VecPy builds native libraries from arbitrary kernel functions written in Python. Native libraries leverage multi-threading and SIMD instructions on modern x86 processors to execute the kernel as efficiently as possible. Multiple language bindings allow the vectorized kernel to be called from Python, C++, and Java - all from a single, shared library.

One of the primary design goals of VecPy is simplicity. In just a few lines of code, VecPy translates and compiles a Python function into an efficient, data-parallel native library. The generated library can then be loaded as a Python module, allowing the optimized function to be used as a drop-in replacement for the original Python function. The following program illustrates how simple it can be to use VecPy to significantly improve Python program performance.

#Import VecPy
from vecpy.runtime import *
from vecpy.compiler_constants import *
#Define the kernel
def volume(radius, volume):
 volume = (4/3 * math.pi) * (radius ** 3)
#Generate some data
def data():
 array = get_array('f', 10)
 for i in range(len(array)): array[i] = (.1 + i/10)
 return array
radii, volumes = data(), data()
#Call VecPy to generate the native module
vectorize(volume, Options(Architecture.avx2, DataType.float))
#Import the newly-minted module and execute kernel
from vecpy_volume import volume
volume(radii, volumes)
#Print the results!
print('Radius:', ', '.join('%.3f'%(r) for r in radii))
print('Volume:', ', '.join('%.3f'%(v) for v in volumes))

Other design goals of VecPy include utility, flexibility, and efficiency.

VecPy aims to implement a sufficiently large feature set to be useful for meaningful, real-world applications. Conditional operations within if-elif-else blocks and while loops are fully implemented, and the following Python operators, functions, and constants are currently available:

• Operators
  • Arithmetic: +, -, *, /, //, %, **
  • Comparison: ==, !=, >, >=, <, <=
  • Boolean: and, or, not
  • Bitwise: &, |, ^, ~, <<, >> (integer only)
• Functions
  • global: abs, max, min, pow, round
  • math: acos, acosh, asin, asinh, atan, atan2, atanh, ceil, copysign, cos, cosh, erf, erfc, exp, expm1, fabs, floor, fmod, gamma, hypot, lgamma, log, log10, log1p, log2, pow, sin, sinh, sqrt, tan, tanh, trunc
• Constants
  • math: e, pi

VecPy provides many options to allow for extensive customization. The following data types and language bindings are currently supported:

• Data Types
  • 32-bit floats
  • 32-bit unsigned integers
• Language Bindings
  • C++
  • Python
  • Java

VecPy relies on multi-threading and SIMD execution to achieve the fastest possible performance. The following x86 architectures and operating systems are currently supported:

• Architectures
  • Generic (no SIMD)
  • SSE4.2
  • AVX2
• Operating Systems
  • Linux

• Python 3.x (to run VecPy)
• g++ (to compile the native library)
• Optional: Python 3.x headers if compiling as a Python module
• Optional: JDK if compiling for use with Java via JNI