This article presents an exercise on the use of Parallelware Analyzer to parallelize a simplified implementation of the LULESH benchmark using the OpenMP Common Core. It consists of the 16 most widely used constructs of OpenMP, the de facto standard for writing multithreaded code to take advantage of shared memory platforms. Parallelware Analyzer facilitates using […]
Parallelware Analyzer
Parallelware Analyzer and the OpenMP Common Core: an exercise with PI
This article presents an exercise on the use of Parallelware Analyzer to parallelize an implementation of the computation of the PI number using the OpenMP Common Core. It consists of the 16 most widely used constructs of OpenMP, the de facto standard for writing multithreaded code to take advantage of shared memory platforms. Parallelware Analyzer […]
Parallelware Analyzer 0.18 released
We are excited to announce a new public release of Parallelware Analyzer, the first static code analyzer specializing in performance. This version 0.18 features a new user experience designed to help you boost the performance of your code. We have redesigned the entry point of Parallelware Analyzer so that you get, right from the first […]
How to get started with Parallelware Analyzer: MATMUL example
In this blog post, we show how to get started with Parallelware Analyzer using a matrix multiplication example in C. You will see how to obtain actionable insights through optimization reports that help ensure best practices to speedup the code through parallel computing in vector, multicore and accelerator processors. A structured report displays the actionable […]
Flexibility and Performance: turbocharging loop unswitching
In the previous post we talked about loop unswitching: a technique that the compilers use to make loops faster. Loop unswitching consists of detecting conditional statements whose condition never changes during the execution of the loop (loop invariant conditions) and then moving the condition outside of the loop. Loop unswitching opens the door for other […]
Flexibility and performance: introduction to loop unswitching
Flexibility is very important in the development of software. A great deal of research and education is invested into creating flexible software. However, flexibility and performance don’t often come hand in hand, as most developers have already witnessed sometime in their developer’s careers. One of the ways to achieve flexibility is to parametrize the behavior […]
Is your algorithm running at peak performance? The roofline model
There are worlds where performance matters. For example HPC world: faster software means less wait time for the scientists. Embedded world: faster software means we can use cheaper silicon to build our product. Game world: faster software means that our game will run on slower CPUs, thus making our game more interesting to people with […]
Parallelware Analyzer 0.17 released
We are pleased to announce the availability of Parallelware Analyzer 0.17 which includes many new exciting features. New vectorization support introduces detection of opportunities for vectorization and the generation of compiler-specific and portable directives. A lot of effort has also been put into improving usability through a redesigned reporting that reflects the function and loop […]
A touch of parallelism: example of NPB CG Benchmark
Our ultimate goal at Appentra is to create a software suite that will help users achieve the peak performance of their software. One of the ways to do it is with a touch of parallelism. This post will talk about the NPB CG benchmark, a popular benchmark for comparing supercomputers, developed by NASA. We will […]
Parallelware Analyzer 0.16 released
We are pleased to announce the availability of Parallelware Analyzer 0.16. This release features a redesigned pwloops tool and new checks to improve the detection of data races and GPU data movement issues: Redesign of pwloops to better match the parallelization workflow: first, make sure that the code is analyzable; second, see what computation patterns […]