| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161 | # StarPU --- Runtime system for heterogeneous multicore architectures.## Copyright (C) 2009, 2010, 2011  Université de Bordeaux 1# Copyright (C) 2010, 2011  Centre National de la Recherche Scientifique## StarPU is free software; you can redistribute it and/or modify# it under the terms of the GNU Lesser General Public License as published by# the Free Software Foundation; either version 2.1 of the License, or (at# your option) any later version.## StarPU is distributed in the hope that it will be useful, but# WITHOUT ANY WARRANTY; without even the implied warranty of# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.## See the GNU Lesser General Public License in COPYING.LGPL for more details.StarPU 1.0 (svn revision xxxx)==============================================The extensions-again release  * Fields xxx_func of struct starpu_codelet are made deprecated. One	should use instead fields xxx_funcs.  * Applications can provide several implementations of a codelet for    the same architecture.  * A new multi-format interface permits to use different binary    formats on CPUs & GPUs, the conversion functions being provided by    the application and called by StarPU as needed (and as less as    possible).  * Add a gcc plugin to extend the C interface with pragmas which    allow to easily define codelets and issue tasks.  * Add codelet execution time statistics plot.  * Add bus speed in starpu_machine_display.  * Add a StarPU-Top feedback and steering interface.  * Documentation improvement.  * Add a STARPU_DATA_ACQUIRE_CB which permits to inline the code to    be done.  * Permit to specify MPI tags for more efficient starpu_mpi_insert_task  * Add SOCL, an OpenCL interface on top of StarPU.  * Add gdb functions.  * Add complex support to LU example.  * Add an OpenMP fork-join example.  * Permit to use the same data several times in write mode in the parameters of    the same task.  * Some types were renamed for consistency. The tools/dev/rename.sh    script can be used to port code using former names. You can also    choose to include starpu_deprecated_api.h (after starpu.h) to keep    using the old types.StarPU 0.9 (svn revision 3721)==============================================The extensions release  * Provide the STARPU_REDUX data access mode  * Externalize the scheduler API.  * Add theoretical bound computation  * Add the void interface  * Add power consumption optimization  * Add parallel task support  * Add starpu_mpi_insert_task  * Add profiling information interface.  * Add STARPU_LIMIT_GPU_MEM environment variable.  * OpenCL fixes  * MPI fixes  * Improve optimization documentation  * Upgrade to hwloc 1.1 interface  * Add fortran example  * Add mandelbrot OpenCL example  * Add cg example  * Add stencil MPI example  * Initial support for CUDA4StarPU 0.4 (svn revision 2535)==============================================The API strengthening release  * Major API improvements    - Provide the STARPU_SCRATCH data access mode    - Rework data filter interface    - Rework data interface structure    - A script that automatically renames old functions to accomodate with the new      API is available from https://scm.gforge.inria.fr/svn/starpu/scripts/renaming      (login: anonsvn, password: anonsvn)  * Implement dependencies between task directly (eg. without tags)  * Implicit data-driven task dependencies simplifies the design of    data-parallel algorithms  * Add dynamic profiling capabilities    - Provide per-task feedback    - Provide per-worker feedback    - Provide feedback about memory transfers  * Provide a library to help accelerating MPI applications  * Improve data transfers overhead prediction    - Transparently benchmark buses to generate performance models    - Bind accelerator-controlling threads with respect to NUMA locality  * Improve StarPU's portability    - Add OpenCL support    - Add support for WindowsStarPU 0.2.901 aka 0.3-rc1 (svn revision 1236)==============================================The asynchronous heterogeneous multi-accelerator release  * Many API changes and code cleanups    - Implement starpu_worker_get_id    - Implement starpu_worker_get_name    - Implement starpu_worker_get_type    - Implement starpu_worker_get_count    - Implement starpu_display_codelet_stats    - Implement starpu_data_prefetch_on_node    - Expose the starpu_data_set_wt_mask function  * Support nvidia (heterogeneous) multi-GPU  * Add the data request mechanism    - All data transfers use data requests now    - Implement asynchronous data transfers    - Implement prefetch mechanism    - Chain data requests to support GPU->RAM->GPU transfers  * Make it possible to bypass the scheduler and to assign a task to a specific    worker  * Support restartable tasks to reinstanciate dependencies task graphs  * Improve performance prediction    - Model data transfer overhead    - One model is created for each accelerator  * Support for CUDA's driver API is deprecated  * The STARPU_WORKERS_CUDAID and STARPU_WORKERS_CPUID env. variables make it possible to    specify where to bind the workers  * Use the hwloc library to detect the actual number of coresStarPU 0.2.0 (svn revision 1013)==============================================The Stabilizing-the-Basics release  * Various API cleanups  * Mac OS X is supported now  * Add dynamic code loading facilities onto Cell's SPUs  * Improve performance analysis/feedback tools  * Application can interact with StarPU tasks    - The application may access/modify data managed by the DSM    - The application may wait for the termination of a (set of) task(s)  * An initial documentation is added  * More examples are suppliedStarPU 0.1.0 (svn revision 794)==============================================First release.Status: * Only supports Linux platforms yet * Supported architectures   - multicore CPUs   - NVIDIA GPUs (with CUDA 2.x)   - experimental Cell/BE supportChanges: * Scheduling facilities   - run-time selection of the scheduling policy   - basic auto-tuning facilities * Software-based DSM   - transparent data coherency management   - High-level expressive interface
 |