Python Optimization Accelerators - Edubirdie

University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2024
Views:

49

Pages:

6
Author:

Heidy Riley

Outline       Introduction Profiling Data Structures Syntax Generators Accelerators Accelerators  These are some tools that can be used in conjunction with numpy to speed up numpy-based functions. numba  The numba library can translate portions of your Python code and compile it into machine code on demand.  Achieves a significant speedup compared with regular Python.  Compatible with numpy ndarrays.  Can generate code to execute automatically on GPUs. numba from numba import jit  The @jit decorator is used to indicate which functions are compiled.  Options: # This will get compiled when it's first executed @jit def average(x, y, z): return (x + y + z) / 3.0  GPU code generation  Parallelization  Caching of compiled code  Can produce faster array code than pure NumPy statements. # With type information this one gets # compiled when the file is read. @jit (float64(float64,float64,float64)) def average_eager(x, y, z): return (x + y + z) / 3.0 numexpr  Another acceleration library for Python.  Useful for speeding up specific ndarray expressions.  Typically 2-4x faster than plain NumPy  Code needs to be edited to move ndarray expressions into the numexpr.evaluate function: import numpy as np import numexpr as ne a = np.arange(10) b = np.arange(0, 20, 2) # Plain NumPy c = 2 * a + 3 * b # Numexpr d = ne.evaluate("2*a+3*b") f2py  Fortran code can be lightly modified and re-compiled into Python compatible functions.  High performance routines are relatively easy to code in Fortran 95/2003.  f2py is part of the numpy library.  Compiled Fortran code can be >100x faster than equivalent Python code (even when based around numpy).

Related Documents

Python Optimization Accelerators

Python Optimization Accelerators - Page 1

Python Optimization Accelerators - Page 2

Python Optimization Accelerators - Page 3

Python Optimization Accelerators - Page 4

Python Optimization Accelerators - Page 5

Python Optimization Accelerators - Page 6

of 6

Recommended Documents

Introduction to MPI - Mixing Blocking

Introduction to MPI - Mixing Blocking

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Tuning MATLAB For Better Perfomance - Code Optimization

Tuning MATLAB For Better Perfomance - Code Optimization

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Tuning MATLAB For Better Perfomance SOR Update Function

Tuning MATLAB For Better Perfomance SOR Update Function

Boston University MET CS 201 | Introduction to Programming

Lecture Note

MPI Beginning Lecture - Introduction Pt 1

MPI Beginning Lecture - Introduction Pt 1

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Lecture About Artificial Intelligence

Lecture About Artificial Intelligence

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Security Camp Cloud Security

Security Camp Cloud Security

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Tuning MATLAB For Better Perfomance

Tuning MATLAB For Better Perfomance

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Introduction and Computational Thinking Pt 1

Introduction and Computational Thinking Pt 1

Boston University MET CS 201 | Introduction to Programming

Lecture Note

LaTeX Command Summary Vocabulary

LaTeX Command Summary Vocabulary

Boston University MET CS 201 | Introduction to Programming

Cheat Sheet

Introduction to MPI Lecture Note

Introduction to MPI Lecture Note

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Introduction To C++ Part 1.3

Introduction To C++ Part 1.3

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Introduction to Databases Lecture Note

Introduction to Databases Lecture Note

Boston University MET CS 201 | Introduction to Programming

Lecture Note

New Documents from this Course

WEB Design

Boston University MET CS 201 | Introduction to Programming

Research

To Change the PMG Problem Conﬁguration

To Change the PMG Problem Conﬁguration

Boston University MET CS 201 | Introduction to Programming

Research

Introduction to Matlab Parallel Computing Tooblox Pt 3

Introduction to Matlab Parallel Computing Tooblox Pt 3

Boston University MET CS 201 | Introduction to Programming

Case

A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing

A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing

Boston University MET CS 201 | Introduction to Programming

Research

Using Crash Hoare Logic for Certifying the FSCQ File System Ch 4

Using Crash Hoare Logic for Certifying the FSCQ File System Ch 4

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Using Crash Hoare Logic for Certifying the FSCQ File System Ch 3

Using Crash Hoare Logic for Certifying the FSCQ File System Ch 3

Boston University MET CS 201 | Introduction to Programming

Lecture Note

Introduction to MATLAB Lecture 4 Pt 2

Introduction to MATLAB Lecture 4 Pt 2

Boston University MET CS 201 | Introduction to Programming

Lab

Processing of 2D RGB and Depth Data into 3D Space

Processing of 2D RGB and Depth Data into 3D Space

Boston University MET CS 201 | Introduction to Programming

Research

Introduction to MATLAB Lecture 4 Pt 3

Introduction to MATLAB Lecture 4 Pt 3

Boston University MET CS 201 | Introduction to Programming

Lab