Tuesday, June 18, 2019

Vulkan Kernel - Windows - Linux - Android - Apple

The Vulkan Kernel - Windows - Linux - Android - Apple


References to VULKAN can be replaced or supplemented with Metal (Mac OS) & Direct X

All driver parameters are set according to the Vulkan principle with hardware and AVX & Float versioning...

Under the level 2 kernel api calls, Direct messages to the driver modules in Vulkan compatible script, The kernel is fittingly representing the screen resolution though mode & VESA Standard 2D & 3D instructions..

Mode query will one proposes include Vulkan API call queries to all compatible devices..
For this purpose the api will call an ID on the device for driver model/Version.

Once the VESA & Vulkan standards are found; Mode setting can be carried out by the OS,
However the Open CL & GL functions for screen settings can be used pure.

Depending on space in the bios uefi the calls may have already been made to call the screen optimum into being, Bios calls can reference hardware, IO, DMA & Memory/Feature Sets,
Such settings are equivalent to environmental flags (Set).

Pure Vulkan kernel calls in Kernel speed up the process & lower RAM usage,
Possibly increase security (ideally)

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.


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.

Kernel plans are to improve OS + Kernel integration for Bios MK3/4:

"GPU/CPU Float capacity; May well be far more of worth than we have as yet anticipated.

SiMD <> Float conversion makes practical the evolution of super functional dynamic capacity,
In the CPU/GPU/Processor market..
Integer <> Float conversion; Covers the rest of our needs for precision enhancement.

(c)RS" 

****

Thought it best to let CPU/GPU Creators get there thoughts around the latest development that would improve the OS & bios..Making improvements in interaction that improve latency issues and ram usage in conditions such as HPC,

High performance computing requires low latency & reliable OS function with a whole lot less ram leaks & CPU clock cycle usage..Particularly in the case of GPU usage in kernel load; For example in linux or phone (Android for example or ios)

Development of low tick dynamic kernel integrated GPU functions in the same manner as chrome integrates function on the fly,Chrome is quite impressive in this light; As also are the OpenGLES applications..

Core functions are the most worthy of optimisation & obviously we require the best work to be done on shared resources ..Resources shared across core functions like Float and AVX/SiMD.

Integrating core AVX or Vector function of provident use & optimisation; Are surely of most use to GPU Manufacturers 

Interested in pipelined Pixle/SiMD instruction pipelines for Tessellation or other core non floating,

Instruction...Integrating float conversion so that SiMD can be carried out converted into Majority GPU/CPU Float capacity; May well be far more of worth than we have as yet anticipated.

SiMD <> Float conversion makes practical the evolution of super functional dynamic capacity,
In the CPU/GPU/Processor market..
Integer <> Float conversion; Covers the rest of our needs for precision enhancement.

(c)Rupert S

****

SVM:S.ADFM SVM : Dedicated Adaptive Hardware & Firmware

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.

SVM:S.ADFM SVM : Dedicated Adaptive Hardware & Firmware

Dedicated Adaptive Hardware & Firmware:  SVM:S.ADFM SVM ML Adaptive, 
The new SVM:S.ADFM SVM ML Adaptive : Sample with AutoDynamic Feedback & Map Streaming with compression(tm)RiS technology

(c)Rupert S 

http://science.n-helix.com/2020/04/render.html

****

https://science.n-helix.com/2019/06/vulkan-stack.html

https://science.n-helix.com/2016/04/3d-desktop-virtualization.html

https://science.n-helix.com/2017/02/open-gaming.html

lhttps://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html

https://science.n-helix.com/2019/05/compiler-optimisation.html

Compiler books & reading : https://science.n-helix.com/2017/04/boinc.html

Vectored code : tessellation & other functions using SIMD & Compute Shader maths: 
https://www.youtube.com/watch?v=0DLOJPSxJEg

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 >
HTML5 >
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

https://science.n-helix.com/2017/02/open-gaming.html

https://science.n-helix.com/2016/04/3d-desktop-virtualization.html

https://science.n-helix.com/2020/04/render.html - 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."

https://generalassemb.ly/coding/full-stack-web-development/dom-manipulation

https://www.w3schools.com/js/js_htmldom.asp

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 https://is.gd/ProcessorLasso
****

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.

Creating SDK ARM/X86/GPU/FPMG/SiMD

Vectored code : tessellation & other functions using SIMD & Compute Shader maths:
https://www.youtube.com/watch?v=0DLOJPSxJEg

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)
https://mirrorace.com/m/X7ia

021-026.pdf (20.02 MB)
https://mirrorace.com/m/X7ib

RayTracing Vectorized_Production_Path_Tracing_DWA_2017.pdf (2.79 MB)
https://mirrorace.com/m/51v6t

Raytracing Cell Vector Unit - AVX.pdf (411.51 KB)
https://mirrorace.com/m/51v6u

Ray Tracing CPU Study 2c2adb30f1ea25eb374839f3f64f9a32b6c7.pdf (6.11 MB)
https://mirrorace.com/m/51v6A

Raytracing Multi-threaded Sycro Burst thread - A_Vectorized_Traversal_Algorithm_for_Ray_Tracing.pdf (753.83 KB)
https://mirrorace.com/m/4lxS6

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/

Area-Preserving Parameterizations for Spherical Ellipses 1805.09048.pdf (5.11 MB)
https://mirrorace.com/m/4lxS5

Sphere Sampling
Peters2019-SamplingSphericalCaps.pdf (13.52 MB)
https://mirrorace.com/m/3FApr

ML SVM Assessment - DDOS Protection - Sustainability-12-01035.pdf (1.11 MB)
https://mirrorace.com/m/1Dro1

Attack and anomaly detection in IoT sensors in IoT sites using ML 1-s2.0-S2542660519300241-main.pdf (2.19 MB)
https://mirrorace.com/m/51v5z

Machine learning for internet of things data analysi 1-s2.0-S235286481730247X-main.pdf (0.99 MB)
https://mirrorace.com/m/3FAb0

Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors - Namuduri_2020_J._Electrochem._Soc._167_037552.pdf (1 MB)
https://mirrorace.com/m/51v6D

An ultra-compact particle size analyser using a CMOS image sensor and machine learning s41377-020-0255-6.pdf (2.26 MB)
https://mirrorace.com/m/51v5u

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)
https://mirrorace.com/m/51v5C

Deep Learning in Agriculture + Food Supply sensors-18-02674.pdf (1.4 MB)
https://mirrorace.com/m/51v5w

svm-notes-long-08.pdf (1.31 MB)
https://mirrorace.com/m/1Dro9

IEEE 754 Precision 151193633.pdf (2.29 MB)
https://mirrorace.com/m/1Dro5
The world defined by Science - IEEE754 Precision conformant compute maths - RS 2020-06-07.txt (1.39 KB)
https://mirrorace.com/m/1Dk5c
Sony_to_release_world’s_first_Intelligent_Vision_Sensors_with_AI_processing_functionality.pdf (621.57 KB)
https://mirrorace.com/m/1Dro3

https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
https://scikit-learn.org/stable/modules/svm.html
https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989
https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47?gi=51274a92cf9b
http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf

https://docs.microsoft.com/en-us/machine-learning-server/r-client/what-is-microsoft-r-client
https://docs.microsoft.com/en-us/machine-learning-server/python-reference/microsoftml/microsoftml-package
https://docs.microsoft.com/en-us/machine-learning-server/install/microsoftml-install-pretrained-models
https://github.com/iterative/dvc

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:
Shaders,
Tessellation,
Compression,
PML Vector Ray-Tracing

Sharpening Image Enhancement:

(S²ecRETA²i)(tm)
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.
GPU/CPU/Feature-set/SVM

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

https://scikit-learn.org/stable/modules/svm.html

(c)RS
*

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

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

Technical video : Tanks : https://www.youtube.com/watch?v=LGBHkpYq9hA

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

(c)RS*

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 .

QUiDOC-ML

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

ML OM-FT DFS

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.

(c)RS
(c)Rupert Summerskill

****

LUT tables and tone mapping: Vectors

https://gpuopen.com/using-amd-freesync-2-hdr-gamut-mapping/

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:
https://www.youtube.com/watch?v=0DLOJPSxJEg

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.

https://science.n-helix.com/2014/08/turning-classic-film-into-3d-footage-crs.html

https://science.n-helix.com/2019/06/vulkan-stack.html

(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

https://is.gd/SuggestedShaders

https://is.gd/ShadersSDK

https://is.gd/CodecShaders

https://is.gd/InterpolationShaders

https://is.gd/ShaderGuide

https://is.gd/DirectXShaderCompiler2019

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.

(c)RS

https://reshade.me/

https://reshade.me/presets

****

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 http://science.n-helix.com/2020/04/render.html

****
https://science.n-helix.com/2012/09/geometric-calculating-machines.html

WebCLGL : Libraries & JS
https://github.com/stormcolor/webclgl
https://github.com/stormcolor/webclgl/blob/master/dist/webclgl/WebCLGL.min.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
https://www.khronos.org/registry/webgl/specs/latest/2.0-compute/#diff-with-gles31

https://www.khronos.org/assets/uploads/developers/library/2017-webgl-webinar/Khronos-Webinar-WebGL-20-is-here_What-you-need-to-know_Apr17.pdf

****
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 ...

https://www.phoronix.com/scan.php?page=news_item&px=Radeon-ROCm-2.3-Released

https://rocm.github.io/blog.html

https://github.com/RadeonOpenCompute/ROCm

ROCm & Vulkan Drivers install

run this after downloading file (google drive): https://is.gd/Install_gpl_ROCm_amd_drv_sh

sudo chmod 774 Install-gpl-ROCm-amd-drivers.sh
sudo ./Install-gpl-ROCm-amd-drivers.sh

****
Open Source Driver for Vulkan : Debian/Ubuntu/Linux
https://github.com/GPUOpen-Drivers/AMDVLK

run this after downloading file: https://is.gd/Install_gpl_amd_drivers_sh

sudo chmod 774 Install-gpl-amd-drivers.sh
sudo ./Install-gpl-amd-drivers.sh

****
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.

https://github.com/gfx-rs/portability

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

https://github.com/KhronosGroup/MoltenVK/releases/tag/v1.0.35

https://github.com/KhronosGroup/OpenCL-ICD-Loader

https://github.com/KhronosGroup/OpenCL-CLHPP

****
Texture & polygon optimiser & compressor

https://github.com/GPUOpen-Tools/Compressonator/releases

https://github.com/KhronosGroup/glTF-Compressonator

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

Fetch code includes optimisation - to be run before JQuery

https://is.gd/FetchB4JQuery

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

Site Efficiency!

https://requirejs.org/docs/download.html

https://jquery.com/download/

https://jqueryui.com/

****

*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

http://science.n-helix.com/2018/09/hpc-pack-install-guide.html

(c)RS