Moshe Goldstein/david dayan - Implementing Parallel Programming Design Patterns using EFL for Python EFL (Embedded Flexible Language), a deterministic parallel programming tool, may be embedded in any host language. Two versions of the EFL pre-compiler for Python were implemented. One translates EFL blocks into Python's Multiprocessing code, and the other one into DTM/MPI4PY code. EFL implementations of Parallel Programming Design Patterns will be shown, generated parallel code compared, and differences discussed. ----- Multi-core CPUs are abundant and utilizing them effectively requires programmers to parallelize CPU-intensive code. To facilitate this, we have developed EFL (Embedded Flexible Language), a deterministic parallel programming tool. The parallel parts of a program are written as EFL-blocks, which are embedded into a sequential host language program. The sequential parts of the program are written in the host language, outside the EFL blocks. EFL may be embedded in any host language by writing an appropriate EFL pre-compiler. At the moment, we implemented two versions of the EFL pre-compiler. Both pre-compilers translate EFL blocks into parallel Python code - one of them generates parallel code based on Python's Multiprocessing module, and the other one generates parallel code based on the DTM/MPI4PY Python module. We will present the principles upon which EFL is built. We will show the implementation of Parallel Programming Design Patterns using EFL's parallel programming constructs (such as parallel assignments, parallel for-loops, etc.). Using our two EFL pre-compilers we will show their translation to Python parallel code according to the Multiprocessing module as well as the DTM/MPI4PY module. The differences between code versions produced by the EFL pre-compilers will be discussed.