porting work to Maxeler DFE

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mpi 8b213605f2 finish to prefix GET_BLAS_* helpers преди 15 години
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tools c722a34e6e more prefixing with starpu_ and s/CORE/CPU/g преди 15 години
AUTHORS 65931ca292 Welcome to our recent contributors :) преди 15 години
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Makefile.am 89c1870f00 Define a new header which should contain the public functions that should only преди 15 години
README 6444979fdf create autogen.sh to call autoreconf with the proper parameters преди 15 години
acinclude.m4 f33fc377f9 Get rid of the starpu-async branch where all recent changes have been made. преди 15 години
autogen.sh 6444979fdf create autogen.sh to call autoreconf with the proper parameters преди 15 години
configure.ac 2ef2a414ce use clock_gettime just when it is available преди 15 години
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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

$ ./autogen.sh

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

$ ./configure
$ make
$ make install

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

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