Matching architecture (64-bit if you're building for 64-bit systems)
Installed for all users
Added to PATH
Installed with debugging symbols and binaries
Setup
Setup config
./fips set config vulkan-win64-vs2019-debug in your project directory
Set environment variables
Run fips nebula verb to set work and toolkit directory registry variables:
fips nebula set work {PATH}
fips nebula set toolkit {PATH}
Build project
In your project directory:
fips fetch
fips physx build win-vs16 (if you are running VS 2017, use win-vs15 instead)
fips anyfx setup
fips build
fips physx deploy
Features
Nebula is being developed continuously, which means that features keep getting added all the time. Currently, we support this:
Completely data-driven design from bottom to top.
Data structure suite, from containers to OS wrappers, everything is designed for performance and minimal call stacks.
Multithreading.
SSE-accelerated and intuitive maths library.
Full python supported scripting layer.
Advanced rendering framework and shaders.
Test-benches and benchmarking.
Profiling tools.
Rendering
A lot of effort has been made to the Nebula rendering subsystem, where we currently support:
Unified clustering system - fog volumes, decals and lights all go into the same structure.
Screen-space reflections - working condition, but still work in progress.
SSAO - Horizon-based ambient occlusion done in compute.
Physically based materials and rendering.
Multi-threaded subpass recording.
Shadow mapping for local lights and CSM for global/directional/sun light.
Volumetric fog and lighting.
Geometric decals.
CPU-GPU hybrid particle system.
Skinning and animation.
Scripted rendering path.
Vulkan.
Tonemapping.
Asynchronous compute.
Virtual texturing using sparse binding.
Fast and conservative GPU memory allocation.
Entity system
Nebula has historically had a database-centric approach to entities.
With the newest iteration of Nebula, we've decided to keep improving by adopting an ECS approach, still keeping it database-centric.
Data-oriented
Data-driven
Minimal memory overhead per entity.
High performance without compromising usability or simplicity
Blueprint and template system for easily instantiating and categorizing entity types.
Automatic serialization and deserialization
Screenshots
Deferred Lighting using 3D clustering and GPU culling.
Geometric decals, culled on GPU and rendered in screen-space.
Volumetric fog lighting.
Local fog volumes.
Profiling tools.
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