Boost the performance of your code
Unlock the performance of modern multicore CPUs and GPUs through the first static code analyzer specializing in software performance.
Develop bug-free code ensuring best practices for safety and performance.
Detect and prevent bugs originated by data races and data movement issues, and get best-practice recommendations to develop faster software.
A new catalog of parallel programming
best practices and common errors
Enforce best practices in your code
Parallelware Analyzer based on the Parallelware Artificial Intelligence (AI) engine for static code analysis specializing in performance is the first tool supporting this innovative catalog by reporting race conditions, data movement issues and best-practice recommendations to create efficient and bug-free parallel code.
Ensure the quality of C/C++/Fortran parallel code according to best practices
Open catalog of defects and recommendations for parallel programming built in collaboration with experts in multicore and GPU programming to establish parallel programming best practices. Open set of curated example codes that clearly describe errors commonly seen in C/C++/Fortran parallel codes.
Capabilities of Parallelware Analyzer
Parallelware analyzer provide an innovative solution for the development of C/C++/Fortran parallel code targeting multicore CPUs and GPUs. Its the first static code analyzer specializing in performance and helps to accelerate the software run-time by reducing development effort through detection and generation of bug-free code.
Accelerate the software runtime through code performance techniques
Discover opportunities for performance in your code
Quickly design and implement parallel code for CPU/GPU.
Detect and fix defects such as data movement issues in the code.
Verify data-race free parallel code.
Enforce performance best practice recommendations in order to prepare the code for parallelization or optimize its performance.
A static code analyzer specializing in performance
Parallelware Analyzer helps developers create fast, bug free code in C/C++/Fortran, reporting providing them with feedback in the form of objective and measurable metrics and seamlessly integrating into their development workflow and CI/CD tool.
4 tips to avoid race conditions on GPUs
The 4 tips presented leverage parallel programming best practices, enabling to write parallel code as good as that written by experts in parallel programming for GPUs. They cover two typical data movement issues, one typical data race, and one recommendation to prevent introducing bugs in the parallel code inadvertently.