We are thrilled to announce the official launch of Parallelware Analyzer 1.0, the first static code analyzer specializing in performance. During the last 2 years, over 200 early adopters have helped us shape Parallelware Analyzer thanks to their invaluable feedback through the Early Access Program. Appentra as an innovation company provides a revolutionary approach: Parallelware […]
Technical
How can Parallelware Analyzer advance the vectorization state-of-the-art?
Parallelware Analyzer can help advance the state-of-the-art of performance optimization. One representative example is vectorization. Compilers normally try to automatically vectorize as many loops as possible. However, in many cases they encounter code that is either not suitable for vectorization or too complex for them to handle. In these scenarios, invoking Parallelware Analyzer will identify […]
Parallelware Analyzer for large codes: GROMACS case study
Static code analysis is complex and thus, static analysis tools often struggle for large codes. One frequent question we get asked regarding Parallelware Analyzer is how well it behaves when analyzing large code bases. In order to answer it we have chosen the GROMACS project containing more than 340 thousand lines of code across more […]
Performance gains for classic computational examples with Parallelware Analyzer
Appentra’s performance-demos GitHub repository showcases the performance gains when parallelizing code with the help of Parallelware Analyzer. The following classic computational codes have been parallelized using CPU multi-threading and GPU offloading with Parallelware Analyzer: ATMUX: sparse matrix-vector multiplication. CANNY: image edge detection. COULOMB: computation of the electric potential created by a set of charges in […]
Parallelware Analyzer and the OpenMP Common Core: an exercise with LULESHmk
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 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 […]
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 […]
Many ways to speed up your program
There are many approaches to make your program run faster. Some approaches are based on using more efficient libraries, others rely on using the standard library and the language in an efficient manner. Other approaches include using more efficient algorithms. But sometimes, even after we’ve applied all the possible optimizations, the code performance is not […]