DracoLIX

Draconis Scientific Computing & High-Performance Numerical Computing

A native, multi-language scientific computing stack built from scratch around performance, reliability and numerical control.

DracoLIX is an open-source scientific computing project developed by Draconis Engineering.

The goal is to build a high-performance numerical computing stack spanning linear algebra, numerical methods, scientific simulation and HPC, with a particular focus on giving the developer direct control over data types, memory layout and computation.

DracoLIX is designed to be independent of NumPy and similar numerical runtimes. Instead of wrapping an existing numerical backend, DracoLIX aims to provide its own array types, dtype system, memory model and computational kernels.

The project is currently in an early stage. At present, the implementation is focused primarily on matrix operations, but the architecture is being developed with a much larger scientific computing ecosystem in mind.

---

Vision

Scientific computing is built on numerical primitives.

Matrices, vectors, tensors, numerical solvers and differential equations form the foundation of everything from engineering simulations to data analysis and computational physics.

DracoLIX aims to provide that foundation for the Draconis Engineering ecosystem.

The long-term vision is a platform capable of powering:

• Linear algebra
• Numerical analysis
• Scientific simulation
• Optimization
• Differential equations
• CFD
• Engineering computation
• High-performance data analysis
• HPC workloads

The core principle is simple:

Build the numerical infrastructure instead of depending on it.

---

Architecture

DracoLIX is designed as a multi-language system where each language has a specific role.

                         DracoLIX
                            │
                 ┌──────────┴──────────┐
                 ▼                     ▼
            Public APIs            Core Runtime
                 │                     │
        ┌────────┼────────┐            │
        │        │        │            │
     Python    Julia    (C++)          │
        │        │        │            │
        └────────┴────────┴─┬──────────┘
                            ▼
                  DracoLIX C++ Core
                            │
              ┌─────────────┼─────────────┐
              │             │             │
           Arrays        DTypes        Memory
              │             │             │
              └─────────────┼─────────────┘
                            ▼
                     Compute Runtime
                            │
              ┌─────────────┴─────────────┐
              │                           │
        C++ Kernels           Optional Fortran Kernels
              │                           │
              └─────────────┬─────────────┘
                            ▼
                       CPU / SIMD

C++

C++ forms the foundation of DracoLIX.

It is responsible for:

• Core data structures
• Memory management
• Array implementations
• DType system
• Runtime and dispatch
• Parallel execution
• SIMD-oriented computation
• Language bindings
• Performance-critical kernels

Fortran

Fortran is an optional HPC backend. Enabled via -DDRACOLIX_USE_FORTRAN=ON at build time; off by default.

It is intended for:

• Numerical kernels
• Scientific algorithms
• Linear algebra
• Numerical solvers
• Future HPC functionality

Python

Python is one of the primary high-level interfaces to DracoLIX.

The Python API is intended to make high-performance numerical computation accessible without requiring users to write Rust or Fortran.

Python bindings are implemented directly against the DracoLIX core rather than relying on NumPy as an underlying execution engine.

import dracolix as dlx

A = dlx.matrix(
    [[1.0, 2.0],
     [3.0, 4.0]],
    dtype=dlx.f64
)

B = dlx.matrix(
    [[5.0, 6.0],
     [7.0, 8.0]],
    dtype=dlx.f64
)

C = A @ B

Julia

Julia is planned as a first-class scientific computing interface.

Julia's strengths in numerical programming make it particularly suitable for:

• Scientific research
• Mathematical experimentation
• Numerical methods
• Differential equations
• Optimization
• Simulation

---

Native DTypes

One of the core goals of DracoLIX is to provide its own numerical type system.

DracoLIX should not require NumPy's dtype system to represent numerical data.

Instead:

dlx.f32
dlx.f64
dlx.i32
dlx.i64
dlx.c64
dlx.c128

...would represent native DracoLIX types.

Conceptually:

                  DType
                    │
        ┌───────────┼───────────┐
        ▼           ▼           ▼
     Integer      Float      Complex
        │           │           │
     i8/i16/...   f32/f64    c64/c128

The DType system is intended to become part of the DracoLIX runtime itself.

This allows the runtime to make decisions based on:

• Element size
• Alignment
• Numerical representation
• SIMD compatibility
• Kernel availability
• Memory requirements
• Future accelerator support

The goal is to build a numerical type system designed around DracoLIX's own execution model.

---

Native Arrays

DracoLIX will provide its own numerical data structures.

dlx.Array
dlx.Vector
dlx.Matrix
dlx.Tensor

These structures will own and manage their underlying memory through the DracoLIX runtime.

A conceptual array representation might contain:

Array<T>
 ├── data
 ├── shape
 ├── strides
 ├── dtype
 └── layout

This gives DracoLIX control over important performance characteristics such as:

• Contiguous memory
• Strided views
• Row-major and column-major layouts
• Memory alignment
• Cache efficiency
• SIMD compatibility
• Zero-copy views
• Slicing
• Parallel access

This is a deliberate design choice.

DracoLIX should not simply become:

Python
  ↓
NumPy
  ↓
  C
  ↓
Rust

Instead:

Python
  ↓
nanobind
  ↓
DracoLIX Core
  ↓
Native Kernels
  ↓
CPU / SIMD / HPC

---

Computational Model

The long-term architecture separates what is being calculated from how it is calculated.

For example, a high-level operation might request:

C = A @ B

The runtime can then determine the appropriate implementation based on:

• DType
• Matrix dimensions
• Memory layout
• Hardware capabilities
• Available kernels
• Parallel execution options

Conceptually:

                 Matrix Multiplication
                          │
                          ▼
                    Kernel Dispatch
                          │
              ┌───────────┼───────────┐
              ▼           ▼           ▼
            Scalar      SIMD      Parallel
              │           │           │
              └───────────┼───────────┘
                          ▼
                     Native Code

This dispatch architecture is intended to become increasingly important as DracoLIX grows beyond basic linear algebra.

---

Planned Scientific Computing Stack

DracoLIX is intended to grow incrementally.

1. Core

• Native DTypes
• Arrays
• Vectors
• Matrices
• Tensors
• Memory management
• Views and slicing
• Basic arithmetic

2. Linear Algebra

• Matrix multiplication
• Vector operations
• Decompositions
• Linear system solvers
• Eigenvalue problems
• Sparse matrices
• Advanced matrix algorithms

3. Numerical Computing

• Numerical integration
• Interpolation
• Optimization
• Root finding
• ODE solvers
• PDE solvers
• Numerical differentiation

4. Scientific Computing

• Scientific simulation
• Computational physics
• Engineering workloads
• CFD
• Large-scale numerical analysis

5. HPC

Future research areas may include:

• Advanced SIMD
• Multithreading
• GPU acceleration
• Distributed computation
• MPI
• Domain decomposition
• Large-scale simulation

These features are long-term goals, not current capabilities.

---

Draconis Ecosystem

DracoLIX is intended to become the numerical computation backbone of Draconis Engineering.

                     Draconis
                         │
              ┌──────────┼──────────┐
              │          │          │
           Olympus     DuraPy     ICARUS
              │          │          │
              └──────────┼──────────┘
                         │
                     DracoLIX
                         │
              Numerical Computation

Olympus

Olympus Analytics Engine will be one of the first major consumers of DracoLIX.

As Olympus processes increasingly large quantities of training and physiological data, DracoLIX can provide the numerical foundation for:

• Time-series analysis
• Statistical calculations
• Signal processing
• Rolling computations
• Training-load calculations
• Numerical models
• Large-scale historical analysis

Olympus understands the domain.

DracoLIX handles the mathematics.

DuraPy

DuraPy can use DracoLIX as its numerical backend for high-performance scientific and endurance-sports computation.

---

Design Principles

From the ground-up

DracoLIX should own its core numerical representation. We do not just simply wrap another numerical library.

Speed

Performance matters.

Memory layout, cache behavior, SIMD, parallelism and algorithmic complexity should be considered fundamental parts of the architecture.

Reliability

Numerical software must be predictable.

Correctness, testing, numerical stability and deterministic behavior are core concerns.

Multi-language

Different languages are good at different things.

DracoLIX embraces Python, Julia, Rust and Fortran instead of forcing everything into one language.

Open Source

DracoLIX is fully open source.

The goal is to build a transparent numerical computing stack that can be studied, modified and extended by others.

Modular Architecture

DracoLIX should consist of smaller components rather than becoming one enormous monolithic library.

dracolix-core
dracolix-array
dracolix-linalg
dracolix-solver
dracolix-runtime
dracolix-python
dracolix-julia
...

The exact crate/package structure may evolve as the project develops.

---

Current Status

DracoLIX is experimental and heavily under development.

Current functionality is limited compared to the long-term vision.

At the moment, the project primarily contains early implementations of numerical operations such as matrix multiplication.

The architecture and roadmap described above represent the direction of the project, not a claim that all of these capabilities currently exist.

---

Roadmap

• Project foundation
• C++ numerical core
• Initial Python interface (nanobind binding)
• Matrix multiplication prototype
• Native DType system
• Native Array / Matrix types
• Memory layout system
• Better benchmarking infrastructure (backend matrix: scalar / AVX2 / AVX-512 / OpenBLAS, f64 + f32)
• SIMD kernels (register-blocked AVX-512 and AVX2, runtime-dispatched)
• Parallel execution (thread pool, row-panel-parallel GEMM, parallel reductions)
• Expanded linear algebra (matmul, matvec, dot, norms, diag, batched)
• Sparse matrices (CSR/CSC, sparse matmul, CG)
• Numerical solvers (LU, QR, Cholesky, eigen, SVD)
• Julia interface (C ABI + ccall)
• ODE/PDE tooling (ODE solvers done, PDE not started)
• Optimization
• Scientific simulation
• CFD
• GPU acceleration
• Distributed HPC

The roadmap is intentionally ambitious.

Not every feature is guaranteed to be implemented, and priorities may change as the project evolves.

---

Philosophy

DracoLIX started as a simple linear algebra library.

The long-term goal is considerably larger.

Instead of building another abstraction layer around existing numerical software, DracoLIX aims to explore what a modern, native, multi-language scientific computing stack could look like when the numerical runtime itself is designed from the ground up.

Own the data. Own the types. Own the computation.

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License

DracoLIX is licensed under the GPLv3+ license. It is open source and intended to remain freely available to the community.

See the repository license for details.