ChangeLog 4.0 KB

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  1. StarPU 0.5 (svn revision ????)
  2. ==============================================
  3. The yet-more-stuff release
  4. * Provide the STARPU_REDUX data access mode
  5. * Externalize the scheduler API.
  6. * Add theoretical bound computation
  7. * Add the void interface
  8. * Add power consumption optimization
  9. * Add parallel task support
  10. * Add starpu_mpi_insert_task
  11. * Add profiling information interface.
  12. * Add STARPU_LIMIT_GPU_MEM environment variable.
  13. * OpenCL fixes
  14. * MPI fixes
  15. * Improve optimization documentation
  16. * Upgrade to hwloc 1.1 interface
  17. * Add fortran example
  18. * Add mandelbrot OpenCL example
  19. * Add cg example
  20. * Add stencil MPI example
  21. StarPU 0.4 (svn revision 2535)
  22. ==============================================
  23. The API strengthening release
  24. * Major API improvements
  25. - Provide the STARPU_SCRATCH data access mode
  26. - Rework data filter interface
  27. - Rework data interface structure
  28. - A script that automatically renames old functions to accomodate with the new
  29. API is available from https://scm.gforge.inria.fr/svn/starpu/scripts/renaming
  30. (login: anonsvn, password: anonsvn)
  31. * Implement dependencies between task directly (eg. without tags)
  32. * Implicit data-driven task dependencies simplifies the design of
  33. data-parallel algorithms
  34. * Add dynamic profiling capabilities
  35. - Provide per-task feedback
  36. - Provide per-worker feedback
  37. - Provide feedback about memory transfers
  38. * Provide a library to help accelerating MPI applications
  39. * Improve data transfers overhead prediction
  40. - Transparently benchmark buses to generate performance models
  41. - Bind accelerator-controlling threads with respect to NUMA locality
  42. * Improve StarPU's portability
  43. - Add OpenCL support
  44. - Add support for Windows
  45. StarPU 0.2.901 aka 0.3-rc1 (svn revision 1236)
  46. ==============================================
  47. The asynchronous heterogeneous multi-accelerator release
  48. * Many API changes and code cleanups
  49. - Implement starpu_worker_get_id
  50. - Implement starpu_worker_get_name
  51. - Implement starpu_worker_get_type
  52. - Implement starpu_worker_get_count
  53. - Implement starpu_display_codelet_stats
  54. - Implement starpu_data_prefetch_on_node
  55. - Expose the starpu_data_set_wb_mask function
  56. * Support nvidia (heterogeneous) multi-GPU
  57. * Add the data request mechanism
  58. - All data transfers use data requests now
  59. - Implement asynchronous data transfers
  60. - Implement prefetch mechanism
  61. - Chain data requests to support GPU->RAM->GPU transfers
  62. * Make it possible to bypass the scheduler and to assign a task to a specific
  63. worker
  64. * Support restartable tasks to reinstanciate dependencies task graphs
  65. * Improve performance prediction
  66. - Model data transfer overhead
  67. - One model is created for each accelerator
  68. * Support for CUDA's driver API is deprecated
  69. * The STARPU_WORKERS_CUDAID and STARPU_WORKERS_CPUID env. variables make it possible to
  70. specify where to bind the workers
  71. * Use the hwloc library to detect the actual number of cores
  72. StarPU 0.2.0 (svn revision 1013)
  73. ==============================================
  74. The Stabilizing-the-Basics release
  75. * Various API cleanups
  76. * Mac OS X is supported now
  77. * Add dynamic code loading facilities onto Cell's SPUs
  78. * Improve performance analysis/feedback tools
  79. * Application can interact with StarPU tasks
  80. - The application may access/modify data managed by the DSM
  81. - The application may wait for the termination of a (set of) task(s)
  82. * An initial documentation is added
  83. * More examples are supplied
  84. StarPU 0.1.0 (svn revision 794)
  85. ==============================================
  86. First release.
  87. Status:
  88. * Only supports Linux platforms yet
  89. * Supported architectures
  90. - multicore CPUs
  91. - NVIDIA GPUs (with CUDA 2.x)
  92. - experimental Cell/BE support
  93. Changes:
  94. * Scheduling facilities
  95. - run-time selection of the scheduling policy
  96. - basic auto-tuning facilities
  97. * Software-based DSM
  98. - transparent data coherency management
  99. - High-level expressive interface