Scheduling works 3 ways: RS
Smart compute shaders with ML optimising sort order:
Sort = (Variable Storage (4Kb to 64Kb & up to 4Mb, AMD Having a 64Bit data ram per SiMD Line)
Being ideal for a single unit SimV SimD/T & data collation & data optimisation,
With memory Action & Location list (Variable table),
Time to compute estimator & Prefetch activity parser & optimiser with sorted workload time list..
Workloads are then sorted into estimated spaces in the Compute load list & RUN.
CPU & GPU & FPU Anticipatory scheduler with ML optimising sort order:
Sort = (Variable Storage (4Kb to 64Kb & up to 4Mb, AMD Having a 64Bit data ram per SiMD Line)
Being ideal for a single unit SimV SimD/T & data collation & data optimisation,
With memory Action & Location list (Variable table),
Time to compute estimator & Prefetch activity parser & optimiser with sorted workload time list..
Workloads are then sorted into estimated spaces in the Compute load list & RUN.
Open CL, SysCL cache streamlined fragment optimiser with ML optimising sort order:
Sort = (Variable Storage (4Kb to 64Kb & up to 4Mb, AMD Having a 64Bit data ram per SiMD Line)
Being ideal for a single unit SimV SimD/T & data collation & data optimisation,
With memory Action & Location list (Variable table),
Time to compute estimator & Prefetch activity parser & optimiser with sorted workload time list..
Workloads are then sorted into estimated spaces in the Compute load list & RUN.
(c) Rupert S
ORO-DL : Objective Raytrace Optimised Dynamic Load & Machine Learning : RS
Simply places raytracing in the potent hands of powerful CPU & GPU Features from the 280X & GTX 1050 towards newer hardware.. While reducing strain for overworked GPU/CPU Combinations..
Potentially improving the PS4+ and XBox One + & Windows & Linux based source such as Firefox and chrome
Creating potential for SiMD & Vectored AVX/FPU Solutions with intrinsic ML.
This solution is also viable for complex tasks such as:
3D features, 3D Sound & processing strategy.
Networking,Video & other tasks you can vector:
(Plan,Map,Work out,Algebra,Maths,Sort & compare,examine & Compute/Optimise/Anticipate)
(Machine Learning needs strategy)
Primary Resources of Objective Raytrace:
Resource assets CPU & GPU FPU’s precision 8Bit, 16Bit, 32bit + Up to capacity,
Mathematical Raytrace with a priority of speed & beauty first,
HDR second (can be virtual (Dithered to 10Bit for example) AVX & SiMD
(Obviously GPU SiMD are important for scene render MESH & VRS so CPU for both FPU & Less utilised AVX SSSE2 is advisable)
Block render is the proposed format, The strategy optimises load times at reduced IRQ & DMA access times..
Reducing RAM fragmentation & increasing performance of DMA transferred work loads.
Block Render DMA Load; OptimusList:
64KB up to 64MB block DMA requested to the float buffer in the GPU for implementation in the vertice pipeline..
Under the proposal the Game dynamic stack renders blocks in development testing that fit within the requirements of the game engine,
Priority list DMA buffer 4MB 16MB 32MB 64MB
The total block of Ray traced content & Audio, Haptic, Delusional & Dreamy Simulated,
SiMD Shader content that fits within the recommended pre render frame limit of 3 to 7 frames..
1 to 7 available & Ideally between 3 & 5 frames to avoid DMA,RAM & Cache thrashing..
and Data load.
As observed in earlier periods such as AMIGA the observable vector function of the CPU is not so great for texturing, However advancements and necessity allow this.
SiMD Shader emulation allows all supported potential and in the case of some GPU..AVX2, AVX 256/512 & dynamic cull…
The potency is limitless especially with Dynamic shared AMD SVM,FP4/8/DOT Optimised stack.
Background content & scenes can be pre rendered or dynamically (Especially with small details)..
In terms of tessellation & RayTrace & other vital SiMD Vector computation without affecting the main scene being directly rendered in the GPU..
Only enhancing the GPU’s & CPU’s potential to fully realise the scenes.
Fast Vector Cache DMA.
So what is the core logic ?
CPU Pre frame RayTrace is where you render the scene details: Mode
Plan to use 50% of processor pre frame & timed post boot & in Optimise Mode :RS :
50% can be dynamic content fusion.
Integer(for up to 64Bit or Virtual Float 32Bit.32Bit)(Lots of Integer on CPU so never underestimate this),Vector,AVX,SiMD,FPU processed logic ML
The Majority of the RayTrace CPU does can be static/Slow Dynamic & Pre planned content.
(Pre planned? 30 Seconds of forward play on tracks & in scene)
Content with static lights & ordered shift/Planned does not have to be 100% processed in the GPU.
To be clear CPU/GPU planned content can be transferred as Tessellated content 3D Polygons or as Pre Optimised Lower resolution Float maths & shaders.
Potential usage include:
3D VR Live Streaming & movies : RS
With logical arithmetic & Machine learning optimisations customised for speed & performance & obviously with GPU also.
We can do estimates of the room size and the dimensions and shape of all streaming performers & provide 3D VR for all video rooms in HDR 3DVR..
The potential code must do the scene estimate first to calculate the quick data in later frames.
Later in the scene only variables from object motion & a full 360degree spin would do most of the differentiation we need for our works of action & motion in 3D Render.
The potential is real, For when we have real objective, dimensions & objects? We have real 3D.
The solution is the mathematics of logic.
3D VR Haptic & learn
Conceptually the relevance of mapping haptic frequency response is the same parameter as in ear representative 3D sound.
For a start the concept of an entirely 3D environment does take the concept of 2D rendering the 3D world & play with your mind.
Substantially deep vibration is conceptually higher & intense pulse is thus deeper,
However the concept is also related to the hardness of earth & sky or skin.
Ear frequency response mapping is a reflection of an infrared diode receptor & infrasound harmonic 3D interpretation, Such as Sonar & Radar.
(c)RS
All this can be ours : Witcher 3 Example Video : https://www.youtube.com/watch?v=Mjq-ZYK7oJ8
https://science.n-helix.com/2019/06/vulkan-stack.html
https://science.n-helix.com/2014/08/turning-classic-film-into-3d-footage-crs.html
https://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html
https://science.n-helix.com/2020/01/float-hlsl-spir-v-compiler-role.html
https://science.n-helix.com/2020/02/fpu-double-precision.html
Raytracing potent compute research’s:
https://bitshifter.github.io/2018/06/04/simd-path-tracing/
Realtime Ray Tracing on current CPU Architectures : https://pdfs.semanticscholar.org/7449/2c2adb30f1ea25eb374839f3f64f9a32b6c7.pdf
RayTracing Vector Cell : https://www.sci.utah.edu/publications/benthin06/cell.pdf
https://aras-p.info/blog/2018/04/10/Daily-Pathtracer-Part-7-Initial-SIMD/
https://aras-p.info/blog/2018/11/16/Pathtracer-17-WebAssembly/
Demo WebAssem: NoSiMD: SiMD & AVX Proof of importance
https://aras-p.info/files/toypathtracer/
(c)RS
(c)Rupert S https://science.n-helix.com
Update confirmed:
Nvidia even ray-traced the 980! in Vulkan … Works on AMD,Quadcom, Android, NVidia and PowerVR..
The potential exists for all,
Powerful CPU’s & GPU’s make all possible #TraceThatCompute2020 .
