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- 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->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 WORKERS_GPUID and WORKERS_CPUID env. variables make it possible to
- specify where to bind the workers
- * Use the hwloc library to detect the actual number of cores
- StarPU 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 supplied
- StarPU 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 support
- Changes:
- * 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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