GPU
.why gpu existed why we needed the gpu what GPU can do that CPU cant ?
Ans :- in pre 1970 in cli era where computer only show u the charcter cpu dotn have to show any img or pixel very little bit pixel ( a to z ; 0–9 ) only that pixel
Then comes gui era 1970–1990 here computer will show image but only in 2d think like square here only height and breadth matter
On your 2D Flat Table:
You only have two axes: Width ($X$) and Breadth ($Y$). As you perfectly noted, everything must touch the surface of the table. Because everything is flat against the table, the CPU’s memory layout only needs to track a simple 2D index ($X, Y$).
Inside your 3D Cube Box:
You add the critical third dimension: Height ($Z$). Your description of a big cube containing many small cubes inside it is exactly what engineers call a Voxel Grid or 3D Coordinate Space.
- Because you have height ($Z$), an object can be suspended completely “in mid-air” inside the box.
- It can be hanging in the top-left-back corner, or floating in the bottom-right-front corner.
- To know where that object is, the computer cannot just look down at the floor of the table. It has to calculate its relationship to the top, bottom, left, right, front, and back sides of the box simultaneously.
So earlier in 2d era ( breadth, width ); [x,y] but in 3d era [ height, width,breadth ]; [ x,y,z]
n the 3D cube world, if you want to turn or rotate that cube box even slightly, every single one of those tiny internal cubes changes its position in space relative to your eye. To calculate how those objects move, slide, hide behind one another, and catch light from a virtual light bulb hanging inside the box, the system has to run massive chains of three-dimensional multiplication equations (X x Y x Z ) for every single vertex point, 60 times a second.
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2.just like ram is made of (transisitor + capicitor ), ssd (Insulated Floating-Gate Transistors), cpu has made of (transiitor) what gpus are made of and how many transisior ?
Ans:-GPUs are made of like cpu many transiitor only
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3.internals compoents of GPUs and their uses? In a single core GPUs campare with CPUs
Ans:- gpu has 128 FPU + 64 ALU + 1 PC + 1 INSTRUCION REGISTER + MANY REGISTER ALL COMBINED IN 1 UNIT
unlike cpu who has 1 fpu,1alu and 18 different component see in cpu section
Its like cpu and gpu both have power to do things
CPU= government + burecurecy
GPU=hardcore army
Think of a CPU core like an elite luxury car. It has its own steering wheel (Control Unit), its own GPS (Program Counter), its own dashboard metrics (PSW), and a very powerful engine (ALU). If you have an Octa-core CPU, you have 8 independent cars driving on the road.
A GPU cluster is a massive transit bus. It has one steering wheel (Control Unit), one driver (Program Counter), and one dashboard (PSW) — but it carries 256 passengers (the ALUs/FPUs) all going to the exact same destination at the exact same millisecond.
If you want to move 8 high-ranking government officials to 8 different secret meetings across the city, you use the 8 CPU cars. But if you want to move 10,000 workers to the exact same factory floor to perform the exact same task, you don’t buy 10,000 cars — you buy a fleet of GPU buses.
This is why a single GPU cluster can hold hundreds of math units using fewer total control components than a multi-core CPU. It cuts away the administrative overhead of the brain and keeps only the raw physical muscle of the arithmetic switches.
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- What is VRAM and tell whole flow of data from ssd to cpu to GPU`s 128 fpu+64 alu ?
ans:- VRAM is a massive breakthrough: VRAM is not inside the actual GPU silicon chip. It is made of separate high-speed memory chips (called GDDR or HBM) welded onto the circuit board immediately next to the GPU, surrounding it like a ring of protection. They are connected by thousands of tiny microscopic copper tracks on the board to form that ultra-wide memory highway.
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- how GPU fpu and alu after modify pixels render to screen and how VRAM is crucial for whole CPU +RAM +SSD+GPU+VRAM+screen controller flow ?
Ans:-
GPU alu and fpu send ==> 1byte for 1 transistor containing 3 sub transitor ( 1byte , 1 byte, 1byte )(10010010,111100001,00011000)
Byte got tranformed into rgb value (199,20,90)
Rgb value tranformed into actaul voltage(5v,1v,3v)
Then all that volatge hit the 1 transitor containing 3 sub transisitor (5v,1v,3v )
and acording to thieer voltage value the color bulb just got lit up if low voltage then that coloe low voltage will glow if high voltage then more glow
So display screen is an active matrix of millions of microscopic transistor switches. Every fraction of a second, millions of custom-tailored voltage levels fly down the glass tracks, hitting millions of transistors simultaneously. The transistors open up to precise levels, lighting up the Red, Green, and Blue sub-pixels to form the unified image that hits your eye.
GPU alu and fpu ==>byte ==> rgb value ==> voltage ===> goes to transistor===> digital screen
1st of what is digital display screen :- a digital screen is massive cluster of transisitor which has 3 sub pixel or transisior in it for rgb
So let say u want to open a mountain veiw with green grass image which is in ur gallery app u tocch the thumbnail of that img icon then let see the whole flow ?
==>
-user touch the screen on that image thumbnail
-Screen make interuppt call to cpu with that image data
-Cpu goes to ssd and look into image and cpu made decsion that in top left and top right coridinate pixel sky blue and in bottom left+right cordinate pixel green is there so cpu write this task to VRAM
CPU writes task into VRAM. What to draw and where.
GPU reads that task from VRAM. Does all the math. Calculates all colors.
GPU writes the final result back into VRAM. Same VRAM. Now VRAM has blue at top addresses and green at bottom addresses.
Screen controller is continuously scanning VRAM. It sees new values have appeared. It reads blue at top addresses and sends blue voltage to top transistors. It reads green at bottom addresses and sends green voltage to bottom transistors.
Mountain image appears on screen.
One line summary.
VRAM is the only meeting point. CPU writes tasks into VRAM. GPU reads tasks from VRAM and writes final pixel colors back into VRAM. Screen controller reads those final pixel colors from VRAM and lights up the screen. Nobody talks to the screen directly except the screen controller. And the screen controller only ever reads from VRAM.
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6.diff between gpu and graphic card ; why pc need big graphic card and laptop has gpu chip can big is cpu have gpu as well ?
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Ans:- there are 3 stages or usages of gpu
- Normal user gpu use in mobile + laptop :- here gpu don’t have their own seprate gpu chip rather it has gpu is attached in cpu [ gpu + cpu ] 12 core ( many alu and fpu) + no vram used ram + no fan + less display engine + lives inside the cpu
- Video editor and gaming user :- here they use gpu as abrick here 140 core cluster (many more than normal alu and fpu ) + vram + fan + display engine
- Ai training gpu use :- 200 core (massive alu and fpu ) + massive vram + own fan + no display engine
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- what is nvidia cuda core like this data center has 16,000 cuda core ( 1 cuda core= 1 FPU +1 ALU ); and how gpu use come from making img to train AI data model ? And why learning math MATRIX is most important like how matrix helps GPU to do parallelism that defines the use of gpu rather then cpu ? And how VRM is neede to store milllions of matrix ?
how gpu use come from making img to train AI data model ? like what the exact moment engineer relaise this gpu could be also use to train ai model from createing millions array of byte like [10001010,11001100,00001111] (it also kind a like matrix with 3 col and 1 row )for subpixel to trainng massive many rows and many columns matrix ?
Ans:-
- 1 CUDA Core = (1 FPU + 1 ALU)
2. The Squad/Cluster (Streaming Multiprocessor / SM)
This is the “Core Cluster” we discussed earlier. In Nvidia’s real architecture, they take 128 of these individual CUDA Cores and bundle them together into one large village.
- Inside this cluster, those 128 CUDA Cores share 1 Program Counter (PC), 1 Instruction Register (IR), and a massive block of high-speed Registers.
- The Math: 1 Core Cluster (SM) = 128 CUDA Cores + 1 PC + 1 IR + Shared Registers
3. The Full GPU Chip
A flagship data center GPU (like the Nvidia H100) is built by packing 144 of these Core Clusters onto a single giant piece of silicon.
what gpu use to do b4 training the a model :- as above ; gpu is use to genrate an array of 3 byte value like this [10001010,11001100,00001111] (it also kind a like matrix with 3 col and 1 row ) then byte became rgb value [12,34,221] the it became voltage [1v,3v,5v] then all that volate hit the transisior inside the screen then color bulb lit up a/c to voltage it get if u see this its also a kind of Matrix.
How math MATRX is related with ai training :- matrix is a kind of math
2 , 4 1, 3 30,42 ; 2x1+4x7 = 30 ; 2x3+4x9=42
5, 6 x 7,9 = 47,69 ; 5x1+6x7=47 ; 5x3+6x9=69
Real life use of matrix example. A small shop selling items.
You have a shop. You sell 3 items. Rice, Oil, Sugar.
You have 2 customers. Customer 1 and Customer 2.
Matrix A. What each customer bought.
Customer 1 bought 2kg Rice, 1 bottle Oil, 3kg Sugar. Customer 2 bought 4kg Rice, 2 bottles Oil, 1kg Sugar.
Put this into a matrix. Rows are customers. Columns are items.
Rice Oil Sugar
Cust1 [ 2 1 3 ]
Cust2 [ 4 2 1 ]
This is Matrix A. Size 2 rows x 3 columns.
Matrix B. Price and tax for each item.
Each item has two numbers. Price per unit and Tax per unit.
Rice costs 50 rupees per kg and 5 rupees tax. Oil costs 100 rupees per bottle and 10 rupees tax. Sugar costs 30 rupees per kg and 3 rupees tax.
Put this into a matrix. Rows are items. Columns are Price and Tax.
Price Tax
Rice [ 50 5 ]
Oil [ 100 10 ]
Sugar [ 30 3 ]
This is Matrix B. Size 3 rows x 2 columns.
Now multiply A x B.
Result matrix will be 2 rows x 2 columns. One row per customer. One column for total price and total tax.
Customer 1 total price. 2 times 50 plus 1 times 100 plus 3 times 30. 100 plus 100 plus 90 equals 290 rupees.
Customer 1 total tax. 2 times 5 plus 1 times 10 plus 3 times 3. 10 plus 10 plus 9 equals 29 rupees.
Customer 2 total price. 4 times 50 plus 2 times 100 plus 1 times 30. 200 plus 200 plus 30 equals 430 rupees.
Customer 2 total tax. 4 times 5 plus 2 times 10 plus 1 times 3. 20 plus 20 plus 3 equals 43 rupees.
Result matrix.
Price Tax
Cust1 [ 290 29 ]
Cust2 [ 430 43 ]
So in 1 line why matrix is most used is is in a single op i get 4 things cut1 price ,cst1 tax,c2 price ,c2 tax all i have to do is get the data of everything thing assemble them in a specific order matrix then just multiply and add in a single ops whole thing is done for each cstmr
How gpu core (1 fpu and 1 alu ) do parallelism and cpu cant :-
1st of all gpu has way more fpu and alu then cpu cpu has limited core=(1 alu + 1 fpu ) and it does matrix multiplication one by one but let say matrix multpilication is happening with 10000 datapoint in matrix a 5k number and in matrix b another 5k number ; so cpu with 7–8 core does math slowly line by line ; but gpu like 16,000 core (1 core= 1 fpu + 1 alu )
Divide 10,000 numbers among 16, 000 core alu and fpu and do the same thing paralley bcoz matrix is independt math so 16,000 core will make many smaller matrix from 2 gaint matrix and do the math parealley so gpu is better then cpu in 3 sense
1.sheer number of fpu and alu i.e resposnisble for doing float /decimal number multiplication and addition like gpus 16,000 fpu and 16000 alu VS cpu 8 fpu and 8 alu like here no campariosn
2.parallel math solving
3.Memory bandwidth. VRAM feeds cores at 10 to 40 times the speed of RAM feeding CPU cores. Cores never starve for data.
How gpu VRAMS and cpu RAM are diffrenece also :-
Having 16000 cores is useless if those cores are sitting idle waiting for numbers to arrive from memory. Imagine a factory with 16000 workers but only one narrow door to bring in
raw materials. Workers will starve and wait.
CPU RAM bandwidth is roughly 50 to 80 GB per second. This is that narrow door.
GPU VRAM bandwidth is 500 to 3350 GB per second depending on GPU. This is a massive highway with thousands of lanes.
VRAM is physically different memory. It sits directly on the graphics card right next to the GPU chip. The connection between GPU chip and VRAM chips is extremely wide. HBM memory in H100 stacks memory chips vertically right on top of the GPU chip making the connection even shorter and wider.
So the real third point is this.
- Memory bandwidth. VRAM can feed numbers to 16000 cores fast enough to keep them all busy. RAM cannot feed numbers fast enough to keep even 8 CPU cores fully busy for matrix math. The bottleneck is not just storage location. It is how fast numbers travel from storage to the cores doing the math.
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