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 Analyzer, the first static code analyzer that is designed specifically to boost the performance of C/C++ code. Earlier generations of source code analysis are limited to bugs, coding standard enforcement or security, possibly even a combination of this functionality. While important, nothing is done to ensure that the code is written to take advantage of modern hardware capabilities offered by chip manufacturers in the low-power multicore processors. By using Paralleware Analyzer, leverage the benefits of parallelism and deliver significantly faster applications, in the order of magnitude of 3x-4x. Clearly, the benefits are huge to C/C++ applications in the scope of mission and business critical industries such as Aerospace, Automotive, Telecommunications, and Semiconductors.
Parallelware Analyzer is designed to focus on identifying insights for your code, get in-depth reports on its compute and memory requirements and even rewrite your code to introduce parallelizations. You can do so either through a native graphical user interface or from the command line, which allows for easy integration with other tools such as CI/CD systems.
With the assistance of Parallelware Analyzer you can increase the performance of your code. Our demonstrator benchmarks show how you can easily achieve up to 9x speedups for some classic computational examples! Additionally, Paralleware Analyzer performs extremely well for large codes such as GROMACS (340K lines of code across 2.5K files): it can successfully analyze 99.6% of its files, devoting an average of 3 seconds per file with an error rate as low as 0.39%.
Parallelware Analyzer raises the bar as the state of the art for performance optimization. Take for instance the case of vectorization: benchmarks show that up to 64% of loops can not be vectorized by current vectorizing compilers. Parallelware Analyzer provides human-readable actionable insights to help you overcome such limitations.
We will be happy to meet with you to show you a demo or reply to your questions!
- Parallelware Analyzer website
- Parallelware Analyzer flyer
- How to get started with Parallelware Analyzer: MATMUL example
- Parallelware Analyzer for large codes: GROMACS case study (one-page PDF)
- Performance gains for classic computational examples with Parallelware Analyzer
- How can Parallelware Analyzer advance the vectorization state-of-the-art?
Start boosting the performance of your code with Parallelware Analyzer