans

README

++=================++
|| I. Introduction ||
++=================++

+----------------------
| I.a. What is StarPU ?

StarPU is a runtime system that offers support for heterogeneous multicore
machines. While many efforts are devoted to design efficient computation kernels
for those architectures (eg. to implement BLAS kernels on GPUs or on Cell's
SPUs), StarPU not only takes care of offloading such kernels (and implementing
data coherency accross the machine), but it also makes sure the kernels are
executed as efficiently as possible.

+------------------------
| I.b. What StarPU is not

StarPU is not a new langage, and it does not extends existing langages either.
StarPU does not help to write computation kernels.

+---------------------------------
| I.c. (How) Could StarPU help me?

While StarPU will not make it easier to write computation kernels, it does
simplify their actual offloading as StarPU handle most low level aspects
transparently.

Obviously, it is crucial to have efficient kernels, but it must be noted that
the way those kernels are mapped and scheduled onto the computational resources
also affect the overall performance to a great extent.

StarPU is especially helpful when considering multiple heterogeneous processing
resources: statically mapping and synchronizing tasks in such a heterogeneous
environment is already very difficult, making it in a portable way is virtually
impossible. On the other hand, the scheduling capabilities of StarPU makes it
possible to easily exploit all processors at the same time while taking
advantage of their specificities in a portable fashion.

++==================++
|| II. Requirements ||
++==================++

* make
* gcc (version >= 4.1)
* if CUDA support is enabled
* CUDA (version >= 2.0)
* CUBLAS (version >= 2.0)
* extra requirements for the svn version
* autoconf (version >= 2.60)
* automake

++=====================++
|| III. Getting StarPU ||
++=====================++

StarPU is available on https://gforge.inria.fr/projects/starpu/.

It is also possible to access the latest svn version:
$ svn checkout svn://scm.gforge.inria.fr/svn/starpu/trunk/
or via http (DAV):
$ svn checkout https://scm.gforge.inria.fr/svn/starpu/trunk/

++=============================++
|| IV. Building and Installing ||
++=============================++

+---------------------------
| IV.a. For svn version only

$ autoreconf

+------------------------
| IV.b. For all versions:

$ ./configure
$ make
$ make install

++============++
|| V. Contact ||
++============++

For any questions regarding StarPU, please contact Cédric Augonnet
(cedric.augonnet@inria.fr).