
.. _introduction:


************
Introduction
************

Theano motivations
------------------
Theano tries to be the **holy grail** in computing: *easy to code* and *it fast to execute* !

it works only on mathematical expressions, so you won't have:

  - Function call inside a theano function
  - Structure, enum
  - Dynamic type (Theano is Fully typed)

Unfortunately it doesn't do coffee... yet.

.. image:: pics/Caffeine_Machine_no_background_red.png


Theano status
-------------

Why you can rely on Theano:

- Theano has been developed and used since January 2008 (3.5 yrs old)
- Core technology for a funded Silicon-Valley startup
- Driven over 40 research papers in the last few years
- Good user documentation
- Active mailing list with participants from outside our lab
- Many contributors (some from outside our lab)
- Used to teach IFT6266 for two years
- Used by everyone in our lab (\textasciitilde 30 people)
- Deep Learning Tutorials
- Unofficial RPMs for Mandriva
- Downloads (June 8 2011, since last January): Pypi 780, MLOSS: 483, Assembla (``bleeding edge'' repository): unknown

Why scripting for GPUs ?
------------------------

**GPUs?**

- Faster, cheaper, more efficient power usage
- How much faster? I have seen numbers from 100x slower to 1000x faster.

  - It depends on the algorithms
  - How the benchmark is done
      
    - Quality of implementation
    - How much time was spent optimizing CPU vs GPU code

  - In Theory:

    - Intel Core i7 980 XE (107Gf/s float64) 6 cores
    - NVIDIA C2050 (515 Gf/s float64, 1Tf/s float32) 480 cores
    - NVIDIA GTX580 (1.5Tf/s float32) 512 cores
  
  - Theano goes up to 100x faster on th GPU because we don't use multiple core on CPU
    
    - Theano can be linked with multi-core capable BLAS (GEMM and GEMV)
  - If you see 1000x, it probably means the benchmark is not fair

**Scripting for GPUs?**

They *Complement each other*

- GPUs are everything that scripting/high level languages are not

  - Highly parallel
  - Very architecture-sensitive
  - Built for maximum FP/memory throughput
- CPU: largely restricted to control

  - Optimized for sequential code and low latency (rather than high throughput)
  - Tasks (1000/sec)
  - Scripting fast enough

Theano vs PyCUDA vs PyOpenCL vs CUDA
------------------------------------

- Theano

  - Mathematical expression compiler
  - Generates costum C and CUDA code
  - Uses Python code when performance is not critical

- CUDA

  - C extension by NVIDA that allow to code and use GPU

- PyCUDA (Python + CUDA)

  - Python interface to CUDA
  - Memory management of GPU objects
  - Compilation of code for the low-level driver

- PyOpenCL (Python + OpenCL)
  - PyCUDA for OpenCL

Python
------

- Interpreted language
- General-purpose high-level programming language
- OO and scripting language
- Emphasizes code readability
- Large and comprehensive standard library
- Indentation for block delimiters
- Dynamic type and memory management
- Dictionary ``d={'var1':'value1', 'var2':42, ...}``
- List comprehension: ``[i+3 for i in range(10)]``

NumPy
-----

- Base scientific computing package in Python on the CPU
- A powerful N-dimensional array object

  - ndarray.{ndim, shape, size, dtype, itemsize, stride}
  
- Sophisticated broadcasting functions
    
  - ``numpy.random.rand(4,5) * numpy.random.rand(1,5)`` -> mat(4,5)
  - ``numpy.random.rand(4,5) * numpy.random.rand(4,1)`` -> mat(4,5)
  - ``numpy.random.rand(4,5) * numpy.random.rand(5)`` -> mat(4,5)

- Tools for integrating C/C++ and Fortran code
- Linear algebra, Fourier transform and pseudorandom number generation


