Friday, March 24, 2023

Path-trace-RTDL (c)RS - The combination of Ray Tracing & Path Tracing & FSR_DL; The advantage being a combination of RayTrace CU & General SiMD

Path-trace-RTDL (c)RS

The combination of Ray Tracing & Path Tracing & FSR_DL; The advantage being a combination of RayTrace CU & General SiMD, RS 2023-03 in response to the RS Technology being implemented.

Path Tracing define: RS

Path tracing is when you take an objective viewpoint; A number of viewpoints to the receptor (Observer, such as gamer or camera

VP = View Point, Observer is camera, RT Path = RT Core Ray

View point Mesh, That is directional

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

VP : VP : VP : VP : VP {Forward} VP : VP : VP : VP : VP

VP : VP : VP : VP : VP {Observer} VP : VP : VP : VP : VP

VP : VP : VP : VP : VP {Backward} VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

                             VP : VP : VP : VP : VP

The location VP initiates a SiMD view directly to & from reflective objects & calculates distortion of view & texture with FSR_DL

In this view i would like you to consider a reflective bounce camera viewpoint & think of the energy that saves you.

RayTracing Define:

Ray is cast from object & calculated to target vector; With Distortion calculation & reflections.

We Combine minimum intersection with the VP; Using a cast Ray; So we know the viewpoint is active,
We trace the route back as the observer & calculate each intersection as an observer...

FSR_DL handles surface distortions & fogs of war...

We minimise the viewpoints memory footprint by altering the scale of the viewpoint in respect to the observers screen resolution / Distance .... We can also upscale the pretend frame!

We can cache the frame & discard if we wish!

Raytracing also provides distortion defines for viewpoints & Ray Distortion & Direction Calculations.

RT-Sparse-Field Pre Calculation Cache : RS & Lisa Lue

During the initiation of the frame we calculate polygon placement,
We Cache the metrics & use them for our distance fields.

Long term Non Volatile Cache
Short term recalculation cache
Validate Cache & use for ray tracing RayMarch & lighting.

Real Time Sparse Distance Fields:

Distance Fields are defined as Object detection with range finding,
In GPU SiMD we can reduce the Field Multiple Recount,

Low cache containment Serial processing; Is where we have not got all the Polygon distances counted & in cache...

We can however count on the GPU having the Polygon Map in RAM for a small segment of polygons; But due to the fact that we place the polygons in precise locations; We already have Distance.

Distance fields are helpful because; Ray-forwarding (Ray March) does not need to do more than,
Process; Distortion & Viscosity & Density & transparency & Reflection,

But we can do this over larger fields in areas with lower levels of modification property with counts as a lower required precision!

(c)Rupert S

Path-trace-RTDL : This could be us : Path Tracing all light reflection, Does not require something as high on GPU as RX6500! Can be CPU SiMD/AVX on the Vectors, So can be a regular thing!

We can even super sample our cube maps dynamically; So that we take the vector locations & transform the cube maps into fully RayMaped Polygons.

The results are all about how we plan to Dynamically Optimise & Draw Vectors.



PoCL Source & Code

Bus Tec :

Audio BT Codec

DSC, ETC, ASTC & DTX Compression for display frames

Monday, February 27, 2023


Similar Wavelet Conversion with minimal reprocessing : Smart Access : RS

(repeated encoding cost reduction) i know you are a coder, you could help ffmeg & avx on the FX8320E, Likewise consoles face same issue with FFPEG & Codecs & likewise with media acceleration by non repetition of encoding

Similar Wavelet Conversion with minimal reprocessing : Smart Access : RS

Printing Technology 'When you "Tie" the Knot' : 
We want those Hand drawn Donald duck, Micky & Daffy in true line drawn splendour, 
But hand drawing 8K is hell, 
Remaster printing technology : For all monitors, TV's & Operating systems : DTS, Dolby : Functioning wave conversion

Smart-De-Compression : repeated encoding cost reduction : (c)Rupert S

Wavelet Classifiers

Compressed Data, GZip, BZip, LZH

Primarily our goal is to Originate Encode in a form that is Compatable with the hardware chain,

For example in the case of HDD > CPU > GPU the right Texture & Number formats, Often 16Bit or 32Bit float & Texture,

However with Video we have to expand the frame wavelets into Compatable Texture formats!

We convert the Video Wavelet in Smart Access to the closest Texture format wavelet; Or directly play the video! But suppose we are using Bink Video? We directly convert & keep wavelets that are the same in the new texture,

We therefore select a texture format like NV12 or ETC2; One that has the most Similar Wavelets & can therefore reduce Conversion Cost of the frame by as much as 100% (If all wavelets are the same)!

We know Wavelet types & Colour depth of all texture classes; So we will select one with a good range,
In most cases we play MP4+ Wavelets; So we can Use a JPG type texture; So all the compression wavelets remain minimally processed.

A single Frame + previous B Frame; Into a single texture of the same Wavelet Compression Classification,

The result is minimal processing CPU Cycles.


Overall reducing costs of higher resolution resolving; As available in 264 > 265 > 266/VVC & other Media Encoders : Rupert S

You can see that, formats such as 265 & 264 are related, Obviously at a higher resolution in the case of 265!
But in many Wavelet transform cases we can minimise the Processing cost, We do however need to know like Google's ML Voice Encoder; The ones we do not need to change (minimum benefaction)

My chief challenge of Wavelet thought is a multiple frame picture of an eye (WebP for example),
The resolution is 640x480 & We know in most probabilities that; The Eye was transformed to wavelet in HD,

So we have a wavelet curve; Black centre & A surrounding Iris!
We need to expand that wavelet so we will suppose that the higher precision version of the wavelet will add details?

We must explore how the wavelet transforms a Higher Resolution form into a lower resolution form,
We can therefore in theory use the same wavelet at higher resolving depth?

We might be able to convert a lower resolving wavelet in 12Bit into the 16Bit version & have a better understanding of the higher quality version!

We can therefore most probably reuse the wavelet; Transforming from 264 to 265 & upscale & compress more,

Overall reducing costs of higher resolution resolving; As available in 264 > 265 > 266/VVC

#WaveletProve Both that the wavelet is infinite & that; The Breton
shirt wavelet has a pattern represented in 12Bit but liberating into
the profound on 16Bit, 32Bit & more!

(To understand wavelet context, in textile & theory & of course Audio & Video)

Can we prove the wavelet of a Breton shirt for infinity, like mauri
My augment being that we can upscale that Breton shirt! & prove it's
17th century values...
Both that the wavelet is infinite & that; The Breton shirt wavelet has
a pattern represented in 12Bit but liberating into the profound on
16Bit, 32Bit & more!

Example Wavelets to prove upscaling is possible

Rupert S

Wavelet Upscaling : JPG / Video / Games

Example 2 Voxel to High Quality : RS

The Story : HP : V-FX Wavelet Voxel Transforms : V-FX-WVT (c)RS (Harry Potter + More)

I was wondering what to add to Wavelet transforms; Well i was thinking about Harry Potter,
Full body FX are Half Resolution; In Fact they are Depth of Field Voxels,

For people who don't know Voxel is when you make a Cube of the right shade from a picture & set it at the right depth!

For those criticizing such an act as lazy; You would have to understand how fast technology has developed!

Some characters Fly at a very low resolution & Others like Harry Potter & Melfoy Don't!

You would have to realise that V-FX is based on the ability of the person to be in the role... They perform ;-)


V-FX Wavelet Voxel Transforms : V-FX-WVT (c)RS (Harry Potter + More)


The Wavelet is the JPG Pixel Group of a single Group of pixels at the same size as the composing Voxels of the V-FX

A Voxel is a Cube of Pixels set in 3D

When it comes to Transforms; This piece is called:

Transforms for classic movies : How you upscale VFX : RS

Firstly the VOXEL (Simple Wavelet Cube) needs to be compared to a fully dressed original character,

Then you need to map the correct features into The voxel cube space; After you Average Anti-Alias & Upscale the Cube Map (Original V-FX + Original Video Frame Person)

You then need to map an effective Wavelet of the Original V-FX with a modifier Layer of transparent Wavelet (The Photo in High Detail, This is also a Wavelet Series)



Example 3 : Lessons to learn : Wavelets : Upscaling (c)RS

Now about the Voxel 4x4 cube map 'Transform wavelet' is a simple JPG Wavelet
(if used properly compressed & older games did not because processors where not very fast (33Mhz)

High resolution 'Transform Wavelet' (Overlayed) is a full to higher resolution JPG Wavelet
In Upscaling we need to get from one to the other,
Transform Wavelet from Voxel Wavelet,

Sample Scaling:But supposing we have samples of like minded objects?
We can use Machine Learning to imprint a pattern!

But great looking as this is, not perfect as seen in Example 3 About Example 2 : HP!

Wavelet permutation:

Resolve the wavelet to full precision, Workable; But we need to know the result is correct!ML Can help; But that is very subjective..

Mostly this works.

Identity Follow through:

Machine Learning that identifies the subject matter [Samsung & LG TV's 2020+ Example]

So what do we do? We Add the lot! haha

Rupert S


Example 4 : Lessons to learn : Wavelets : Upscaling (c)RS

2 Pattern Matrix Wavelet (c)RS

Wavelets are patterns; With Colour infilling (why not a wavelet itself!

Well wavelets come in forms (Gif)8Bit, 10Bit, 12Bit, 16Bit(JPG)

We can advance the precision by using a higher Precision (16Bit, 24Bit, 32Bit); But we need to save storage space!

First thing is to use bF16 & bF32; This keeps the majority of the data from being sub pixels.

Second thing is to make maximum use of multiple Precisions, Mix F16 with F32..
Google Lyra Codec demonstrates this in Machine Learning.

Third : Keep Precision within margins, Small Textures do well in 8Bit Matrix Wavelets...
But 16Bit Colour Precision & 16Bit Precision both look good in HD High Quality HDR WCG

(Usable as encryption archetype): Chaos:A:B:T:Pi:Arc:Sin:Tan
Very usable /dev/rnd Random Ring : TRNG : GPU : CPU : Asics : Using Chaos Wavelet

{Wavelet:Colour Point) A to B as expression of Pi
{Wavelet:Colour Point} A to B as expression of Arc, Sin, Tan

[2PMW File Array]
[Header : Easy Identifier : Basic Name]
{Header Packed Wavelet Groups] [1 Image Wavelet : Colour Shading Wavelet 2, 4, 8 Group]

[Image Array lines]
|Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]
[Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]
[Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]



Audio/Video/Image Format : Packing Vectors (c)RS

Vector Wavelet Examples : Math object

Wavelet Curve compress, Normally from left because we code Left to right & that is optimal for our hardware.
Can be numeric sequence Direction point 1=D D=1,2,3,4 2=Db = 1,2,3,4 | Displacement Dp = 1,2,3,4 Assuming Left To Right or curve displacement = Time

Distance N from source edge, Curve:Sin/Tan
(Example) D=1 Db=3 Dp1=2 Dp2=3 | Curve = Tan3+Db2

Logarithmic Pack,
Integer Comparator : N+N2+N3=N+1+2+3 | Sequence

Example 5 : Predict Scaling : SiMD/AVX.SSE3 : (c)RS

SiMD Interpolation grids & Predict with Raytracing & General SiMD
Reference Grid

With the Interception/Processing of Predict Statements in Frames of Video & Audio; Using a simple Grid:

Pr = Predict (motion) Px = Pixel t1:2:3 time period


Basically you can see the pixels move in frame Px1 & Predicted in Pr2 & Pr3,
Raytracing SiMD predict future motion though maths; We can use the SiMD to,

Both predict & interpolate/Upscale from 8bit, 10Bit, 12Bit, 14Bit to 16Bit values or rather wavelets,
Because Raytracing SiMD are high precision maths; They prove advantageous if we have them; SiMD/AVX.SSE3

Interpolation : Prxi Pxri : {PxPrPi} Theory : RS

We must present a point between Px (pixel) & Pr (predict); In maths this would be a remainder,
We can draw a pixel in the Remainder Point; The Interpolation point (PI); When? When we upscale!,
We can use two principles, Px (actual pixel), Pr (Predicted Pixel), PI Pixel Interpolation!

We can guess with both Px & Pr on the content of PI & both Predict & Interpolate the pixel...
As additional Data; This does not worry us a lot.


(c)Rupert S


Interpolation & Extrapolation Policy : RS

We can conclude Interpolation & Tessellation have requirements : 2D & 3D Spline Interpolation & Extrapolation; Gaussian methods on linear surfaces,

We extrapolate the new; Such as blade edge; We can however layout a simple grid to our supposition edge & interpolate.

We do not need to extrapolate where we have planed to draw; With so much as a 3cm polygon with 4 Lines & 2 edges,

We can however draw a fractal blade; For example : HellSinger from Elric Melbone.
Massive Datasets

Python Libraries Interpolation:

15 Types


JIT Compile Displacement Micromap : Interpolation & Extrapolation Policy : RS

Compress its internal geometry representations into the compressed format Just in time,
Optimizing, Allocating & de-allocating in accord with Mesh Shaders & Cache availability.

VK_NV_displacement_micromap, which for Vulkan ray-tracing can help with added detail
No Comment
VK_NV_displacement_micromap allows a displacement micromap structure to be attached to the geometry of the acceleration structure,
allow the application to compress its internal geometry representations into the compressed format ahead of time.


Our options for interpolation (don't forget Gaussian)

bsplin3val — 3d spline arbitrary derivative evaluation function
cshep2d — bidimensional cubic shepard (scattered) interpolation
eval_cshep2d — bidimensional cubic shepard interpolation evaluation
interp — cubic spline evaluation function
interp1 — 1D interpolation in nearest, linear or spline mode
interp2d — bicubic spline (2d) evaluation function
interp3d — 3d spline evaluation function
interpln — linear interpolation
linear_interpn — n dimensional linear interpolation
lsq_splin — weighted least squares cubic spline fitting
mesh2d — Triangulation of n points in the plane
smooth — smoothing by spline functions
splin — cubic spline interpolation
splin2d — bicubic spline gridded 2d interpolation
splin3d — spline gridded 3d interpolation


2D-3D Spline Interpolations with background complementary colour layer smooth blend

Right on the kindle paper white 2D Spline is good for a single layer, 3D Spline is good if you rasterize a shader behind the text and shade it: The method would not cost over 1% of processing power on a 2 core ARM 400Mhz, If the image is relatively static.

On full Colour HDR WebBrowser, The 3D Spline method makes sense with complementary colour blending...
On mostly static content; 3% of total page processing costs.
On mostly Static Text with mobile images a combination of 2D & 3D Spline; 7% to 15% of cost.

interp2d — bicubic spline (2d) evaluation function
interp3d — 3d spline evaluation function

Rupert S

Specification for Open Compute & Gaussian Interpolation & JIT Compile
Displacement Micromap : Interpolation & Extrapolation Policy : RS,,,,


Texture Compressors

To Compress using CPU/GPU: MS-OpenCL

PoCL Source & Code


The Smart-access

[Innate Compression, Decompression, QoS To Optimise the routing, Task Management To optimise the process] : Task Managed Transfer : DMA:PIO : Transparent Task Sharing Protocols

The following is the initiation of the Smart-access Age

QoS To Optimise the routing:Task Management To optimise the process

Transparent Task Sharing Protocols

Innate Compression, Decompression

Examples of compression

Thursday, February 23, 2023

PM-QoS - Processor Model QoS Tree for TCP, UDP & QUICC

Quality of Service Protocol & the TCP & UDP & QUICC Protocols : RS

Extremely good for HDMI & DisplayPort & USB/URT & 2.4G/Bluetooth : In regards to Codec development and flow & device control,
Audio, Video, Process & Command

Congress - Congestion Control - Combined Network QOS Routing Table Tree-Swarm - Quality of Service Protocol & the TCP & UDP & QUICC Protocols


Processor Model for TCP, UDP & QUICC : (c)RS

To put TCP, UDP & QUICC in a proper place in your minds for application,
Think about Applying them to processors; Particularly Neuromorphic, ML & GPU/CPU!

How exactly?

Address space modelling for data transfer:
Between RAM, HDD/SDD & CPU & Internally mapping across cache & Sparse Model NAND Gates.

In the situation internal to Device Gates & Logic Circuits; We map address spaces across the processor,
We internalize the location logic as a network & utilise TCP, UDP & QUICC,

We do not need the sending strategy of Data Transfer to be Random; Random wastes Bandwidth!
But we do need a QOS Data Transfer policy & Networking Tactics!

Why ? Not all processor functions are directly connected in MultiChip & 3D Model Processor.


By thinking about the Processor Model for TCP, UDP & QUICC : (c)RS

We soon find the best light TCP, UDP & QUICC Network Strategy.

Think about this model designing the Network Protocols



"Kevin Cisco-Kevin

Date: Tue, 21 Feb 2023 08:32:03 -0800

Subject: Re: To think about the Network Model : Processor Model for TCP, UDP & QUICC : (c)RS

What we really need is a transfer layer mechanism modeled after Swarm

where packets are broken up into chunks and reassembled after

handshaking. But we don't live in that world."

Kevin Suggests we think about Swarm : RS : What do i think on average (Swarm)

PM-QoS - Swarm : Networking TCP UDP QUICC NTP DNS

I think that Swarm; Multi Target Networking is a primary method under consideration for QUICC & UDP & NTP Responses,

Swarm is high noise; High Volume Send & Receive,
With alteration though Statistical & Machine rout optimisation... That bandwidth cost reduces,
ML : Neural network, Send, Receive & Confirm, Swarm, In effect on globally predictable commodities such as:

NTP, DNS (popular), News & Decentralised command...

Can work! Network Command requires directly applied logic; What i mean is : Confirmed Command & Reception affirmation & Action!

So i propose the following:

Combined Network QOS Routing Table Tree-Swarm : CNetQSRT-Tree-Sw : Rupert S 2023-02

QOS Applied to QUIC, TCP, UDP Data packet Anagrams

What I mean is that QUIC is a protocol that passes data through multiple network adapters like a tree,
What we do is send information on the data transfer abilities of each adapter (locally) & prefer a route,
We prioritise routes based on data flow statistics & choose thereby optimum routes...

By Statistically collating data locally (in network adapter group, per localised network...

We will further select a route based on those statistics; Machine Learning is not obligatory & hence there is less to go wrong,

Routers do not need to be as modern & We can collect that information for routing tables & Create Optimum routes; Like a tree; With little need for control or modification...

All TCP, UDP & QUIC & NTP & DNS packets get to the required destination fast & with low latency.

QOS is clearly of advantage to QUIC, Because we can assess the data throughput of the modems/Network adapters & change routes? 
For optimum performance & minimum error or work.

Swarm:ML (Known Receiver : Known Sender)

DNS Global Submit

Network Tunnelling, For example: Torado, Large Download Acceleration

Secure Network Tunnelling, For example: VPN, VPS, Ethernet, 3G, 4G LTE, Volt, 5G Volt, Telecommunications Networking & GPS)

Defined routing with QOS Network optimisation (Localised) & Data bandwidth data (Localised)

Global Zone routing through tables...

Statistic Enhanced Routing & Delivery


QOS : Quality Of Service protocol : RS

Personally I believe QOS : Quality Of Service protocol be introduced
to all network traffic,
Primarily the Point A to point Z route needs planning first.

QOS Datagram
Network throughput Capacity of the network card
Advertise Capacity in local network
Plan routes based on network capacity

So the Quality Of Service Protocol needs to send a datagram to the
network adapter of site:

A to Z

A list of local routes needs to be cached & prioritised based on
Network point A's network capacity & priority,

The rout needs Point A to Z mapped & Z to A

We then send data with a packet listing preferred routes

[QOS][Origin : Target][Preferred route list forward sent][Network Performance Metric Packet]

[Origin : Target][Preferred route list forward sent][Semi Static Route Tunnel]

[Packet ID][Origin : Target][Data Packet]

Searching for a route with every packet costs processor Cycles; So
preferred routes need to be tunnelled & Secured with TLS

Rupert S

Code Speed


Example of a Secure Tunnel System:

TLS Optimised

Ethernet Security


Suitable for codec, Texture, Video Element, Firmware & ROM, Executable, Storage & RAM, DLL & Library runtimes, CSS & JS & HDMI & DisplayPort VESA Specifications :

Install and maintain as provided HPC Pack, Exactly as presented with nodes & functions; Be as generous as you can towards our research goals.



PM-QoS - Processor Model QoS Tree for TCP, UDP & QUICC

The Method of PM-QoS Roleplayed in a way that Firmware & CPU Prefetch ML Coders can understand.


Multiple Busses &or Processor Features in an Open Compute environment with competitive task scheduling

[Task Scheduler] Monticarlo-Workload-Selector

We prioritise data traffic by importance & Need to ensure that all CPU Functions are used...

In the case of a Chiplet GPU We need to assign function groups to CU & QoS is used to asses available Multiple BUSS Capacities over competing merits,
[Merits : Buss Data Capacity, Buss Cycles, Available Features, Function Endpoint]

PM-QoS is a way of Prioritising Buss traffic to processor functions & RAM & Storage Busses that:

States a data array such as:

Buss Width

divisibility ((Example) Where you transform a 128Bit buss into 32Bit x 4 Data motions and synchronize the transfers,

Data Transfer Cycles Available

Used Data Rate / Total Data Throughput Rate = N

(c)Rupert S

**************************** Reference Ambition

Title: Specifying New Congestion Control Algorithms

Date: Fri, 17 Feb 2023 16:39:25 +0100

Title: Specifying New Congestion Control Algorithms

Document date: 2023-02-17



The IETF's standard congestion control schemes have been widely shown

to be inadequate for various environments (e.g., high-speed

networks). Recent research has yielded many alternate congestion

control schemes that significantly differ from the IETF's congestion

control principles. Using these new congestion control schemes in

the global Internet has possible ramifications to both the traffic

using the new congestion control and to traffic using the currently

standardized congestion control. Therefore, the IETF must proceed

with caution when dealing with alternate congestion control

proposals. The goal of this document is to provide guidance for

considering alternate congestion control algorithms within the IETF.

The IETF Secretariat