The Parallelware technology is now compatible with OpenPOWER systems. POWER processors are great for multi-threaded applications, such as those taking advantage of OpenMP. For instance, a single 12-core SMT8 POWER9 processor is capable of running up to 96 simultaneous threads. As a matter of fact, the #1 and #2 fastest supercomputers run POWER9 processors. Now […]
Technical
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 […]
On the Parallelization of Finite-Difference Time-Domain Codes with Parallelware
The focus of this report is showing that Parallelware succeeds in the 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 and […]
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 […]
Latest best practices in parallel programming in C with OpenMP
Parallel programming is a complex, time-consuming and error-prone undertaking even for experienced programmers. Software tools that help the programmer in the development and manteinance of parallel codes are extremely sensitive to syntactical variations in the implementation of an algorithm. In particular, the C programming language poses a difficult challenge due to its syntax and to […]
Acceleration 5x of computational electromagnetics MOM Codes with Parallelware
One fundamental goal behind Computational Electromagnetics (CEM) is to reliably simulate the behavior of electromagnetic devices before they are actually built. For this purpose, many powerful numerical analysis techniques have been developed in the field of CEM in the last decades. Some of the most commonly used methods today are Finite Difference Time Domain (FDTD), […]
Parallelization with Parallelware
What’s Parallelware? Parallelware is a software tool for guided parallelization which in just a click, will convert your sequential code into a parallel code. The target audience are programmers, software developers, researchers, engineers and scientists from industry and academy that use compute-intensive simulations in their application areas. How to use Parallelware? In previous posts we saw how […]