Faster development of better parallel software with Parallelware
Unlock the performance of modern heterogeneous hardware, no matter whether your code runs on a supercomputer, laptop or embedded device. Take advantage of Appentra’s unique technology for static code analysis specialized in parallelism to develop better quality parallel software in less time.
Learn parallel programming faster and at your own pace
Learn parallelization concepts and techniques guided by parallel patterns used in real software. Use our integrated learning environment to experiment with different technologies for parallel programming. Make expert decisions for the development of multicore and GPU-accelerated software.
Quality Assurance and development tools for parallel code
Ensure the quality of your parallel code and speed up the development process to make the most out of modern heterogeneous hardware. Let Parallelware help you find software defects related to parallelism. Identify the best opportunities for parallelization, and implement parallel versions of your code, optimized for your specific heterogeneous platform.
Industries
Oil and Gas
Molecular simulation
Astrophysics
Climate Modeling
Automotive
Wireless communication
Embedded vision
Ed Tech
Case Studies
Enabling fast user selection in 5G networks through a GPUenabled maxvol algorithm
King Abdullah University of Science and Technology (KAUST)
Parallelizing a shallow-water flow model with variable pressure
EDANYA. UNIVERSITY OF MALAGA.
Testimonials
News & Events
Appentra winner of the 'Innovative Science' category at the Innovative Radar Prize 2020
The European Commission has awarded prizes to Europe's most promising [...]
Parallelware Analyzer 0.15 released
This release has the following new features: Memory access patterns reporting. Now pwloops provides information not only about [...]
Interprocedural analysis across source code files with Parallelware Analyzer
Programs are rarely built from a single source code file. Normally, code is organized into several[..]
Parallelware Trainer 1.5 released
We’ve happy to announce the release of Parallelware Trainer 1.5 which brings exciting new features to offload computations [...]
Horizon 2020 Projects
Contact our Team.