Scientific coding
We assist clients in crafting advanced applications for predictive analytics, simulations, and complex calculations. Our unique value lies in merging mathematical expertise with proficiency in multiple software languages like Python, Julia, MATLAB, and C++.
Software development.
We frequently join projects at the ideation stage, offering assistance with specifications and mathematical evaluations. Early engagement enables us to test designs in proofs-of-concept, focusing on the most challenging aspects. This approach ensures high-performance solutions and maintainability.
We keep you closely involved and prioritize leveraging the expertise of your specialists, aiming for your autonomy in confidently owning the code. Our focus is on building long-term relationships based on value and appreciation, not dependencies. Partner with us to develop your computational solutions on time and within budget. With proven scientific coding experience since 2008, we have served various industries, including:
High-tech, Automotive, Aerospace
Semicon, Automotive, Machinery, Mechatronics
Finance, Insurance, Utilities
Banking, Insurance, Regulatory
Energy, Maritime, Offshore
Shipping, Dredging, MetOcean, Windfarms, Oil & gas
Expert knowledge.
Our developers are distinguished by their scientific backgrounds. We understand abstractions, complex analysis, and computational challenges. Our software skills enable us to translate concepts into efficient software solutions.
Math and languages
Crafting scientific software is an art. Our developers excel in diverse mathematical algorithms and their implementations, including some we have crafted ourselves. We regularly assess new methodologies and languages like Julia, which is gaining traction in scientific software and beyond.
Performance-aware programming
Naturally, implementations often begin by ensuring all computations function as intended. Initially, small and idealized data samples are used to achieve initial results. Later, real data is introduced, revealing performance and memory issues. We have mastered performance-aware programming to optimize resource usage from the outset.
Best practices
Expertly coded apps are high-performing and easily maintainable, learned through experience. We ensure quality with meticulous designs and coding standards, ensuring consistency for developers. Our MATLAB-specific code checker (CC4M) streamlines work and ensures compliance.
Computational optimization.
We analyze your code for time-consuming areas and memory spikes, using standard performance tools for quick wins. With extensive experience across various languages, we offer rapid insights into challenges and opportunities.
Reducing memory loading
Trimming data and selecting optimal structures reduce memory loading; where size and shape are both critical considerations. Targeted algorithm refinements further minimize memory usage. Leveraging diverse experience, we optimize memory for various computational clusters and embedded applications.
Improving performance
Thoroughly revisiting algorithms, math representations, and data loading yields substantial performance gains. We prioritize standardized and optimized math libraries over less efficient native implementations. Assessing underlying hardware mitigates limitations and capitalizes on opportunities, optimizing overall performance.
Parallel computing
Once algorithms are optimized, workload distribution is assessed. Parallel computing is a powerful option to reduce runtime, still increasingly so, as processors are still becoming progressively efficient in matrix operations and multicore task handling. Together, we identify the optimal parallel computation strategy.
GPU | CUDA Programming
With superior parallel computation, GPUs, initially for graphics, now enhance scientific calculations. Unlike CPUs, GPUs offer vast arrays of processors and high-speed memory. We aid in harnessing GPU and CUDA programming for performance enhancement.ns of GPU and CUDA programming.
Numerical benchmarking
In software optimization, preserving numerical validity and core functionalities is paramount, necessitating continuous testing and domain understanding. Proper comprehension of computational core purposes is essential. Collaboration is key; we work with your specialists to optimize code confidently.