Category Archives: .NET

.NET, Accelerator, Features, HPC, ILNumerics, Numerical Algorithms, Usage

Introducing ILNumerics.ONAL – The Open Numerical Algorithm Language for .NET

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Introducing ILNumerics.ONAL

We are happy to announce the public availability of ILNumerics.ONAL, the open-source reference implementation of the ONAL (Open Numerical Algorithm Language) standard for .NET.

ONAL defines a standardized numerical array language for .NET, designed to provide MATLAB- and NumPy-compatible expressiveness while remaining stable, vendor-neutral, and compiler-friendly.

The source code is available on GitHub, and NuGet packages are published on nuget.org.

Technical Highlights

ILNumerics.ONAL provides a complete, production-ready environment for numerical computing in C# and Visual Basic. The project is derived from the commercial ILNumerics technology stack, which has been used successfully in thousands of industrial and academic applications for more than a decade.

The ONAL language library includes:

  • multidimensional dense arrays
  • MATLAB- and NumPy-compatible array semantics
  • broadcasting and advanced indexing
  • mutable arrays with thread-safe semantics
  • complex numbers as first-class numeric types
  • cells and heterogeneous containers
  • integration-friendly native buffer storage
  • linear algebra and FFT functionality based on robust LAPACK implementations automatically translated from the official Netlib FORTRAN sources
  • a comprehensive set of mathematical, elementary, and trigonometric functions
  • Visual Studio debugger integration, including array visualizers for convenient inspection of numerical data

The implementation focuses deliberately on correctness, robustness, and numerical language semantics. Performance-related APIs and execution details are intentionally kept out of the programming model.

The code base is accompanied by a comprehensive suite of roughly 2000 unit tests.

Open Source and Why It Matters

We decided to open-source the ONAL language core in 2026 in response to a problem we have observed for many years across both industry and research: the uncontrolled growth of custom in-house math libraries.

In the absence of a comprehensive, vendor-neutral numerical foundation for .NET, many organizations ended up building and maintaining their own array and math infrastructures. The result was often duplicated effort, fragmented semantics, difficult maintenance, compatibility issues, and long-term dependency risks.

Too many of these libraries eventually accumulated quirky APIs, legacy design constraints, performance-driven low-level details leaking into user code, poor maintainability—and, ironically, disappointing performance.

ILNumerics.ONAL aims to provide a stable and open alternative: a standardized numerical language foundation that remains fully usable without proprietary execution engines or vendor-specific runtimes.

Users keep full ownership of their algorithms and numerical IP, while benefiting from a mature and actively maintained implementation.

The Larger ILNumerics Ecosystem

ILNumerics.ONAL represents the core language layer of the broader ILNumerics ecosystem.

Algorithms written against the ONAL standard execute directly on .NET with reliable baseline performance. When higher performance is required, ONAL-compatible compilers—such as the ILNumerics Accelerator Compiler—can transparently apply advanced optimization techniques including:

  • automatic parallelization
  • graph optimization
  • array pipelining
  • SIMD vectorization
  • heterogeneous CPU/GPU scheduling
  • kernel fusion

ILNumerics.Computing serves as a fully compatible high-performance execution layer for ONAL-based algorithms. Existing code can often be accelerated simply by changing a package reference, without rewriting algorithms or introducing parallelization-specific code.

In practice, this frequently outperforms weeks or months of manual optimization work involving threading, vectorization, memory management, and hardware tuning.

At the same time, projects gain access to the broader ILNumerics ecosystem, including advanced visualization systems, charting components, debugger tooling, and domain-specific extensions.

This separation allows the numerical language itself to remain stable, open, and vendor-neutral, while execution technology and tooling continue to evolve independently.

In many ways, ILNumerics.ONAL can be seen as the semantic core of ILNumerics.Computing, separated from all performance-induced API complexity and execution-specific implementation details. It provides a clean and stable foundation for numerical algorithms today, while preserving a straightforward path toward high-performance execution whenever needed.

Make sure to check out the project and get involved. Contributions, discussions, and feedback are very welcome.

GitHub: https://github.com/ilnumerics/ILNumerics.ONAL
Nuget: https://www.nuget.org/packages/ILNumerics.ONAL

 

 

.NET, Features, ILNumerics, Scientific Computing

ILNumerics: High Performance as Default

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ILNumerics releases Version 7.0

The ILNumerics team is proud and excited to announce the availability of a new major release of its ILNumerics Ultimate VS product line. This milestone represents a significant advancement in compiler technology: it introduces the first autonomously parallelizing array compiler, ILNumerics Accelerator.

Array codes, such as numpy and Matlab, are popular within the scientific community. They simplify the description of complex numerical algorithms. However, when these numerical codes are implemented into industrial-grade products, distributed to customers, and maintained by a larger development team for many years, existing tools reveal considerable limitations, with execution speed being possibly the greatest limitation.

ILNumerics Accelerator is here to resolve this issue once and for all. Built on the convenient language of ILNumerics Computing Engine for authoring scientific array codes directly in C# /.NET, our intelligent Accelerator compiler reformulates array codes and executes them in the most efficient manner, unguided and autonomously.

Short Dive: Building an Array Compiler …

The key to optimal hardware utilization today is, of course, the ability to efficiently parallelize the workload. Therefore, ILNumerics Accelerator compiler automatically detects and utilizes parallel potential within subsequent array instructions of your algorithm.

The prevalent approach to automatic parallelization today involves analyzing your code to detect instructions that can execute in parallel. The code is then rewritten to allow these independent instructions to run concurrently. This approach originated at a time when C/C++ and FORTRAN were considered ‘high-level’ languages. While these languages offer some basic array support, numerical algorithms written in them inherently deal with scalar data.

In contrast, the algorithms we handle use n-dimensional arrays as the fundamental data type. When striving for real efficiency, the arrays’ complexity prohibits any compile-time decision about data independence, a suitable execution device, or a low-level implementation using vector instructions, among other factors. Too many variables determine the optimal execution strategy, and their variation is too extensive to identify at development time.

… with a fresh Approach to Parallelization …

ILNumerics takes a data-driven approach to parallelization, making all crucial decisions ‘just in time’ at runtime. This method enables us to implement the low-level internals of array instructions efficiently and eliminates the need for expensive dependency analysis of large program segments. It’s easier to determine at runtime whether an instruction relies on other data and when that data will be available.

However, this increases the compiler’s complexity significantly. Instead of producing fixed code for an array instruction found in the original program, we now generate an intermediate program, often representing several subsequent array instructions. This intermediate program is then transformed at runtime to reflect the original instructions in a manner that respects all influencing factors and enables optimal efficiency.

… with Intelligence and Freedom

These programs all run asynchronously, scheduling themselves onto suitable devices for execution at runtime. They connect with previous and subsequent asynchronous neighbors on the fly, even as they progress in calculating results. They autonomously determine the optimal implementation on the device they select. Significant decision-making power comes with immense responsibility. The final program code builds up asynchronously and autonomously at runtime.

Executing the same code branch twice may result in very different machine instructions. As the creators of the compiler, we can’t predict many details of the final implementation. We can’t even forecast the actual number of asynchronous programs, which we refer to as segments, executing concurrently. The decision now rests with the computer. It has a better understanding of the situation than we do and can process information and make educated decisions much faster and more effectively.

Why another Beta, then?

The first autonomous array compiler has arrived. Are you eager to try it out? Great! Go ahead and test the current beta from nuget.org. Its performance is genuinely impressive.

The majority of our users are building industrial products and have chosen ILNumerics for its reliability and seamless integration into .NET (which means all of .NET, since 2005). The compiler will become a regular part of the ILNumerics Computing Engine once it’s ready. Currently, it’s like a young child, learning new things each day. It’s learning to walk and talk, occasionally stumbling over previously unknown obstacles. Sometimes, it even surprises its creators with unexpected efficiency.

With recent advancements, the Accelerator compiler is now set to ‘always on’ mode. This marks a significant milestone in the development of a compiler. It’s now trusted to be stable and flexible enough to handle all codes, even unseen ones, ensuring it produces correct results and serves its primary purpose: to enhance efficiency.

The compiler will continue to develop until the final release. It will become more stable and gain more real-world exposure. We would greatly appreciate if your feedback contributes to its maturity by then.

All previous modules of ILNumerics have been adapted for the new Accelerator. They are thoroughly tested, have undergone many bugfixes and improvements, and are available for production on nuget.org as of today.

 

Introducing the ILNumerics Accelerator:

[ Video does not show? Download here: https://ilnumerics.net/media/andere/ILNumerics_Segments_2022.mp4 ]

.NET, C#, Interesting/ useless

Fun with Visual Studio regexp search

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I only recently realized that the Visual Studio regexp feature in Search & Replace is even able to handle regexp captures. Example: In order to locate and replace all line endings in your code which exist at the end of non-empty lines, exluding lines which end at ‘/’ or with other non-alphanumeric characters one can use:

Search Pattern: ([0-9a-zA-Z)])\r\n
Replace: $1;\r\n

searchandreplacevisualstudioMatches inside () parenthesis are captured. When it comes to replacing the match the new value is created by first inserting the captured value at the position marked by ‘$1′. This way it it possible to, let’s say insert a new char in the middle of a search pattern – if it fulfills certain conditions.

There appears to be an error in the MSDN documentation, unfortunately at the point describing how to reference captures in Visual Studio regexp replace patterns. Anyway, using the ‘$’ char in conjunction with the index is the common way and it works here just fine.

Multiple captures are possible as well. Just refer to the captured content by the index according to the count of ‘()’ captures in the search pattern.

The Visual Studio regexp support is really helpful when translating huge C header files to C# – to name only one situations. It saved me a huge amount of time already. Hope you find this useful too.

.NET, C#, ILNumerics, Matlab

HDF5 and Matlab Files – Fun with ILNumerics

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Why to use HDF5 and ILNumerics?

HDF5 is a file format (Hierarchical Data Format) especially desgined to handle huge amount of numerical data. Just to mention an example,  NASA chose it to be the standard file format for storing data from the Earth Observing System (EOS).

ILNumerics easily handles HDF5 files. They can be used to exchange data with other software tools, for example Matlab mat files. In this post I will show a step by step guide – how to interface ILNumerics with Matlab.

Continue reading HDF5 and Matlab Files – Fun with ILNumerics →

.NET, C#, Features, ILNumerics, Interesting/ useless, Scientific Computing, Visualization

ILNumerics for Scientists – Going 3D

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Recap

Last time I started with one of the easiest problems in quantum mechanics: the particle in a box. This time I’ll add 1 dimension and we’ll see a particle in a 2D box. To visualize its wave function and density we need 3D surface plots.

2D Box

This time we have a particle that is confined in a 2D box. The potential within the box is zero and outside the box infinity. Again the solution is well-known and can be found on Wikipedia. This time the state of the wave function is determined by two numbers. These are typically called quantum numbers and refer to the X and the Y direction, respectively.

The absolute size of the box doesn’t really matter and we didn’t worry about it in the 1D case. However, the relative size of the length and the width make a difference. The solution to our problem reads

$\Psi_{n,k}(x,y) = \sqrt{\frac{4}{L_x L_y}} \cdot \sin(n \cdot \pi \cdot x / L_x) \cdot \sin(k \cdot \pi \cdot y / L_y)$

The Math

Very similar to the 1D case I quickly coded the wave function and the density for further plotting. I had to make sure that the arrays are fit for 3D plotting, so the code looks a little bit different compared to last post’s

     public static ILArray<double> CalcWF(int EVXID, int EVYID, double LX, double LY, int MeshSize)
     {
        ILArray<double> X = linspace<double>(0, LX, MeshSize);
        ILArray<double> Y = linspace<double>(0, LY, MeshSize);

        ILArray<double> Y2d = 1;
        ILArray<double> X2d = meshgrid(X, Y, Y2d);

        ILArray<double> Z = sqrt(4.0 / LX / LY) * sin(EVXID * pi * X2d / LX) * sin(EVYID * pi * Y2d / LY);

        return Z.Concat(X2d,2).Concat(Y2d,2);
     }

Again, this took me like 10 minutes and I was done.

The Visualization

This time the user can choose the quantum numbers for X and Y direction, the ratio between the length and the width of the box and also the number of mesh points along each axis for plotting. This makes the visualization panel a little bit more involved. Nevertheless, it’s still rather simple and easy to use. This time it took me only 45 minutes – I guess I learned a lot from last time.

The result

Here is the result of my little program. You can click and play with it. If you’re interested, you can download the Particle2DBox source code. Have fun!

Particle2DBoxThis is a screenshot of the application. I chose the second quantum number along the x axis and the fourth quantum number along the y axis. The box is twice as long in y direction as it is in x direction. The mesh size is 100 in each direction. On the left hand side you see the wave function and on the right hand side the probability density.

.NET, C#, ILNumerics, Scientific Computing

ILNumerics for Scientists – An easy start

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Motivation

I’ve been working as a scientist at universities for 10 years before deciding to go into industry. The one thing I hated most was coding. At the end of the day coding for scientists is like running for a football player. Obviously, you need it but it’s not what you’re here for.

I really dreaded the coding and the debugging. So much precious time for something that was so clear on paper and I just wanted the solution of my equations to see whether my idea made sense or not. More often than not scientists find that their idea was not so great and now they had spent so much time coding just to find out that the idea didn’t work. Continue reading ILNumerics for Scientists – An easy start →

.NET, Features, ILNumerics, Usage, Visualization

Fun with HDF5, ILNumerics and Excel

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It is amazing how many complex business processes in major industries today are supported by a tool that shines by its simplicity: Microsoft Excel. ‘Recently’ (with Visual Studio 2010) Microsoft managed to polish the development tools for all Office applications significantly. The whole Office product line is now ready to serve as a convenient, flexible base framework for stunning custom business logic, custom computations and visualizations – with just a little help of tools like ILNumerics.

ExcelWorkbook1In this blog post I am going to show how easy it is to extend the common functionality of Excel. We will enable an Excel Workbook to load arbitrary HDF5 data files, inspect the content of such files and show the data as interactive 2D or 3D plots. Continue reading Fun with HDF5, ILNumerics and Excel →

.NET, ILNumerics, Usage

Performance on ILArray

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Having a convenient data structure like ILArray<T> brings many advantages when handling numerical data in your algorithms. On the convenience side, there are flexible options for creating subarrays, altering existing data (i.e. lengthening or shortening individual dimensions on the run), keeping dimensionality information together with the data, and last but not least: being able to formulate an algorithm by concentrating on the math rather than on loops and the like.

Convenience and Speed

Another advantage is performance: by writing C = A + B, with A and B being large arrays, the inner implementation is able to choose the most efficient way of evaluating this expression. Here is, what ILNumerics internally does: Continue reading Performance on ILArray →

.NET, ILNumerics, Usage, Visualization

Plotting Fun with ILNumerics and IronPython

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Since the early days of IronPython, I keep shifting one bullet point down on my ToDo list:

* Evaluate options to use ILNumerics from IronPython

Several years ago there has been some attempts from ILNumerics users who successfully utilized ILNumerics from within IronPython. But despite our fascination for these attempts, we were not able to catch up and deeply evaluate all options for joining both projects. Years went by and Microsoft has dropped support for IronPython in the meantime. Nevertheless, a considerably large community seems to be active on IronPython. Finally, today is the day I am going to give this a first quick shot.

Continue reading Plotting Fun with ILNumerics and IronPython →