We’ve happy to announce the release of Parallelware Trainer 1.5 which brings exciting new features to offload computations to GPUs. Two offloading to GPU modes are now available for OpenMP. Naive parallelization using offloading consists of using the directive “target parallel for”. However, in order to better map to GPUs architectures, the use of “teams […]
Technical
Using CMake’s compilation database with Parallelware Analyzer
When building a program, seldom do we only invoke the compiler passing a source file and an output binary name. In most cases, we must supply several compiler flags such as include paths or macro definitions. A program is usually built by compiling different source files where each may require different compiler flags. One of […]
Parallelware Analyzer 0.14 now available in the Early Access Program
We are happy to announce the availability of Parallelware Analyzer 0.14. This release has a significant number of new features: Linux, Windows and MacOS versions: Parallelware Analyzer was available for Linux x86 and Power architectures supporting C/C++ and Fortran; now it is also available for Windows and MacOS supporting C/C++. Data race free analysis: pwcheck […]
Parallelware Analyzer NPB Quickstart
Index What is Parallelware Analyzer?– Where to start?– Useful options common to all toolsQuickstart with NAS Parallel Benchmarks Early Access Program is now open! Enter the program to have access to all versions of Parallelware Analyzer and support until the official release. Resources:– NAS Parallel Benchmarks (download C version) – Parallelware Analyzer webpage (link)– Full quickstart […]
Parallelware Trainer 1.4 released
We’re glad to announce the release of Parallelware Trainer 1.4 which greatly improves the deployment and user experience in supercomputers by allowing configuration through environment variables. 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 […]
Customizing Parallelware Trainer through environment variables
Starting with version 1.4.0, Parallelware Trainer can be customized using environment variables. This is particularly helpful in deployments using a multi-user license or when certain development packages are installed in non-standard locations (e.g. MPI headers being located in /opt/mpi/include instead of in /usr/include/mpi). Customizing license file location When looking for a license file, Parallelware Trainer […]
Defects and recommendations for parallelism
Implementing parallel software is hard. On top of all the kinds of bugs that may be present in sequential code, parallel code can also suffer from race conditions. A race condition exists when several threads access the same data at the same time and at least one of them writes to it. Opening the door […]
Fixing defects in parallel code: an OpenMP example
Parallelware Analyzer provides a set of tools covering the most challenging steps of your parallel development workflow. Join the Parallelware Analyzer Early Access Program to get access to the tools for free. In this post, we present the pwcheck tool which provides a static code analyzer to ensure that your code is defect-free and compliant […]
Parallelware Trainer 1.3 released
We’re happy to announce the release of Parallelware Trainer 1.3 which now supports the detection of defects and recommendations for concurrency and parallelism. We expect this new feature to further improve the learning process by providing feedback about code issues right in the integrated editor. Parallelware Trainer provides an enhanced interactive learning environment, including provision […]
OpenACC programming: Implementing a parallel forall (9/9)
This is the ninth video of our OpenACC programming course based on Appentra’s unique, productivity-oriented approach to learn best practices for parallel programming using OpenACC. Learning outcomes include how to decompose codes into parallel patterns and a practical step-by-step process based on patterns for parallelizing any code. In this last video we will learn to […]