Appentra is pleased to announce the release of Parallelware Trainer 1.1, improving the provision of accessible HPC and parallel programming training using OpenMP and OpenACC.Appentra’s goal has always been to minimize and eventually remove the parallel software development barrier, democratizing access to HPC and making parallel computing easier for everyone. Parallelware Trainer 1.1 provides an […]
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Appentra releases Parallelware Trainer 1.0 helping to improve access to HPC training
Parallelware Trainer 1.0 continues Appentra’s mission to bring HPC to everyone by providing enhanced training support A Coruña, Spain, 13 September 2018 — Appentra Solutions releases the newest HPC training assistance tool, Parallelware Trainer 1.0. helping to address the company’s mission of bringing High Performance Computing (HPC) and parallel programming to everyone. Appentra’s goal has […]
Using LLVM for guided parallelism
Computational hardware is rapidly changing with an increasing movement to multi-core processors available everywhere from your mobile phone to the fastest supercomputers. To take advantage of these technologies software needs to be able to execute in parallel: something that isn’t straightforward to achieve. In addition, for optimal performance and ease of use software ideally needs […]
The Parallelware Method: providing assisted parallelism by understanding parallel patterns
The use of multiple cores is now ubiquitous in software. From the fastest supercomputers in the world to mobile phones, multi-core hardware is now available for everyone to use. However, the difficulty comes in utilizing the hardware that is available. The Parallel Computing Laboratory at UC Berkeley describes the challenge as “Writing programs that scale […]
From CesgaHack17 to SC17: A Success Story With Parallelware Trainer
One of the goals of the GPU Hackathons that we organize is to create a collaborative relationship with the participating teams to continue researching and creating success stories that demonstrate the capabilities of Parallelware Trainer as an experiential learning tool of parallel programming. The first edition of the CesgaHack, in the summer of 2017, hosted […]
Parallelware Trainer 0.5 is here!
We work every day to make parallel programming faster and easier, so we are excited to introduce you to the new features of Parallelware Trainer 0.5 version. Take a look at the big changes! User can now select among three implementations of parallel scalar/sparse reductions: Built-in reductions (e.g. OpenMP/OpenACC reduction clause) Atomic access (e.g. OpenMP/OpenACC […]
What are we working on right now? #guidedparallelization
Last week we realized that we had not published a post since april. Actually, the team believes it is since november, after attending SC15 in Austin, Texas. During all these months we have been working in the improving the Parallelware technology, using the market suggestions as a guide. At the end of this process, we […]
On the Automatic Parallelization of Finite-Difference Time-Domain Codes with Parallelware
The focus of this report is showing that Parallelware succeeds in the automatic parallelization of sequential C code that uses the finite-difference time-domain (FDTD) method. As case study we will use a 3D FDTD simple code written in C developed by Dmitry Gorodetsky. For illustrative purposes, the following figure shows the main magnitudes Hx, Hy […]
Latest best practices in parallel programming in C with OpenMP (part 2)
In the second article of the series on latest best practices in parallel programming in C with the OpenMP standard, we focus our attention on source code rewriting rules that more clearly exhibit the parallelism available in the sequential source code. The idea is simple: match the programming pattern in your source code and rewrite […]
Latest best practices in parallel programming in C with OpenMP (part 1)
In the first article of the series on latest best practises in parallel programming in C, we pay attention to current features and limitations of compilers that support the OpenMP standard. We present the following two case studies. First, we suggest to declare program variables at the beginning of the smallest loop scope, instead of declaring them at the […]