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Large scale array-oriented computing with Python

Large scale array-oriented computing with Python

Talk given at PyCon Tawain in 2012. History of SciPy and early thoughts on Numba and Blaze described.

Travis E. Oliphant

April 12, 2014

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  1. Science led to Python Raja Muthupillai Armando Manduca Richard Ehman

    1997 ⇢0 (2⇡f)2 Ui (a, f) = [Cijkl (a, f) Uk,l (a, f)] ,j
  2. Python origins. Version Date 0.9.0 Feb. 1991 0.9.4 Dec. 1991

    0.9.6 Apr. 1992 0.9.8 Jan. 1993 1.0.0 Jan. 1994 1.2 Apr. 1995 1.4 Oct. 1996 1.5.2 Apr. 1999 http://python-history.blogspot.com/2009/01/brief-timeline-of-python.html
  3. Brief History Person Package Year Jim Fulton Matrix Object in

    Python 1994 Jim Hugunin Numeric 1995 Perry Greenfield, Rick White, Todd Miller Numarray 2001 Travis Oliphant NumPy 2005
  4. 1999 : Early SciPy emerges Discussions on the matrix-sig from

    1997 to 1999 wanting a complete data analysis environment: Paul Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, and others. Activity in 1998, led to increased interest in 1999. ! In response on 15 Jan, 1999, I posted to matrix-sig a list of routines I felt needed to be present and began wrapping / writing in earnest. On 6 April 1999, I announced I would be creating this uber-package which eventually became SciPy Gaussian quadrature 5 Jan 1999 cephes 1.0 30 Jan 1999 sigtools 0.40 23 Feb 1999 Numeric docs March 1999 cephes 1.1 9 Mar 1999 multipack 0.3 13 Apr 1999 Helper routines 14 Apr 1999 multipack 0.6 (leastsq, ode, fsolve, quad) 29 Apr 1999 sparse plan described 30 May 1999 multipack 0.7 14 Jun 1999 SparsePy 0.1 5 Nov 1999 cephes 1.2 (vectorize) 29 Dec 1999 Plotting?? ! Gist XPLOT DISLIN Gnuplot Helping with f2py
  5. SciPy 2001 Founded in 2001 with Travis Vaught Eric Jones

    weave cluster GA* Pearu Peterson linalg interpolate f2py Travis Oliphant optimize sparse interpolate integrate special signal stats fftpack misc
  6. Community effort • Chuck Harris • Pauli Virtanen • David

    Cournapeau • Stefan van der Walt • Dag Sverre Seljebotn • Robert Kern • Warren Weckesser • Ralf Gommers • Mark Wiebe • Nathaniel Smith
  7. Why Python for Technical Computing • Syntax (it gets out

    of your way) • Over-loadable operators • Complex numbers built-in early • Just enough language support for arrays • “Occasional” programmers can grok it • Supports multiple programming styles • Expert programmers can also use it effectively • Has a simple, extensible implementation • General-purpose language --- can build a system • Critical mass
  8. What is wrong with Python? • Packaging is still not

    solved well (distribute, pip, and distutils2 don’t cut it) • Missing anonymous blocks • The CPython run-time is aged and needs an overhaul (GIL, global variables, lack of dynamic compilation support) • No approach to language extension except for “import hooks” (lightweight DSL need) • The distraction of multiple run-times... • Array-oriented and NumPy not really understood by most Python devs.
  9. Putting Science back in Comp Sci • Much of the

    software stack is for systems programming --- C++, Java, .NET, ObjC, web - Complex numbers? - Vectorized primitives? • Array-oriented programming has been supplanted by Object-oriented programming • Software stack for scientists is not as helpful as it should be • Fortran is still where many scientists end up
  10. Array-Oriented Computing Example1: Fibonacci Numbers fn = fn 1 +

    fn 2 f0 = 0 f1 = 1 f = 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, . . .
  11. NumPy: an Array-Oriented Extension • Data: the array object –

    slicing and shaping – data-type map to Bytes ! • Fast Math: – vectorization – broadcasting – aggregations
  12. Zen of NumPy • strided is better than scattered •

    contiguous is better than strided • descriptive is better than imperative • array-oriented is better than object-oriented • broadcasting is a great idea • vectorized is better than an explicit loop • unless it’s too complicated --- then use Cython/Numba • think in higher dimensions
  13. Conway’s game of Life • Dead cell with exactly 3

    live neighbors will come to life • A live cell with 2 or 3 neighbors will survive • With too few or too many neighbors, the cell dies
  14. APL : the first array-oriented language • Appeared in 1964

    • Originated by Ken Iverson • Direct descendants (J, K, Matlab) are still used heavily and people pay a lot of money for them • NumPy is a descendent APL J K Matlab Numeric NumPy
  15. Memory using Object-oriented Object Attr1 Attr2 Attr3 Object Attr1 Attr2

    Attr3 Object Attr1 Attr2 Attr3 Object Attr1 Attr2 Attr3 Object Attr1 Attr2 Attr3 Object Attr1 Attr2 Attr3
  16. Benefits of Array-oriented • Many technical problems are naturally array-

    oriented (easy to vectorize) • Algorithms can be expressed at a high-level • These algorithms can be parallelized more simply (quite often much information is lost in the translation to typical “compiled” languages) • Array-oriented algorithms map to modern hard-ware caches and pipelines.
  17. What is good about NumPy? • Array-oriented • Extensive Dtype

    System (including structures) • C-API • Simple to understand data-structure • Memory mapping • Syntax support from Python • Large community of users • Broadcasting • Easy to interface C/C++/Fortran code
  18. What is wrong with NumPy • Dtype system is difficult

    to extend • Immediate mode creates huge temporaries (spawning Numexpr) • “Almost” an in-memory data-base comparable to SQL-lite (missing indexes) • Integration with sparse arrays • Lots of un-optimized parts • Minimal support for multi-core / GPU • Code-base is organic and hard to extend
  19. Improvements needed • NDArray improvements • Indexes (esp. for Structured

    arrays) • SQL front-end • Multi-level, hierarchical labels • selection via mappings (labeled arrays) • Memory spaces (array made up of regions) • Distributed arrays (global array) • Compressed arrays • Standard distributed persistance • fancy indexing as view and optimizations • streaming arrays
  20. Improvements needed • Dtype improvements • Enumerated types (including dynamic

    enumeration) • Derived fields • Specification as a class (or JSON) • Pointer dtype (i.e. C++ object, or varchar) • Finishing datetime • Missing data with bit-patterns • Parameterized field names
  21. Example of Object-defined Dtype @np.dtype class Stock(np.DType): symbol = np.Str(4)

    open = np.Int(2) close = np.Int(2) high = np.Int(2) low = np.Int(2) @np.Int(2) def mid(self): return (self.high + self.low) / 2.0
  22. Improvements needed • Ufunc improvements • Generalized ufuncs support more

    than just contiguous arrays • Specification of ufuncs in Python • Move most dtype “array functions” to ufuncs • Unify error-handling for all computations • Allow lazy-evaluation and remote computation --- streaming and generator data • Structured and string dtype ufuncs • Multi-core and GPU optimized ufuncs • Group-by reduction
  23. More Improvements needed • Miscellaneous improvements • ABI-management • Eventual

    Move to library (NDLib)? • Integration with LLVM • Sparse dimensions • Remote computation • Fast I/O for CSV and Excel • Out-of-core calculations • Delayed-mode execution
  24. Blaze Main Features • New ndarray with multiple memory segments

    • Distributed ndtable which can span the world • Fast, out-of-core algorithms for all functions • Delayed-mode execution: expressions build up graph which gets executed where the data is • Built-in Indexes (beyond searchsorted) • Built-in labels (data-array) • Sparse dimensions (defined by attributes or elements of another dimension) • Direct adapters to all data (move code to data)
  25. Dimensions defined by Attributes Day Month Year High Low 15

    3 2012 30 20 16 3 2012 35 25 20 3 2012 40 30 21 3 2012 41 29 dim1
  26. NDTable (Example) Proc0 Proc1 Proc2 Proc3 Proc0 Proc1 Proc2 Proc3

    Proc0 Proc1 Proc2 Proc3 Proc4 Proc4 Proc4 Proc4 Each Partition: • Remote • Expression • NDArray
  27. Data URLs • Variables in script are global addresses (DATA

    URLs). All the world’s data you can see via web can be in used as part of an algorithm by referencing it as a part of an array. • Dynamically interpret bytes as data-type • Scheduler will push code based on data-type to the data instead of pulling data to the code.
  28. NDArray • Local ndarray (NumPy++) • Multiple byte-buffers (streaming or

    random access) • Variable-length arrays • All kinds of data-types (everything...) • Multiple patterns of memory access possible (Z-order, Fortran-order, C-order) • Sparse dimensions
  29. GFunc • Generalized Function • All NumPy functions • element-by-element

    • linear algebra • manipulation • Fourier Transform • Iteration and Dispatch to low-level kernels • Kernels can be written in anything that builds a C-like interface
  30. PyData All computing modules known to work with Blaze will

    be placed under PyData umbrella of projects over the coming years.
  31. NumPy Users • Want to be able to write Python

    to get fast code that works on arrays and scalars • Need access to a boat-load of C-extensions (NumPy is just the beginning) PyPy doesn’t cut it for us!
  32. Dynamic compilation Python Function NumPy Runtime Ufuncs Generalized UFuncs Function-

    based Indexing Memory Filters Window Kernel Funcs I/O Filters Reduction Filters Computed Columns Dynamic Compilation function pointer
  33. Numba -- a Python compiler • Replays byte-code on a

    stack with simple type- inference • Translates to LLVM (using LLVM-py) • Uses LLVM for code-gen • Resulting C-level function-pointer can be inserted into NumPy run-time • Understands NumPy arrays • Is NumPy / SciPy aware
  34. NumPy + Mamba = Numba LLVM 3.1 Intel Nvidia Apple

    AMD OpenCL ISPC CUDA CLANG OpenMP LLVM-PY Python Function Machine Code
  35. Software Stack Future? LLVM Python C OBJC FORTRA R C++

    Plateaus of Code re-use + DSLs Matlab SQL TDPL
  36. NumFOCUS • Mission • To initiate and support educational programs

    furthering the use of open source software in science. • To promote the use of high-level languages and open source in science, engineering, and math research • To encourage reproducible scientific research • To provide infrastructure and support for open source projects for technical computing
  37. NumFOCUS • Activites • Sponsor sprints and conferences • Provide

    scholarships and grants for people using these tools • Pay for documentation development and basic course development • Fund continuous integration and build systems • Work with domain-specific organizations • Raise funds from industries using Python and NumPy
  38. NumFOCUS • Directors • Perry Greenfield • John Hunter •

    Jarrod Millman • Travis Oliphant • Fernando Perez • Members • Basically people who donate for now. In time, a body that elects directors.
  39. • Large-scale data analysis products • Python training (data analysis

    and development) • NumPy support and consulting • Rich-client or web user-interfaces • Blaze and PyData Development