Briefly, what LLVM does takes an intermediate representation of your code and compile that down to highly optimized machine code, as the code is running. Contrary to what you might expect, llvmlite does not use any LLVM shared libraries that may be present on the system, or in the conda environment. The random numbers are provided by ctypes. It supports Python compilation to run on either CPU or GPU hardware and is designed to integrate with Python scientific software stacks, such as NumPy. Starting with numba version 0. conda install linux-ppc64le v0. I will not rush to make any claims on numba vs cython. Writing a faster mean-squared-displacement function¶. Numba¶ Numba can be used with either CTypes or CFFI. arange into a low level native loop, so it defaults to the object layer which is much slower and usually the same speed as pure python. When Numba cannot infer all type information, some Python objects are given generic object status, and some code is generated using the Python runtime. I don't use Anaconda so I can't confirm if it really is that easy, but if you're using vanilla python it's a bit different: pip install numba. Tools, libraries, and frameworks: Numba, NumPy Learning Objectives At the conclusion of the workshop, you'll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba: > GPU-accelerate NumPy ufuncs with a few lines of code. In fact, we can infer from this that numba managed to generate pure C code from our function and that it did it already previously. Please note that all the chords provided on Chords-Lanka. Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package. Such inference isn't possible in every setting. You can also take a look at Cython for speeding up code and integration with code written in C as shared libraries. • Numba can be used with Spark to easily distribute and run your code on Spark workers with GPUs • There is room for improvement in how Spark interacts with the GPU, but things do work. The CUDA library functions have been moved into Accelerate, along with some Intel MKL functionality. python,jit,ode,numba. Otherwise numba may be installed using pip (pip install numba). Numba supports defining GPU kernels in Python, and then compiling them to C++. I think it's time to use NumPy array with Numba library wherever possible in Big Data Science projects. import numba as nb or some of its functions. 2 Now try this with numba. Press "?" to see the full list of shortcuts. like not using some of the cmake. However, it is wise to use GPU with compute capability 3. And each accounting system using different codes doesn't help either. LLVM takes Numba's translation of the Python code and compiles it into something like assembly code, which is a set of very low-level and very fast. The current iteration of the BitGenerators all export a small set of functions through both interfaces. Numba provides a set of options for the @jitdecorator that can you can use to tweak the compiler's analysis and code generation strategies, based on what you know about the code. As in the introduction article, we will use the simple hydrological model again, as the function we want to speed up with Numba. Definition of numba in the Definitions. At first I was numpy. Numba speeds up basic Python by a lot with almost no effort. Maybe one reason is that access numpy array is 2 times slower in pypy than in CPython with numba. Related Links. Learn how to use Numba JIT compiler to speed your Python and NumPy code. You'll learn about using just-in-time compilation, writing custom NumPy ufuncs (the. When I talk to people about Numba, I find that they quickly pick up the basics of writing CUDA kernels in Python. com are our visitor's interpretation only. 5 on your DGX. 1; linux-armv7l v0. This is especially useful for loops where Python will normally compile to machine code (the language the CPU understands) for each iteration of the. ) you should use the Python 2. Numba is a just-in-time (JIT) compiler that translates Python code to native machine instructions both for CPU and GPU. It is not designed for pandas. Related Links. Expanded coverage of pandas groupby. What is Numba doing to make code run quickly? When you add the jit decorator (or function call), Numba examines the code in the function and then tries to compile it using the LLVM compiler. Julia is not just "a faster Python". No cable box required. It can even be compiled to run on either CPU or GPU hardware. If I'm investigating whether Numba can help, I use jit as a function call, so I can compare results. In those cases we may decide to implement alternative (optional) back-ends in numba. Some research and testing may be require for specific cases. Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. Contrary to what you might expect, llvmlite does not use any LLVM shared libraries that may be present on the system, or in the conda environment. Optimization with Numba¶ When NumPy broadcasting tricks aren't enough, there are a few options: you can write Fortran or C code directly, you can use Cython, Weave, or other tools as a bridge to include compiled snippets in your script, or you can use a tool like Numba to speed-up your loops without ever leaving Python. Using Numba is very straightforward and a Python function written in a decent manner can be speeded up with little effort. 1; osx-64 v0. A better solution is using Numba. Writing code in python is easy: because it is dynamically typed, we don't have to worry to much about declaring variable types (e. Congratulations are in order for Jasmine Sanders! It was announced today that Jasmine has been named Sports Illustrated 2019 Rookie of the Year! Sanders was chosen from an exceptional rookie class that included Camille Kostek, Haley Kalil, Halima Aden, Jasmine Sanders, Kelsey Merritt, Olivia Brower. Portable or not, the choice is yours! WinPython is a portable application, so the user should not expect any integration into Windows explorer during installation. No cable box required. The current iteration of the BitGenerators all export a small set of functions through both interfaces. In the past few months, I've been using Numba in my own code, and I recently released my first real package using Numba, skan. Maybe one reason is that access numpy array is 2 times slower in pypy than in CPython with numba. You can also take a look at Cython for speeding up code and integration with code written in C as shared libraries. Python lists are too heavy in some cases. Using Numba is usually about as simple as adding a decorator to your functions: from numba import jit @jit def numba_mean (x): total = 0 for xi in x: total += xi return total / len (x) You can supply optional types, but they aren't required for performant code as Numba can compile functions on the fly using its JIT compiler. next_double. So, should we use Cython or Numba if we want to have efficient LU factorization in Python? The answer is none of them. Contrary to what you might expect, llvmlite does not use any LLVM shared libraries that may be present on the system, or in the conda environment. Anaconda Cloud. This where it shines. The random numbers are provided by ctypes. Viewed 108 times 2. You can start with simple function decorators to automatically compile your functions, or use the powerful CUDA libraries exposed by pyculib. Press "?" to see the full list of shortcuts. Clear the game board and receive 5000 bonus points. multimethods) for powerful polymorphism, much more common use of type annotations/generics (Python has recently added type anno. Numba is quite easy to use. Download it once and read it on your Kindle device, PC, phones or tablets. More Info ». It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. numba/numba #2979. It can even be compiled to run on either CPU or GPU hardware. Just-in-time compilation (JIT)¶ For programmer productivity, it often makes sense to code the majority of your application in a high-level language such as Python and only optimize code bottleneck identified by profiling. The easiest way to install it is to use Anaconda distribution. I recommend that you use a Python distribution like Anaconda or a Docker image where Numba is already installed. Using an example application, we show how to write CUDA kernels in Python, compile and call them using the open source Numba JIT compiler, and execute them both locally and remotely with Spark. import numba as nb or some of its functions. It is not designed for pandas. It uses the LLVM compiler project to generate machine code from Python syntax. Numba is a Just-In-Time compiler for Python functions. Numba thus provides equivalent performance to C or Cython without the need to either use a different interpreter or actually code in C. My previous post put Numba to use, accelerating my code for generating the Burning Ship fractal by about 10x. Anaconda Cloud. round() calls to numpy. We are also part of Australia's tax news podcast - Tax Talks. But often we don't have time to get into some of the more advanced things that Numba has to offer GPU programmers. What am I doing wrong here? Any help would be welcome! I also included code to generate representative pseudo data for 1000 records below:. Viewed 108 times 2. May 16, 2017 · I am trying to run python code in my NVIDIA GPU and googling seemed to tell me that numbapro was the module that I am looking for. Quick Start¶. In an effort to further explore the benefits of Numba we decided to use a new code that implements floating point operations. 1; linux-32 v0. While this was only for one test case, it illustrates some obvious points: Python is slow. Live TV from 70+ channels. It's a very exciting result to see the calculations finished in 40. Upon completion, you'll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs. GitHub Gist: instantly share code, notes, and snippets. 3 Make two identical functions: one that releases and one that holds the GIL. Numba can be modified to run on PyPy with a set of small changes. Numba One - Numba Ten: A Vietnam War Novel - Kindle edition by Nathaniel R. Since 2005 the Government of Kenya (GoK) has initiated registration of persons in the country using a harmonized approach to address duplication of efforts and to cut costs in registration processes. Numba is a library that enables just-in-time (JIT) compiling of Python code. What am I doing wrong here? Any help would be welcome! I also included code to generate representative pseudo data for 1000 records below:. conda install linux-ppc64le v0. Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Use 4 similar colored tiles and receive 600 bonus points. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes. Harris IDL. Apply key GPU memory management techniques. It uses the LLVM compiler project to generate machine code from Python syntax. NumbaはPythonおよびNumPyのサブセットのソースコードを高速に実行する機械語に変換するJITコンパイラ。llvmliteにて、LLVMをバックエンドに使用し、CPUおよびGPU向けにコンパイルする。Anaconda, Inc. Welcome to Cython's Documentation Dag Sverre Seljebotn, Greg Ewing, William Stein, Gabriel Gellner, et al. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Support GPU accelerators for the stencil computations using numba. In addition, not all code can be vectorized. With this model to optimize, we will explore the different options you have, when using the @jit decorator. Stencil computations are obvious candidates for GPU acceleration, and this is a good accessible point where novice users can specify what they want in a way that is sufficiently constrained for automated systems to rewrite it as CUDA somewhat easily. Clear the game board and receive 5000 bonus points. Goal: wrap Intel's Vector Maths Library (VML) and use it from Numba; VML is a fast library for computations on arrays. You can also save this page to your account. Numba speeds up basic Python by a lot with almost no effort. llvmlite was created last year by the developers of Numba (a JIT compiler for scientific Python), and just recently replaced llvmpy as their bridge to LLVM. Numba provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. This patch uses numba to execute Python byte-code without access to CPython API (no open(), import, or modules like os. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. numba/numba #2979. The easiest way to install it is to use Anaconda distribution. Numba is a library that enables just-in-time (JIT) compiling of Python code. All the above code is available as an ipython notebook: numba_vs_cython. At first I was numpy. Use the Numba JIT compiler to speed up calculation with a single decorator. We are using the same pattern that IPython uses for both numba and llvmpy. Just call, email or use the contact page. Numba can be modified to run on PyPy with a set of small changes. I've patched it in the AUR to use arch's current LLVM with dynamic linking - it passes the tests and all, but the triple name has changed: it is "amdgcn-amd-amdhsa" now rather. Did you know? Hey developers. Numba: a LLVM-based Python JIT compiler. 5 I don't recommend attempting to install CUDA 7. It took a bit of work to get set-up but it's a nice approach that should make it easier for the community to maintain the documentation and web-site of both of these projects. In reality, I have an array of positions for numtrj particles and numpts timepoints and dim dimensions: pos[numtrj, numpts, dim]. Method 1: Using SciPy's ODE solver. 0; linux-aarch64 v0. like not using some of the cmake. In its documentation it says "One objective of Numba is having a seamless integration with NumPy. 5 projects that push Python performance Python's never been as speedy as C or Java, but several projects are in the works to get the lead out of the language. Note that numba is "kind-of" like the Julia language and pypy: it can optimize dumb loops, but if your code use some smart vectorization you won't get much out of it. Use photos, nicknames, and automatic translations to share your thoughts with the world. Burning Ship version 2: using Numba. I tried out numba and it's @jit decorator does seem to speed up. Use Numba to create and launch custom CUDA kernels. Tested with Spark and Dask. The current iteration of the BitGenerators all export a small set of functions through both interfaces. Use the Numba JIT compiler to speed up calculation with a single decorator. It does not work yet on all of Python, but when it does work it can do marvels. Numba supports defining GPU kernels in Python, and then compiling them to C++. Mainly focused on array-oriented and numerical code; Heavily object-oriented, dynamic code not the target use case; Alternative to using native code (e. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. pandas Updated syntax of pandas functions such as resample. During the 1940s and 1950s, the Mexican and American film industry expanded the use of the term rumba as rumbera films became popular. com are our visitor's interpretation only. Writing code in python is easy: because it is dynamically typed, we don't have to worry to much about declaring variable types (e. 0 or above with an up-to-data Nvidia driver. I've patched it in the AUR to use arch's current LLVM with dynamic linking - it passes the tests and all, but the triple name has changed: it is "amdgcn-amd-amdhsa" now rather. Start by importing it. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. We'll demonstrate how Python and the Numba JIT compiler can be used for GPU programming that easily scales from your workstation to an Apache Spark cluster. When Numba cannot infer all type information, some Python objects are given generic object status, and some code is generated using the Python runtime. Coding is much faster in Python, but the performance could be better. Accelerating the functions above just requires adding the @autojit decorator to each of them. integers vs. LLVM takes Numba's translation of the Python code and compiles it into something like assembly code, which is a set of very low-level and very fast. Once I've decided to use Numba, I stick with the decorator syntax since it's much prettier (and I don't care if the "original" function is available). Let's say I work for a company that hands out different types of loans. What am I doing wrong here? Any help would be welcome! I also included code to generate representative pseudo data for 1000 records below:. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, run time or statically (through the included Pycc tool). Searching for "numba" Results: Showing speed improvement using a GPU with CUDA and Python with numpy on Nvidia Quadro 2000D. Writing a faster mean-squared-displacement function¶. がスポンサーになっている。 デコレーター. I am really pleased to see that Numba and Cython exhibit equivalent performance! I am used to profile my code and cythonize the slow parts (using memory views to update the dataframes and openMP to multithread). Numba is a Just-In-Time compiler for Python functions. integers vs. 0 or above as this allows for double precision operations. 1; source v0. numba¶ Although we always stive to write code for forward and adjoint operators that takes advantage of the perks of numpy and scipy (e. Keeping all code as Python code:. Numba can be very easy to use. It uses the LLVM compiler project to generate machine code from Python syntax. Is it possible to use Numba in QGIS? [closed] Ask Question Asked 1 year, 10 months ago. Numba, a Python compiler from Anaconda that can compile Python code for execution on CUDA-capable GPUs, provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. Let us use another tool called Numba. Once I've decided to use Numba, I stick with the decorator syntax since it's much prettier (and I don't care if the "original" function is available). generic_filter well-usable with minimal effort. The CUDA library functions have been moved into Accelerate, along with some Intel MKL functionality. I've made a package 'python-numba-roctools-git' to support AMD ROCm target in numba. 1 Timing python code. Numba is compatible with Python 2. Use the Numba JIT compiler to. The parts of LLVM required by llvmlite are statically linked at build time. NumPy aware dynamic Python compiler using LLVM. A GST code tells your accounting system whether a transaction includes GST and if yes, what to do with it. In an effort to further explore the benefits of Numba we decided to use a new code that implements floating point operations. If I'm investigating whether Numba can help, I use jit as a function call, so I can compare results. Quoted from Numba's Documentation: "Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). This post explains how to use moment matching to reduce. 4 or later, and Numpy versions 1. Philosophy ¶ While llvmpy exposed large parts of the LLVM C++ API for direct calls into the LLVM library, llvmlite takes an entirely different approach. 1 Timing python code. Using parallel=True results in much easier to read code, and works for a wider range of use cases. interp (x, xp, fp, left=None, right=None, period=None) [source] ¶ One-dimensional linear interpolation. I recently attended the LLVM Cauldron to give a talk, Accelerating Python code with Numba and LLVM (slides, video). It's worthwhile to use Numba or Cython with Python, to get Fortran-like speeds from Python, comparable with Matlab at the given test. Mobile friendly. Portable or not, the choice is yours! WinPython is a portable application, so the user should not expect any integration into Windows explorer during installation. The CUDA library functions have been moved into Accelerate, along with some Intel MKL functionality. Numba aims to automatically compile functions to native machine code instructions on the fly. Install and using numba on mac. Using Numba in Distributed Computing •Numba-compiled functions can be serialized and sent to remote systems. Why use numba. 5 on your DGX. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Python examples demonstrating performance improvements using cython and numba 2017 by Goutham Balaraman. In the past few months, I've been using Numba in my own code, and I recently released my first real package using Numba, skan. com courses again, please join. I've made a package 'python-numba-roctools-git' to support AMD ROCm target in numba. Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package. Mobile friendly. sin, cos, exp, sqrt, etc. Numba does have support for. As in the introduction article, we will use the simple hydrological model again, as the function we want to speed up with Numba. Use photos, nicknames, and automatic translations to share your thoughts with the world. Since 2005 the Government of Kenya (GoK) has initiated registration of persons in the country using a harmonized approach to address duplication of efforts and to cut costs in registration processes. Just-in-time compilation (JIT)¶ For programmer productivity, it often makes sense to code the majority of your application in a high-level language such as Python and only optimize code bottlenecks identified by profiling. Is it possible that you have created a new virtual environment with the project you have opened in PyCharm? I don't use anaconda, but it may be that it requires some special setup in PyCharm to use it. And the numba and cython snippets are about an order of magnitude faster than numpy in both the benchmarks. Let me emphasize that, because this is a totally different approach from using Numba or NumPy's ctypes interface, it may not be a faithful toe-to-toe comparison, but it is a good baseline. I don't use Anaconda so I can't confirm if it really is that easy, but if you're using vanilla python it's a bit different: pip install numba. Contribute to harrism/numba_examples development by creating an account on GitHub. Method 1: Using SciPy's ODE solver. Burning Ship version 2: using Numba. python,numpy,jit,numba. Use photos, nicknames, and automatic translations to share your thoughts with the world. 0; linux-aarch64 v0. With these changes, 91. I tried using Numba to see if I can get any speed gains, but it did not change the execution time, whether I add Numba @jit decorators for each function or not. You can also save this page to your account. Viewed 108 times 2. The Intel team has benchmarked the speedup on multicore systems for a wide range of algorithms: Parallel Loops. Numba is a Just-In-Time compiler for Python functions. Added pandas Categorical. So what is the identity of Julia?. round() calls to numpy. multimethods) for powerful polymorphism, much more common use of type annotations/generics (Python has recently added type anno. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). It is basically evaluate a phase term over the matrix, and the phase term is determined by location in the matrix. Contribute to numba/numba development by creating an account on GitHub. Philosophy ¶ While llvmpy exposed large parts of the LLVM C++ API for direct calls into the LLVM library, llvmlite takes an entirely different approach. Showing speed improvement using a GPU with CUDA and Python with numpy on Nvidia Quadro 2000D. You can also save this page to your account. Briefly, what LLVM does takes an intermediate representation of your code and compile that down to highly optimized machine code, as the code is running. The Numba developers decided to start a new binding from scratch, with an entirely different architecture, centered around the specific requirements of a JIT compiler. Use 4 similar colored tiles and receive 600 bonus points. Normally you can only install it with conda and it carries a statically linked LLVM6. It's also about language features that Python lacks like multiple dispatch (a. With this model to optimize, we will explore the different options you have, when using the @jit decorator. Note: if you use a modern Linux distribution (Ubuntu 7. GitHub Gist: instantly share code, notes, and snippets. Burning Ship version 2: using Numba. Writing code in python is easy: because it is dynamically typed, we don't have to worry to much about declaring variable types (e. A better solution is using Numba. NumPy aware dynamic Python compiler using LLVM. Let us use another tool called Numba. NumPy aware dynamic Python compiler using LLVM. C-API or CFFI) with C, Fortran, or Cython. Numba is compatible with Python 2. The easiest way to install it is to use Anaconda distribution. , broadcasting, ufunc), in some case we may end up using for loops that may lead to poor performance. Use numba to compile python loops or array expressions to fast llvm, and problem solved. I want to use numba package in QGIS. Is it possible to use Numba in QGIS? [closed] Ask Question Asked 1 year, 10 months ago. Use Numba to create and launch custom CUDA kernels. Numba provides a set of options for the @jitdecorator that can you can use to tweak the compiler's analysis and code generation strategies, based on what you know about the code. Using Apache Arrow as the in-memory storage and Numba for fast, vectorized computations on these memory regions, it is possible to extend Pandas in pure Python while achieving the same performance of the built-in types. Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Today's post is going to look at fast ways to filter an image in Python, with an eye towards speed and memory efficiency. Switching three numpy. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. They are extracted from open source Python projects. Briefly, what LLVM does takes an intermediate representation of your code and compile that down to highly optimized machine code, as the code is running. However, the WinPython Control Panel allows to "register" your distribution to Windows (see screenshot below). The parts of LLVM required by llvmlite are statically linked at build time. Today's post is going to look at fast ways to filter an image in Python, with an eye towards speed and memory efficiency. Since 2005 the Government of Kenya (GoK) has initiated registration of persons in the country using a harmonized approach to address duplication of efforts and to cut costs in registration processes. Numba One - Numba Ten: A Vietnam War Novel - Kindle edition by Nathaniel R. numba/numba #2979. I recently attended the LLVM Cauldron to give a talk, Accelerating Python code with Numba and LLVM (slides, video). In this section, we try to test the interoperability between two different modules within the same Python program, namely, CuPy and Numba. You can vote up the examples you like or vote down the exmaples you don't like. Firstly, I have a code as below to create a 3d matrix. 3 Make two identical functions: one that releases and one that holds the GIL. 5% of Numba tests pass. next_double. You can also save this page to your account. It's also about language features that Python lacks like multiple dispatch (a. Execution speed appears to be similar to using Numba on CPython, with a small overhead. We are also part of Australia's tax news podcast - Tax Talks. Contrary to what you might expect, llvmlite does not use any LLVM shared libraries that may be present on the system, or in the conda environment. Numba provides additional facilities to automate such calculations: numba. Numba supports CUDA-enabled GPU with compute capability (CC) 2. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba does something quite different. Quoted from Numba's Documentation: "Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). You can also save this page to your account. Showing 1-20 of 1099 topics. Long ago (more than 20 releases!), Numba used to have support for an idiom to write parallel for loops called prange(). Numba? Numba gives you the power to speed up your applications with high performance functions written directly in Python. This patch uses numba to execute Python byte-code without access to CPython API (no open(), import, or modules like os. So, should we use Cython or Numba if we want to have efficient LU factorization in Python? The answer is none of them. Speeding up Python loops. Let me emphasize that, because this is a totally different approach from using Numba or NumPy's ctypes interface, it may not be a faithful toe-to-toe comparison, but it is a good baseline. Hence, it's prudent when using Numba to focus on speeding up small, time-critical snippets of code. In this second setting, Numba typically provides only minor speed gains — or none at all. I recommend that you use a Python distribution like Anaconda or a Docker image where Numba is already installed. Trouble with speeding up functions with numba JIT. That said numba might be a good idea to speed up sequential pure python code, but I feel this is outside of the scope of the question. If you like this song, please buy the relative CD to support the artist. C-API or CFFI) with C, Fortran, or Cython. Fryman and Ibrahim Hur, Principal Engineers, Intel Corporation. Numba doesn't have this issue, so I wanted to learn a little more. They are extracted from open source Python projects. Numba can compile Python functions for both CPU and GPU execution, at the same time. Welcome to Cython's Documentation Dag Sverre Seljebotn, Greg Ewing, William Stein, Gabriel Gellner, et al. Showing speed improvement using a GPU with CUDA and Python with numpy on Nvidia Quadro 2000D. from_numba() factory method. sin, cos, exp, sqrt, etc. floating point numbers).