| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364 | StarPU 0.2.901 aka 0.3-rc1 (svn revision 1236)==============================================The asynchronous heterogeneous multi-accelerator release  * Many API changes and code cleanups    - Implement starpu_get_worker_id    - Implement starpu_get_worker_name    - Implement starpu_get_worker_type    - Implement starpu_get_worker_count    - Implement starpu_display_codelet_stats    - Implement starpu_prefetch_data_on_node    - Expose the starpu_data_set_wb_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->STARPU_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
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