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Articles and news on parallel programming and code modernization

Appentra Releases Parallelware Trainer 1.4

Today Appentra released Parallelware Trainer 1.4, an interactive, real-time code editor with features that facilitate the learning, usage, and implementation of parallel programming by understanding how and why sections of code can be parallelized. “As Appentra strives to make parallel programming easier, enabling everyone to make the best use of parallel computing hardware from the multi-cores in a laptop to the fastest supercomputers. With this new release, we push Parallelware Trainer further towards that goal.”

Full Roundup: SC19 Booth Tour Videos from insideHPC

Now that SC19 is behind us, it’s time to gather our booth tour videos in one place. Throughout the course of the show, insideHPC talked to dozens of HPC innovators showcasing the very latest in hardware, software, and cooling technologies.

An Alternative to OpenMP and an On-Ramp to Future C++ Standards

In this edition of Let’s Talk Exascale, Christian Trott of Sandia National Laboratories shares insights about Kokkos, a programming model for numerous Exascale Computing Project applications. “Kokkos is a programming model being developed to deliver a widely usable alternative to programming in OpenMP. It is expected to be easier to use and provide a higher degree of performance portability, while integrating better into C++ codes.”

Podcast: Developing Multiprecision Algorithms with the Ginkgo Library Project

In this episode of Let’s Talk Exascale, Hartwig Anzt from the University of Tennessee describes how the ECP Ginkgo Library Project is developing a vision for multiprecision algorithms. “Anything reducing the data transfer volume while still communicating the information can help make use of the software more efficient. Benefits are available even if the decreased data transfer volume comes at the cost of additional operations.”

Intel’s Kent Moffat describes the exciting new launch of oneAPI

In this video, Kent Moffat, senior product manager from Intel, describes the oneAPI initiative, an ambitious shift from today’s single-architecture, single-vendor programming models to a unified, simplified programming model for application development across heterogeneous architectures, including CPUs, GPUs, FPGAs and other accelerators.

Interview: Advancing HPC in the UK in the Age of Brexit

In this special guest feature, Robert Roe from Scientific Computing World interviews Mark Parsons on the strategy for HPC in the UK. “We are not part of EuroHPC, so we are not going to have access to the exascale systems that appear in Europe in 2023, they will also have some very large systems in 2021, around 150 to 200 Pflop systems, and we will not have access to that which will have a detrimental effect on our scientific and industrial communities ability to use the largest scale of supercomputing.”

Parallel Computing in Python: Current State and Recent Advances

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.”

New Intel Xeon W and X-Series Processors Accelerate Workstation AI

Today Intel unveiled its latest lineup of Intel Xeon W and X-series processors, which puts new classes of computing performance and AI acceleration into the hands of professional creators and PC enthusiasts.  “The professional and enthusiast communities require product engineering that caters to their specific mission-critical needs and keeps them on the cutting edge of technology advancements. This means the best hardware and software optimizations, but also looking at how we can infuse things like AI acceleration,”

Exploring the Performance Optimization and Productivity Project

The “quest” for improved performance is never over, if you want to remain competitive in your respective market. Your end users will undoubtedly call for more speed in the future, and the models your clients are building are likely bigger and more complex than ever. Enter the Performance Optimisation and Productivity (PoP) project.

Parallelism in Python: Directing Vectorization with NumExpr

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.