Tools for Accelerating Python

If you need to speed up your Python program there are several possible approaches, with varying degree of effort needed, here is (probably not complete) overview:

1. Rewrite your Python code by parallelizing or optimizing/replacing/tuning algorithm(s), e.g. using: 
• Hadoop or Disco
• Open source implementations of MapReduce 
• Parallel Python 
• Message Passing Interface (MPI)
• Frequently used for numerical programming
• Bulk Synchronous Parallel (BSP) 
• RPyC
• RPC for Distributed/Parallel Programming
  
• Twisted
• Network libraries for Distributed/Parallel Programming
  
• Profiling Tools
• Threading or Microthreads (Stackless) 
2. Use a tool that can speed up your code more or less unchanged
   
• Psyco
• Just in time compiler, note: this is probably the easiest approach to try
• Pyrex
• Write and compile Python with a flavor of C data structures
• Cython 
• PyJs 
• Compile (large subset of) Python to Javascript, note: probably more interesting for client development (ajax) than server side.
• Rpython
• Compile (large subset of) Python to native code, note: part of PyPy project
• PyStream
• GPULib
• Shedskin 
• Compile (large subset of) Python to C++, some benchmarks
  
3. Replace (parts of) your Python code with another language
• Simplified Wrapper and Interface Generator (SWIG) 
• Use C/C++ from Python
  
• Fortran to Python Interface Generator (F2PY) 
• Use Fortran from Python
• llvm-py 
• Code and Compile to Assembler for running on Low-Level Virtual Machine
• CorePy 
• Write Assembly Code in Python
• Weave 
• PyInline 
• Boost.Python
• Cinpy 
• C code blended with Python (using ctypes) and runtime compilation with tcc