David Bolton from Slashdot shows how ‘embarrassingly parallel’ code can be sped up over 2000x (not percent) by utilizing Intel tools including the Intel Python compiler and OpenMP. “The Intel Distribution for Python* 2017 Beta program is now available. The Beta product adds new Python packages like scikit-learn, mpi4py, numba, conda, tbb (Python interfaces to Intel Threading Building Blocks) and pyDAAL (Python interfaces to Intel Data Analytics Acceleration Library). “
Search Results for: Python code
211-3605/24-2M Specialised Python DevOps developer with Linux systems experience for the SCIENCE HPC Center
February 13, 2024 by
Specialised Python DevOps developer with Linux systems experience for the SCIENCE HPC Center, at the Department of Computer Science at the University of Copenhagen (DIKU) The SCIENCE HPC Center at DIKU is looking for a Python developer for an in-house developed systems called ERDA/SIF[1]. You will become part of a team with many years of […]
Metabob Launches New AI Generative VSCode Extension
May 4, 2023 by
PALO ALTO, Calif. – May 4, 2023 – Metabob announced today a new artificial intelligence (AI)-powered virtual studio code (VSCode) extension for its debugging and refactoring tool that the company says speeds software development code review by 60 percent. It provides users with functional context-sensitive code recommendations to fix errors right in the integrated development […]
Exascale: Aurora Early Adopter Series March 30 – Data Parallel Python, Bringing oneAPI to Python
March 4, 2022 by
March 4, 2022 — The Argonne Leadership Computing Facility (ALCF) will hold an online event on Wednesday, March 30 at 11 am Central Time as part of its Aurora Early Adopter Series – Praveen Kundurthy, Intel senior technical evangelist, AI and oneAPI, will cover how Data Parallel Python can be used to develop high-performing code […]
Video: Profiling Python Workloads with Intel VTune Amplifier
April 7, 2020 by
Paulius Velesko from Intel gave this talk at the ALCF Many-Core Developer Sessions. “This talk covers efficient profiling techniques that can help to dramatically improve the performance of code by identifying CPU and memory bottlenecks. Efficient profiling techniques can help dramatically improve the performance of code by identifying CPU and memory bottlenecks. We will demonstrate how to profile a Python application using Intel VTune Amplifier, a full-featured profiling tool.”
CUDA-Python and RAPIDS for blazing fast scientific computing
February 20, 2020 by
Abe Stern from NVIDIA gave this talk at the ECSS Symposium. “We will introduce Numba and RAPIDS for GPU programming in Python. Numba allows us to write just-in-time compiled CUDA code in Python, giving us easy access to the power of GPUs from a powerful high-level language. RAPIDS is a suite of tools with a Python interface for machine learning and dataframe operations. Together, Numba and RAPIDS represent a potent set of tools for rapid prototyping, development, and analysis for scientific computing. We will cover the basics of each library and go over simple examples to get users started.”
Parallel Computing in Python: Current State and Recent Advances
October 9, 2019 by
Pierre Glaser from INRIA gave this talk at EuroPython 2019. “Modern hardware is multi-core. It is crucial for Python to provide high-performance parallelism. This talk will expose to both data-scientists and library developers the current state of affairs and the recent advances for parallel computing with Python. The goal is to help practitioners and developers to make better decisions on this matter.”
Parallelism in Python: Directing Vectorization with NumExpr
September 9, 2019 by
According to a new edition of Parallel Universe Magazine, from Intel, Python has several pathways to vectorization. These range from just-intime (JIT) compilation with Numba 1 to C-like code with Cython. A chapter from a recent edition of Parallel Universe Magazine, explores parallelism in Python.
Achieving Parallelism in Intel Distribution for Python with Numba
July 11, 2019 by
The rapid growth in popularity of Python as a programming language for mathematics, science, and engineering applications has been amazing. Not only is it easy to learn, but there is a vast treasure of packaged open source libraries out there targeted at just about every computational domain imaginable. This sponsored post from Intel highlights how today’s enterprises can achieve high levels of parallelism in large scale Python applications using the Intel Distribution for Python with Numba.
Making Python Fly: Accelerate Performance Without Recoding
April 19, 2019 by
Developers are increasingly besieged by the big data deluge. Intel Distribution for Python uses tried-and-true libraries like the Intel Math Kernel Library (Intel MKL)and the Intel Data Analytics Acceleration Library to make Python code scream right out of the box – no recoding required. Intel highlights some of the benefits dev teams can expect in this sponsored post.
