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Cédric Augonnet b2a72aa358 implement the ring example with asynchronous calls hace 15 años
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include 51fe1b1897 Various cleanups in the data management code, start to outline an API to hace 15 años
mpi b2a72aa358 implement the ring example with asynchronous calls hace 15 años
scripts fc22dad676 create a trunk/, branches/ and a tags/ directory hace 16 años
src 42379b35dc bug fix: the number of children was not set properly in the existing hace 15 años
tests c94a373e97 Fix the starpu_sync_data_with_mem API, it is now required to call hace 15 años
tools 480ee416d5 - Cleanup the code managing performance models and per model "debug" files. We hace 15 años
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Makefile.am c3c1b0de02 Introduce the MPI helper lib (enabled with --with-mpi at configure time). hace 15 años
README afbeddc8b3 proofread hace 16 años
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configure.ac c3c1b0de02 Introduce the MPI helper lib (enabled with --with-mpi at configure time). hace 15 años
libstarpu.pc.in fc22dad676 create a trunk/, branches/ and a tags/ directory hace 16 años

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).