Saturday, June 8, 2019

Website Development : 3D : Vulkan Stack for web page,data,science & gaming.

Website Development : 3D : Vulkan Stack for web page,data,science & gaming.

Console, Windows, Linux, Android, Mac, Smart Phone: OS & Kernel

Stack list:

HTML DOM (Document Object Model) >
PHP & Database >
JS - JQuery & JQuery 3D feature stack & jquery ui >
WebGL >
WebGL Compute &
Streamline API
Vulkan API - Direct Render : Ray trace the Audio/Video/Text & Visuals and force/ gravitational effect paths,

Use Vulkan - Direct Compute OpenCL to trace , optimisation and pre-render Vertices,Borders,Renderings & pass Vector trace to GPU & Back To Direct Compute OpenCL.

= Interactive 3D & Web - Open Stack Exploration

HTML DOM (Document Object Model)

"The DOM is the structure a web browser generates from an HTML file. The browser reads the HTML file and generates a version of the elements that is formatted for your JavaScript code to communicate with. We need this “translated” version of the HTML so that we can use JavaScript to talk to the elements on the page. If JavaScript could not talk to the DOM, we wouldn’t be able to use JavaScript to change the appearance of the page."

3D Document manipulation though the use of JQuiry


PHP & Database stack to handle program data and formatting...
& because PHP can output pages and windows in code formatted in DOM and HTML5
We can create menus and pages that do not require the use of large file sets..

Code repetition is the secret of the PHP and database system,
Other systems like sites written in HTML require pages to be written to the server &

PHP with the database stack is a sensible system to reduce the nessety to use Lots of storage for the site code,

PHP code can create multiple window sets with Dom orientated Dynamic JS and HTML5 Dynamically
Using the principle of JQuery & JQuery 3D feature stack.


So HTML5 & DOM create windows, frames & interactive content:

However we need to use standards that create animated objects & people,
While we can draw basic 3D objects in HTML5 we also would like them animated..

3DMax, AutoCAD & other formats provide motion for vectors so we need HTML5 to animate objects though OpenGL, WebGL & Vulkan.. So we need formatting to create motion in GL.

Tables of motion & reaction, created in small database sets, Database sets can be compressed & should be.

For this purpose we use databases, more than 1 so we are able to store sets of objects within the scene or web page.. In essence we stack compressed databases & this allows us to both interact with the page & stop downloading data or save bandwidth.

Secure JS / JQuery / Drupal / FontAwe - Most of the required files are attached with text extension (JS) you will need to unzip them:
Compatible with Migrate version from 1.4 to 3.0.9,
(Later version compatible JS-Code does not need migrate file (Configurations available)

Files - Optimiser's & JS

Trace & Compute : Open CL Direct Compute, GLES,GL,Vulkan,Direct X : 

Video Effects; Ray trace the audio and force/ gravitational effect paths
Use open CL to trace , optimisation and pre-render, Light & Sound & effects such as force-fields,

By Intercepting occlusion in comparison to OpenCL Direct compute directives of force & motion & energy .. Direct compute (OpenGLES 3.1,Vulkan, Open GL & Direct X..

Direct Compute Open CL is able to ray trace anything from simple dynamic effects to bullet trace sound effects, With direct mapped effective & efficient Direct Compute OpenCL in 3 modes:

Direct mapped effective & efficient Direct Compute OpenCL in 3 modes:
Real time
Pre-Rendered on load (scene & lighting, base shadow effects)
Interception real-time pre-render (Microseconds) with Spontaneous : Active CL (tm)

AE Cone Effect: Reducing overhead on 3D Vector emulation of 3D Spaces:

For other functions of reduced precision for the reduction of processing time,Memory or reduced latency.

Use of cone, AE Effects lower the CPU/AVX/GPU processor usage while maintaining effectiveness.

Library builds reduce development costs with Real-Time Engines.

Other effective use of compute such as maths are 100% Effective both in games & on the web.


Vectored code : tessellation & other functions using SIMD & Compute Shader maths:

What we need is a AVX,Vector,Nano,SiMD tessellation solve for a 15 point matrix from Source to face or point of interest and a dynamic vector box to shade in..

If we have 3 destinations that is ether 3 point 16 Tessellations or a total of 3 point 48 point tessellations per culled box or vector cube..

Ray traced : Light,Shadows and depth of field are all obtainable on high efficiency code on our last to latest generations of hardware, GPU:CPU & Vector processor.

Obtain the main trace and we can do micto contoured Commute/Compute shading with the extra resources & even add dynamic polygon count (With or without textures).

Rays have not died yet! Live long and prosper!


VR-VMP-3D - Vector tables/SIMD/RayTracing/High Precision Float:

We can use CPU & GPU MipMap & Tessellation  RiS with micro smoothing predictive tessellation with map fonts, We can also do colour maps and lut conversion for dynamic contrast & Sound for the Realtek Audio codec! We can do this for video also...

Light/Shade & Colour HDR Mapping & Polymorphic HDR 3D Sound; Texture emulation of feel,
Touch and sensation/Sound though Direct Compute Shaders & poly numeric maths.

Haptic 3D feeling/Sensation/Visuals/Sound & Audio for JS/script & code/Open CL/Direct Compute for 3D/Video/Internet HPC.

Sensational Virtual 3D Web/Video/Classic Video/Games/Audio/Fonts with haptic sensation and touch! All new JS ML code to make true sensation : real feels for emotional highs as you chat, tip or cam your game experience & do research high performance compute.

Revolutions in vector: SVM Machine learning optimised & dynamic point/pointer cached ray tracing

Machine Learning Probability Vector Ray Tracing

ML-PVR-T : Wonderful! ML Probability Vector Ray Tracing
Dynamic Many-Light Sampling for Real-Time Ray Tracing

ReSTIR.pdf (47.85 MB)

021-026.pdf (20.02 MB)

RayTracing Vectorized_Production_Path_Tracing_DWA_2017.pdf (2.79 MB)

Raytracing Cell Vector Unit - AVX.pdf (411.51 KB)

Ray Tracing CPU Study 2c2adb30f1ea25eb374839f3f64f9a32b6c7.pdf (6.11 MB)

Raytracing Multi-threaded Sycro Burst thread - A_Vectorized_Traversal_Algorithm_for_Ray_Tracing.pdf (753.83 KB)

Area-Preserving Parameterizations for Spherical Ellipses 1805.09048.pdf (5.11 MB)

Sphere Sampling
Peters2019-SamplingSphericalCaps.pdf (13.52 MB)

ML SVM Assessment - DDOS Protection - Sustainability-12-01035.pdf (1.11 MB)

Attack and anomaly detection in IoT sensors in IoT sites using ML 1-s2.0-S2542660519300241-main.pdf (2.19 MB)

Machine learning for internet of things data analysi 1-s2.0-S235286481730247X-main.pdf (0.99 MB)

Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors - Namuduri_2020_J._Electrochem._Soc._167_037552.pdf (1 MB)

An ultra-compact particle size analyser using a CMOS image sensor and machine learning s41377-020-0255-6.pdf (2.26 MB)

May also help environmental policy:
Processor Applicable Heat Comfort Zones - Application of IoT and Machine Learning techniques 1-s2.0-S1876610218304247-main.pdf (1.21 MB)

Deep Learning in Agriculture + Food Supply sensors-18-02674.pdf (1.4 MB)

svm-notes-long-08.pdf (1.31 MB)

IEEE 754 Precision 151193633.pdf (2.29 MB)
The world defined by Science - IEEE754 Precision conformant compute maths - RS 2020-06-07.txt (1.39 KB)
Sony_to_release_world’s_first_Intelligent_Vision_Sensors_with_AI_processing_functionality.pdf (621.57 KB)

Reducing cost & increasing margins : The power of AI Machine Learning
In a staggering study :

"Calculated based on a resource usage & testing: “We train XLNet-Large
on 512 TPU v3 chips for 500K steps with an Adam optimizer, linear
learning rate decay and a batch size of 2048, which takes about 2.5

In detail ML:

The latest features of CPU,GPU & Brain chip more than counter trouble:

SVM KNN Tensor (AMD) Brain chip majors Google,Sony Visual vector: Japan,China, IBM,Intel

Strategy is important.. the brain chips are mostly about inference,
SVM is about formation & inference implication..
VRISC is about efficiency & power usage..G
PU have many features, FP4,8,16,Float & SiMD

The point is to achieve results that improve much : both efficiency & Accuracy.


SVM Architectural features:RS

CPU & GPU/Processor:

Qualifies 1 to 9 dimensions with elliptic curves,
Into 2 or 3 & Statistics that all can make sense of data,
Under the proposal Elliptic curves from known & recorded messy data sets shall be unitised:
For security

Cypher & GPU/CPU List:RS

SVM Elliptic: Random, Chaos, Entropic like data for security & AI random behaviour

Elliptic curves for security: Pure, Known Messy & Exploratory Messy

Shapes for Games and polygon, Behaviour, Motion, Winds, Rain, Storms : Nature

Mapping & processing Fur and other creative tasks requiring projection or assimily & discovery.


Used directly though automated delivery to AES & Cryptographic features:

Firmware & kernel.

(c)Rupert S


Quantum Neural Networks: Compression & Quantisation for performance boosting: 

precision enhancement & time reduction on a 4/6/8Bit Quantum computer

As explained in the article noise is a big extrapolation in quantum computers, 
Particularly Machine Learning!

Quantizing the data in to conservative maps with precise values,
Increases precision in our world,The quantum world is imprecise surely,

However our data sets do need output that precisely maps to our Quanta sampling,

During sampling quantum data is particularly vulnerable to Sample extrapolation precision reduction.

Strategy is as follows: List : RS

Points are merged with a remainder bit table,
(The remainder bit table has a number of values in a var table.)

SVM Elliptic Quanta table (var)
Elliptic curves are mapped in SVM,
2 to 9 dimension to save space; More for expansive detailing.

Quantisation in this method allows data to merge into reusable Neurons & tables.
Lz4/DOT/GZip compression.

The Remaining table will be accessed in the error correction phase..Usage phase.

The deliberation is to form the resulting data for our quantum sampling;
In as small a package as possible, 

Quanta sampling is error prone.

(c)Rupert S 


Quantum Elliptic Bit Compressed Elliptic ML:

By combining the factors: 
Resonance, Harmonics & Noise filtration & Noise Cancellation in Machine learning..
With compression & bit filtering, Local node bit-depth is not flooded,

The proposal is that bit instability is created by noise & in addition the work done,
Local data width & power output .. Inside the quantum bit; Powerplay a reset.

The name of a reset in our terms is Destabilised Bit & in other words:

Non function quantum fluctuation system, Combining field control & polarity to maintain stability..
Is best served by adaptive Machine Learning, 
Like a chef all the bits of the team controlled dynamically & diplomatically.

Tuning the fields with QE_BEC_ML allows the Irregular bit adaptive Butterfly effect to stabilise the system,
The Man on the job is you. 


University of Chicago
In tandem with the usual electromagnetic pulses used to control quantum systems, 
The team applied an additional continuous alternating magnetic field. 
By precisely tuning this field, 
The scientists could rapidly rotate the electron spins and allow the system to "tune out" the rest of the noise.

Modification that allows quantum systems to stay operational—or "coherent"—10,000 times longer than before.

"Scientists discover way to make quantum states last 10,000 times longer
by Louise Lerner, University of Chicago

MicroCell_VICE_RS(tm) Quantum Useful & Usable

MicroCell_V_ICE(tm) for AI workloads & standard model databases & data

Micro Cell compression with layer pack elliptic vectored Intelligent compression & encryption. (c)RS

Elliptic key defined data in RNDSEED noise: 
Encipher by confusion Quantum Quanta - The data to confuse all 

Up to 16 databases usable at the same time, 
Each has it's own Elliptic key defined to compress & or enhance.
Data sets to be individually compressed in cells.

Noise data set follows elliptic curve standards & can use SVM & Encryption features.

Advantages compression rules work on all data even noise,

1 cell the whole data archive (contains Elliptic noise cell)

Cell Level 2: 2 to 16 (standard practical for multiple ops)(can be more)

All models contain elliptic noise data & are essentially confusing to the decompressor that has no noise key. 


Solid snakes disciple R Python SVM

Machine learning,The Advanced SVM feature Set & Development

CPU lead Advanced SVM potential
GPU refinement & memory Expansion/Expression/Development

SVM/ML Logic for:
PML Vector Ray-Tracing

Sharpening Image Enhancement:

Reactive Image Enhancement : ML VSR : Super Sampling Resolution Enhancement with Tessellated Precision & Anti-Aliasing Ai (S²ecRETA²i) + (SSAA)
Color Dynamic Range Quantification, Mesh Tessellation, Smoothing & Interpolation
Finally MIP-MAP optimised sampling with size/distance, dynamic cache compression.

Machine learning,
The Advanced SVM feature Set & New developments..TPU <> GDev,AMD

Extended support for ML means dynamic INT4/8/16/Float types and dot product instructions execution.

Dual compute unit exposure of additional mixed-precision dot-product modes in the ALUs,
Primarily for accelerating machine learning inference,
A mixed-precision FMA dot2 will compute two half-precision multiplications and then add the results to a single-precision accumulator. For even greater throughput,

Some ALUs will support 8-bit integer dot4 operations and 4-bit dot8 operations,
All of which use 32-bit accumulators to avoid any overflows."

Core-ML runs on all 3 hardware parts: CPU, GPU, Neural Engine ASIC;SVM.
The developer doesn’t specify; The software middle-ware chooses which part to run ML models,

Core strategic advice & adaptable SVM CPU <> GPU


Super resolution AKA resolution enhancement feature:
Is already enabled by super sampling on GN architecture; 
The availability of this product really comes down to the pipeline for sampling..

The real investment is in compute shaders that will load the textures with minimal processing extras;
on load time exploitation, 
The use of DOT3 to DOT5 compression:
Really means that implementing large scale higher precision A(File storage in ram) To B(Final render + Cache data)..

Creates the situation in decision making where the processor Vector based Texture resolution enhancement,
Comes at little ram storage costs; 
When the processing is AVX,SiMD & is a light flow of additional data applied to the texture;
As a cached bump-map & co-modifiers..

The decision to cache the data DOT5 or better compression means that dual loading data is possible given meta data on load...
Combined data can be loaded from game cache (On SSD/HD or in computer RAM),

Given the availability of direct access on the PS4/5 and XBox to the GPU ram from CPU, 
Increasingly the use of CPU AVX or Shared SiMD is capable of processing the flow dynamically..

Improving this the shared cache; CPU & GPU; The frame buffer.. Creates the potential; A vast potential to simply leverage the on-DIE CPU & GPU capacity without suffering DMA flow capacity performance issues!

Data width is a pretty important feature to deal with and fortunately we are not stuck with 32Bit or even 64Bit with DRAM potential being 384 Bit on DMA
& also directly though the board PCI3 to PCI5 specifications,

On the PS5 the 7 layer QoS for data transfer & the direct Storage layer technology,
On the XBox permits Direct to RAM DMA for compressed pure DOT3/5 Textures & processing directly in CPU to GPU should not involve pipeline fluctuations or imprecise mapping of the 128 space SiMD Precision; Control & Enhancement dynamic re-compressed adaptive feature set.


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.

IO & DMA System Drivers & Data Throughput: CPU/GPU/Compute Unit : Scheduling works 3+1vdat ways: (c)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.

IO & DMA System Drivers & Data Throughput: 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.

IO & DMA System Drivers & Data Throughput: 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.

IO & DMA System Drivers & Data Throughput: TPU fragment : ML Inference Open CL, SysCL,
Shader cache & Cache/RAM 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

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.

All this can be ours : Witcher 3 Example Video

Technical video : Tanks :

3D VR Haptic & learn: RS

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 & infra-sound harmonic 3D interpretation, Such as Sonar & Radar.

Game Raytrace & Refraction Logic ML: (c)RS

Use several low precision shape maps and not boxes,
In particular the tank is not transparent

True Depth is most likely for glass,
However the shape of the object in slices along the tank (like a 16 part skin inter-sector,

The Size of Inter-section boxes is (length/16) * (Width/16) * (Depth/16)
Depth also for Glass & tank height & involved a volume format like so:

The Size of Inter-section boxes is (With Transparency)

On Contact = (length/16) * (Width/16) * (Depth/16) = Size |
(Size/% from one end) + (% One Side) = Location |
(Arc,Sin,Tan + Location) * % Opacity = RayDepth |
(Arc,Sin,Tan + Location) * % Refraction index = RayDepth + Probable location

Optimising the number is tested on performance test of a (simple varied complex) Object scene & compared to results of previous results for GPU & CPU Type,

With also a ram block such as 4GB/8GB/16GB


Raytracing potent compute research's:

Realtime Ray Tracing on current CPU Architectures :

Demo WebAssem: NoSiMD: SiMD & AVX Proof of importance

HDR Raytracing - Thoughts & Theory - Mine (7.33 MB):

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 .


Quick and uncomplicated dynamic feedback content optimisation of sub pixel
data and meshes


Optimised Micro Force Tessellation Dynamic Fragment Shading : ML OM-FT DFS

Firstly the list is as follows:

Polygon & Shader : Memory array allocation with tessellation percentile availability
Scene Polygon Mesh load
Secondary Memory array allocation
Optimised list Texture resource load/Pre fetch

Resource availability assessment for dynamic content
Tessellation of on screen & in view content & static

In scene data :

Static load tessellation with dynamic vertic modification buffer (a small piece of shared data cache (up to 2GB))(Tessellation and shaders with Mipmap have modest requirements in HD)
Optimised Micro Force Tessellation Dynamic Fragment Shading : OMFT DFS


Screen resolution enhancement: up-scaling & downscaling: 4D-Vector Enhancement: Kernel+hint 3D: 

Tessellation of the 2D/3D plane surface on the screen buffer,
3D component render into output frame buffer, With RiS with micro smoothing predictive tessellation.

The objective is to present the user with a virtual resolution of almost unlimited size,
From the 2D,3D,4D,5D 8, texture, poly-map, shader pipeline..
After we upscale the vector construction to whatever level we like with tessellation to the render buffer; We will apply texture map with AA + RiS Sharpening SiMD, bump and shader mapping..

Apply Multi-thread,SiMD,AVX,Vector unit or float combinations; To all render targets in the pipeline.

Bearing in mind that the polygonal representation of shadows after we apply the SiMD,AVX,Vector unit or float combinations; To all render targets in the pipeline..
Does not consume the level of RAM that Textures will use in our pipeline,
However applying Vectored AA & sharpening to textures has the potential to hold the maths/Shader resultant float/Integer in the cache.

So by preference we have the ability to use ether more ram for texture + Compression & also shader/float result & N component pre-render target maths/Variables.

This shall be fast & consume less ram with DOT3/4/5 ARGB compression.

Principally render into a virtual frame will be AA+Sharpen+Tessellation enhancement.

Tessellation of 2D VR target output frame to map the colour & sharpening AA ..
Into the final frame that shall be smooth & look observably like vector fonts do with kernel fonting,
AKA kernel vector with hinting; smooth,sharp & clean.

Virtual Render path:For upscaling Cyberpunk 2077

1440/2160 into 4K,8K 
(Does not have to mode set 4K to Virtual Resolution 8K)
Virtual Resolution is a method of superSampling into a lower resolution;
That smooths & sharpens the look; Removing jagged edges.

4x4SuperSample @ 4K & then 2x2 super sample to 8K 

(One pass 6xSuperSample may be worth it for 8K)

2 pipelines to tessellate Lines & textures SiMD to sharpen edges & features

3 pipelines of SiMD to add additional Logic sets:
PreFrame ML (forward render (Biking for example is a linear path)

Wavelet Smoothing & Color,Tone,HDR,WCG render pass

Fine edge rounding & Tessellation.

(c)Rupert Summerskill


LUT tables and tone mapping: Vectors

On the subject of LUT tables and tone mapping, 2 methods are available to us..

The Vectors can be mapped RT with ray tracing (they work out the vector)

The Vectors and dimensions can also be worked out with Open CL and Direct Compute..
Both OpenGL/Vulkan & Direct X have direct compute..

Many forms of vector calculation that involve intricate maths  can be worked out in vector or OpenCL Vector library function, The advantage of Open CL Libraries are that functions and tables can be worked out without ever having to re program the maths solving OpenCL Code,

Such that Open CL & Direct compute libraries can for-fill many tasks, Bearing in mind that Open CL & Direct compute are work solve time controlled we are able to use the functions for many tasks including web browser maths and composure, With these examples we' will define the future of display maths code & logic.

AVX & Float can obviously be used leaving Compute vectors like SIMD viable for code logic.
Compute Shaders are also able, Long logic denotes the advantage of Vectored OpenCL & Direct compute/AVX.

Vectored code : tessellation & other functions using SIMD & Compute Shader maths:

Ultra High Definition Colour : 

Video Colour definition smoothing & Optimisation with sharp edge HDR Contrast Adaptation.
Dynamic colour remap & Optimisation,
Wide path 8 512Bit,256Bit,128Bit,96Bit 8; 16Bit per channel into & from 10Bit per channel & 8Bit Per channel ..
With dynamic hardware accelerated Colour translation & super dithering with AA in transparent ranges, LOD Translation in vectored 3D though FPU/GPU/AVX/SiMD.

(c)Rupert S

Networking, Audio & Display Codecs:
Have you thought about using shaders in Networking ? to realise the network data strategy...
The same is true for displays & Audio & other Science data such as Neural networks,
Image improvement and encoding & entertainment video codecs, 64Bit HDR Dynamic Contrast

We can apply the interpolation to video for smoothing and vectorisation of the video elements in float for sharpening & to our interpolation for tessellation of the RiS sharpening for all our GPU and CPU elements.



List Compressor:RS:

Power VR invented the original: We create the best : Compress : DOT3/4/5+

Great for games without the direct feed of 30GB of DOT3/4/5+ compressed texture cache,
Cache & Layer download from fast B-Ray,ROM's & Storage: Cache dynamic.
Utilise the Blue RAY & ROM & DVD Double Layer..

List Compressor:RS:

GLTF, DOT3 to DOT5 compress all textures; At a minimum in 4Bit too 16Bit per channel,Optimised layer patching, That is when we overlay Higher Bit depth Textures & HLSL Shaders..

In layers on GPU/CPU/Vector/Float processed & merged texture content,The lower bit depth base texture is optimised JPG style and GIF & Merged,

Lower order bumpmap & Shaders are merged into the mipmap layer, To reduce processing overhead; At a reasonable rate of memory usage,

Combined order Process CSS:JS allows 2kb files too merge multiple jason : All are GZ, LHA7 compressed & optimised/Minified,

Storage of Large file is Internal Slot/External HDD & BlueRay/DVD/USB Key Flash & Micro HDD, At 8GB too 2TB minimum specs : USB2/3/3.2The higher the data rate on test, The higher the desired storage profile that ML will allocate :

Dynamic Allocation ML: User Option: Default : External USB Drive for data loading under 250MB/S & 64GB+ of space.

GameHIVE: NamCloud :RS

(refer to List Compressor:RS)

Data Compression, Priority Core Program & Library Optimisation,
Core library Re-evaluation for replacement with upgraded libraries.

The priority is to Recompile core code with DOT3 > Dot5 & compression,
As the compression formulas are introduced into the library of games on the servers..

Core game packs 64MB between 2GB are Plug & Play, Downloaded into place on the Console,
No decompression is needed; The level packs & core compression texture blocks are stored..
As Micro FastLayerCompression/Decompression with quick sort Pre-Compressed Texture formats in LVM/VM/VMD drives..

Optimally they will include 5 Game 15Min play.. auto saves worth of location content.

Multiplayer & Dynamic scenario content:

Obviously core 1Mb to 10MB downloads of cache data in the GameHIVE VM Dynamic Cache drive..
(Optimised to allocated storage, External Flash recommended)

Micro 15KB to 250KB Dynamic scenario content : Weather, Enemies, Updates, Friendly data.

Game cloud storage philosophy to be based upon: Upvote, Pro Review & Necessity.

Game optimisation Review strategy : GORS
Is optimised for texture & vertices file re compression & optimisation.
(refer to List Compressor:RS & GameHIVE: NamCloud :RS)

(c)Rupert S


WebCLGL : Libraries & JS

WebCLGL use WebGL2 specification to interpret code.
WebGL is used like OpenCL for GPGPU calculus using the traditional Render To Texture technique.

WebGL Compute

ROCm & Vulkan Drivers : Debian/Ubuntu Linux install :

The thing with AMD drivers is that you need to uninstall the previous driver completely first before installing the new one.. ROCm sounds promisingly likely to improve with the laboratories promising to improve ROCm with cray & does not require uninstalling ...

ROCm & Vulkan Drivers install

run this after downloading file (google drive):

sudo chmod 774
sudo ./

Open Source Driver for Vulkan : Debian/Ubuntu/Linux

run this after downloading file:

sudo chmod 774
sudo ./

GL to Vulkan : gfx-portability : Prototype library implementing Vulkan Portability Initiative using gfx-hal. See gfx-rs meta issue for backend limitations and further details.

OpenCL/OpenGL/Vulkan API : Mac:Windows:Linux:Android

Texture & polygon optimiser & compressor

Speeding up websites JS - for JQuery, PHP excettera! Very exciting for app development & Boinc SDK

Fetch code includes optimisation - to be run before JQuery

Require-min to be run before JQuery - migration is for older version compatibility

Site Efficiency!


*Node.js package command suggestions:

npm install -g --save npm@latest amd random cacache pacote node-cache requirejs jquery crypto-js zlibjs @types/jqueryui drupal-node.js get-google-fonts google-font-installer font-awesome @fortawesome/fontawesome-free bootstrap angular-bootstrap bootstrap-css-only util texture-compressor compress-images cdnjs jsdom canvas @tensorflow/tfjs-backend-node @tensorflow/tfjs-backend-nodegl ml5 @tensorflow/tfjs @tensorflow/tfjs-backend-wasm @tensorflow/tfjs-backend-webgpu @tensorflow/tfjs-backend-webgl

npm audit fix

linux node.js:

sudo npm install -g --save npm@latest amd random cacache pacote node-cache requirejs jquery crypto-js zlibjs @types/jqueryui drupal-node.js get-google-fonts google-font-installer font-awesome @fortawesome/fontawesome-free bootstrap angular-bootstrap bootstrap-css-only util texture-compressor compress-images cdnjs jsdom canvas @tensorflow/tfjs-backend-node @tensorflow/tfjs-backend-nodegl ml5 @tensorflow/tfjs @tensorflow/tfjs-backend-wasm @tensorflow/tfjs-backend-webgpu @tensorflow/tfjs-backend-webgl && sudo npm audit fix


Machine Learning & Code : Tensor flow JS + WASM AVX+SiMD (c)RS

Tensor flow JS is a flexible way to do Machine Learning & Adaptive functions on Computers; Phones & in web-browsers.

In the guide below are examples & JS Code in a zip also,
The Tensor flow system in improving because originally specialized hardware was required..
Developing a JS base for the ML community has allowed considerable improvement & SiMD/AVX/Vector instruction support..

Considerably improves performance of Tensor ML & Even outperforms less powerful GPU:
GPU without powerful SiMD is a little less powerful than on the CPU with SiMD (In the phone market),

In the PC & Mac market both the GPU & CPU have AVX or SiMD Vector & performance can be considerable!
Bearing in mind that SiMD ML seems to have great performance & utility.

(c)Rupert S


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