Scalable, Parallelizable Implementation
A key benefit of TechFinity’s approach to algorithm development is the utilization of architectures and computational models that support scalable and parallelizable implementations. This is particularly useful in the decision support and resource allocation domain where a large number of options need to be analyzed or optimized simultaneously. TechFinity uses a combination of parallelizable algorithm architectures with probabilistic methods to generate solution approaches that are scalable.
Real-Time Computational Performance
For real-time applications with computationally intensive analysis and resource allocation requirements, TechFinity has developed a number of probabilistic methods that utilize a fast computational engine and can be parallelized to support real-time performance requirements.
In product tests these algorithmic approaches have demonstrated slightly slower performance than well-known, non-optimal greedy algorithms which provide the fastest performance, yet still produce near-optimal results.
The standards-based software architecture and modular framework combine to provide a product platform that is robust and easily maintained. Extensive testing of the software framework and algorithm performance under stressing scenarios has proven the durability of the algorithmic approaches and software methods.