The fifteenth month of work focused on work in the following areas:
- Performance analysis
- Distributed Support
- Release of HPX V1.2.0
- Release of HPXMP V0.1.0
- Released of APEX V2.1.1
- Jupyter Notebook Integration
- Adding data type object (dtype) support
Organizational Activities and Results
- Weekly group-meeting, minutes:
- Phylanx Seminars:
Development Activities and Results
- Algorithms Highlighted: Logistic Regression, ALS, K-Means
- General
- HPX V1.2.0 released
- Fixes for local variables
- Adding Blaze benchmark tests
- APEX
- Released APEX V2.1 in advance of SC18 (See details here)
- Released APEX V2.1.1 point release (See details here)
- APEX/HPX Integration Bugs Fixed:
- OTF2 Trace bug that generated invalid GUIDs (globally unique identifiers) for HPX tasks for some cases
- HPX thread indexing bug that was causing invalid OTF2 traces to be generated
- TAU/APEX integration to ensure robust support for loading TAU symbols with the dynamic library preloading method, using dlopen() and dlsym() calls instead of weak/strong symbols
- Updated nightly regression testing scripts to investigate build/execution failures and to change time when regression tests execute
- Investigated and reported build/test failures from buildbot and nightly regression testing
- Tested, updated, and documented features for APEX release
- Traveler
- Jupyter demo with full Phylanx workflow and Traveler Tree
- Refinements to library for interactive visualization in Jupyter
- Traveler Gantt parent-task attribution changed to most-recent event
- Formative evaluation for Traveler Tree conducted at SC18
- Tiling
- Looking into the theoretical impact of distributed arrays on tiling
- Designed a set of primitives to test tiling model
- Primitives & Algorithms
- HPXMP version 0.1 Released
- Added squeeze primitive which removes single-dimensional entries from the shape of an array
- Added Unique primitive
- Initial version of random forest implementation using NumPy and Python dictionaries
- Added benchmarks to check the performance of Blaze
- Python
- Added support for parallel_block
- Added support for more NumPy primitives which return filled arrays (full, full_like, etc.)
- Enabled defining dtypes for supported NumPy methods
- Implemented the infrastructure for extracting the type and size of variables
- Enhanced HPX initialization process in the frontend
- Improved the handling of illegal returns at the transformation stage
Repository Activity November 1st – November 30th:
Code statistics ------------------------------------------------------------------------------- Language files blank comment code ------------------------------------------------------------------------------- C++ 297 11319 3825 54847 C/C++ Header 211 4178 2606 18944 Python 83 1461 1125 3647 CMake 92 801 672 3257 YAML 2 20 64 342 Dockerfile 2 5 27 55 Markdown 2 15 0 48 INI 1 0 0 8 ------------------------------------------------------------------------------- SUM: 690 17799 8319 81148 -------------------------------------------------------------------------------
Impact on Other Projects