porting work to Maxeler DFE

Cédric Augonnet 1773849014 Properly handle the case of write-only buffers when prefetching (in this case, лет назад: 16
build-aux f33fc377f9 Get rid of the starpu-async branch where all recent changes have been made. лет назад: 16
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examples 7c31645307 The examples should not assume that StarPU was installed, we directly use the лет назад: 16
include e3ca8477e7 C++ (and therefore nvcc) requires that we don't use anonymous enum types. лет назад: 16
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src 1773849014 Properly handle the case of write-only buffers when prefetching (in this case, лет назад: 16
tests dc04c50247 Make the test more ""challenging"" (test the case where only a subset of the лет назад: 16
tools f33fc377f9 Get rid of the starpu-async branch where all recent changes have been made. лет назад: 16
AUTHORS fc22dad676 create a trunk/, branches/ and a tags/ directory лет назад: 16
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Makefile.am ec8baec6b1 Do not reimplement spinlocks, use pthread_spin_t instead. лет назад: 16
README afbeddc8b3 proofread лет назад: 16
acinclude.m4 f33fc377f9 Get rid of the starpu-async branch where all recent changes have been made. лет назад: 16
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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

$ 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).