The document provides an overview of graphics processing units (GPUs). It defines a GPU as a dedicated processor for computer graphics that contains hundreds of parallel execution units tailored for graphics processing. The document compares GPUs to CPUs, describing how GPUs have many parallel units while CPUs operate serially. It outlines the typical architecture of a GPU, including its pipeline from vertex processing to pixel processing to memory storage. The document also discusses how GPUs interact with CPUs and their use of dedicated video memory.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
Our unique 1U GPU servers allow you to use the latest GPUs (Tesla, GTX285, Quadro FX5800) for visualization or offloading processing in a small form factor. These are built on Intel\'s latest Nehalem processors.
Our unique 1U GPU servers allow you to use the latest GPUs (Tesla, GTX285, Quadro FX5800) for visualization or offloading processing in a small form factor. These are built on Intel\'s latest Nehalem processors.
Nowadays modern computer GPU (Graphic Processing Unit) became widely used to improve the
performance of a computer, which is basically for the GPU graphics calculations, are now used not only
for the purposes of calculating the graphics but also for other application. In addition, Graphics
Processing Unit (GPU) has high computation and low price. This device can be treat as an array of SIMD
processor using CUDA software. This paper talks about GPU application, CUDA memory and efficient
CUDA memory using Reduction kernel. High-performance GPU application requires reuse of data inside
the streaming multiprocessor (SM). The reason is that onboard global memory is simply not fast enough to
meet the needs of all the streaming multiprocessor on the GPU. In addition, CUDA exposes the memory
space within the SM and provides configurable caches to give the developer the greatest opportunity of
data reuse.
The Visual Effect Graph gives artists of all experience levels the power to create amazing particle VFX. In this intermediate-level session, Julien Fryer and Vlad Neykov from our development team will give you a sneak peek into how to generate millions of GPU-based particles in real-time using the Visual Effects Graph's toolset.
Vlad Neykov - Unity Technologies
Julien Fryer - Unity Technologies
SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. For efficient construction of large maps searching the best-matching unit is usually the computationally heaviest operation in the SOM. The parallel nature of the algorithm and the huge computations involved makes it a good target for GPU based parallel implementation. This paper presents an overall idea of the optimization strategies used for the parallel implementation of Basic-SOM on GPU using CUDA programming paradigm.
Kernel Recipes 2014 - The Linux graphics stack and Nouveau driverAnne Nicolas
The Linux graphics stack is constantly evolving to add support for new hardware. This evolution and new software specifications have forced the X graphical server to be split into several components including a now rotates in the Linux kernel, the Direct Rendering Manager (DRM). A quick presentation of these components and their role will be carried out before looking at new major change in the common code, the NVIDIA Optimus technology.
One equipped with Optimus technology laptop has two graphics processing units (GPUs), one from Intel and one from NVIDIA. This technology combines the low power Intel GPU when the machine is not used to the performance of NVIDIA GPUs when the user plays. This technology, however, is a nightmare to manage kernel-side although the final building blocks necessary for its complete management are being finalized. Further explanation of this issue will be made and we’ll see how this new software architecture has added graphics acceleration on embedded processor SoCs like Tegra.
The case of open source NVIDIA driver, called “New” will then be studied. This is the graphics driver community as it is developed without the help of NVIDIA and attracted several regular contributors, including myself! We’ll take a quick history of the project before talking about the current developments and issues related to the lack of documentation.
The end of this presentation will then be left to the participants so they can ask more general questions about the graphics stack, if they wish.
Martin Peres, Laboratoire Bordelais de Recherche en Informatique
#LibreOffice is a #free and powerful #officesuite, and a successor to #OpenOffice.org (commonly known as #OpenOffice).
Its clean interface and feature-rich tools help you unleash your #creativity and enhance your #productivity. #LibreOffice includes several applications that make it the most versatile #Free and #OpenSource office suite on the market: #Writer (#wordprocessing), Calc (#spreadsheets), Impress (presentations), #Draw (vector graphics and #flowcharts), Base (#databases), and #Math (#formula editing).
#LibreOffice is #community-driven and #developed #software, and is a project of the #nonprofit #organization, The #Document #Foundation. #LibreOffice is free and #opensource software, originally based on #OpenOffice.org (commonly known as OpenOffice), and is the most actively developed OpenOffice.org successor project.
#LibreOffice is developed by users who, just like you, believe in the principles of #FreeSoftware and in sharing their work with the world in non-restrictive ways.
This office suite can easily replace costly paid option available. If you need a good office suite which is easily and freely available you can for sure give a try and.
It has following features/components for making your work easy and cost free and vendor independent:
Writer – word processor
Calc – spreadsheet
Impress – presentations
Draw – diagrams
Base – database
Math – formula editor
Charts
Better #collaboration
#Sharingdocuments and edits with other users have been enhanced and well tracked, to make modifications more clear.
Work faster in Calc
Working with #Spreadsheet has the new #Bash-like autocompletion feature helps you to input data in a snap.
#Barcodes and borders
We can now insert #barcodes into your #documents with just a few clicks
For Full information about the release you can visit if your are interested.
https://wiki.documentfoundation.org/ReleaseNotes/7.3
If you need any help you can reach out here
https://twitter.com/libreoffice
https://blog.documentfoundation.org/
https://www.facebook.com/libreoffice.org
https://twitter.com/AskLibreOffice
What Next :
#LibreOffice 7.4 – is next major release in August, you can try installing and test it and help the developers to find if any bug or issue or need any improvement.
Let's install and explore.
We will now install it in #Ubuntu and explore this a bit
#SystemArchitecture Series: #Kerberos Architecture Component and communication flow #architecture
#Kerberos is a ticketing-based #authentication #system, based on the use of #symmetric keys. #Kerberos uses tickets to provide #authentication to resources instead of #passwords. This eliminates the threat of #password stealing via #networksniffing. One of the biggest benefits of #Kerberos is its ability to provide single sign-on (#SSO). Once you log into your #Kerberos environment, you will be automatically logged into other applications in the environment.
To help provide a secure environment, #Kerberos makes use of Mutual #Authentication. In Mutual #Authentication, both the #server and the #client must be authenticated. The client knows that the server can be trusted, and the server knows that the client can be trusted. This #authentication helps prevent man-in-the-middle attacks and #spoofing. #Kerberos is also time sensitive. The tickets in a #Kerberosenvironment must be renewed periodically or they will expire.
3. 1/29/2015 3
Why GPU?
To provide a separate dedicated graphics
resources including a graphics processor and
memory.
To relieve some of the burden of the main
system resources, namely the Central Processing
Unit, Main Memory, and the System Bus, which
would otherwise get saturated with graphical
operations and I/O requests.
5. 1/29/2015 5
What is a GPU?
A Graphics Processing Unit or GPU (also
occasionally called Visual Processing Unit or
VPU) is a dedicated processor efficient at
manipulating and displaying computer graphics .
Like the CPU (Central Processing Unit), it is a
single-chip processor.
6. 1/29/2015 6
HOWEVER,
The abstract goal of a GPU, is to enable
a representation of a 3D world as
realistically as possible. So these GPUs are
designed to provide additional
computational power that is customized
specifically to perform these 3D tasks.
7. 1/29/2015 7
GPU vs CPU
A GPU is tailored for highly parallel operation
while a CPU executes programs serially.
For this reason, GPUs have many parallel
execution units , while CPUs have few execution
units .
GPUs have singificantly faster and more
advanced memory interfaces as they need to
shift around a lot more data than CPUs.
GPUs have much deeper pipelines (several
thousand stages vs 10-20 for CPUs).
11. 1/29/2015 11
GPU Architecture
How many processing units?
– Lots.
How many ALUs?
– Hundreds.
Do you need a cache?
What kind of memory?
12. 1/29/2015 12
GPU Architecture
How many processing units?
– Lots.
How many ALUs?
– Hundreds.
Do you need a cache?
– Sort of.
What kind of memory?
13. 1/29/2015 13
GPU Architecture
How many processing units?
– Lots.
How many ALUs?
– Hundreds.
Do you need a cache?
– Sort of.
What kind of memory?
– very fast.
15. 1/29/2015 15
The GPU pipeline
The GPU receives geometry information
from the CPU as an input and provides a
picture as an output
Let’s see how that happens…
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
17. 1/29/2015 17
Host Interface
The host interface is the communication
bridge between the CPU and the GPU.
It receives commands from the CPU and also
pulls geometry information from system memory.
It outputs a stream of vertices in object space
with all their associated information (texture
coordinates, per vertex color etc) .
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
18. 1/29/2015 18
Vertex Processing
The vertex processing stage receives vertices
from the host interface in object space and
outputs them in screen space
This may be a simple linear transformation, or a
complex operation involving morphing effects
No new vertices are created in this stage, and
no vertices are discarded (input/output has 1:1
mapping)
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
19. 1/29/2015 19
Triangle setup
In this stage geometry information becomes
raster information (screen space geometry is the
input, pixels are the output)
Prior to rasterization, triangles that are
backfacing or are located outside the viewing
frustrum are rejected
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
20. 1/29/2015 20
Triangle Setup (cont…..)
A pixel is generated if and only if its center is inside
the triangle
Every pixel generated has its attributes computed
to be the perspective correct interpolation of the
three vertices that make up the triangle
21. 1/29/2015 21
Pixel Processing
Each pixel provided by triangle setup is fed into
pixel processing as a set of attributes which are
used to compute the final color for this pixel
The computations taking place here include
texture mapping and math operations
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
22. 1/29/2015 22
Memory Interface
Pixel colors provided by the previous stage are
written to the framebuffer
Used to be the biggest bottleneck before pixel
processing took over
Before the final write occurs, some pixels are
rejected by the zbuffer .On modern GPUs z is
compressed to reduce framebuffer bandwidth
(but not size).
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
23. 1/29/2015 23
Programmability in GPU pipeline
In current state of the art GPUs, vertex and
pixel processing are now programmable
The programmer can write programs that are
executed for every vertex as well as for every
pixel
This allows fully customizable geometry and
shading effects that go well beyond the generic
look and feel of older 3D applications
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
24. 1/29/2015 24
GPU Pipelined Architecture
(simplified view)
Frame
buffer
Pixel
Shader
Texture
Storage +
Filtering
Rasterizer
Vertex
Shader
Vertex
Setup
C
P
U
Vertices Pixels
GPU
…110010100100…
25. 1/29/2015 25
GPU Pipelined Architecture
(simplified view)
GPU
One unit can limit the speed of the pipeline…
Frame
buffer
Pixel
Shader
Texture
Storage +
Filtering
Rasterizer
Vertex
Shader
Vertex
Setup
C
P
U
26. 1/29/2015 26
CPU/GPU interaction
The CPU and GPU inside the PC work in
parallel with each other
There are two “threads” going on, one for
the CPU and one for the GPU, which
communicate through a command buffer:
CPU writes commands here
GPU reads commands from here
Pending GPU commands
27. 1/29/2015 27
CPU/GPU interaction (cont)
If this command buffer is drained empty,
we are CPU limited and the GPU will spin
around waiting for new input. All the GPU
power in the universe isn’t going to make
your application faster!
If the command buffer fills up, the CPU
will spin around waiting for the GPU to
consume it, and we are effectively GPU
limited
28. 1/29/2015 28
Synchronization issues
In the figure below, the CPU must not
overwrite the data in the “yellow” block
until the GPU is done with the “black”
command, which references that data:
CPU writes commands here
GPU reads commands from here
data
29. 1/29/2015 29
Inlining data
One way to avoid these problems is to
inline all data to the command buffer and
avoid references to separate data:
CPU writes commands here
GPU reads commands from here
However, this is also bad for performance, since we may need to copy several Mbytes
passing around a pointer
30. 1/29/2015 30
GPU readbacks
The output of a GPU is a rendered image on the
screen, what will happen if the CPU tries to read
it?
CPU writes commands here
GPU reads commands from here
Pending GPU commands
GPU must be synchronized with the CPU, ie it must drain its
entire command buffer, and the CPU must wait while this happens
31. 1/29/2015 31
GPU readbacks (cont)
We lose all parallelism, since first the CPU
waits for the GPU, then the GPU waits for
the CPU (because the command buffer
has been drained)
Both CPU and GPU performance take a
nosedive
Bottom line: the image the GPU produces
is for your eyes, not for the CPU (treat the
CPU -> GPU highway as a one way street)
33. 1/29/2015 33
Memory Hierarchy
CPU and GPU Memory Hierarchy
CPU Registers
Disk
CPU Caches
CPU Main
Memory
GPU Video
Memory
GPU Caches
GPU Constant
Registers
GPU Temporary
Registers
34. 1/29/2015 34
Where is GPU Data Stored?
– Vertex buffer
– Frame buffer
– Texture
Vertex Buffer
Vertex
Processor
Rasterizer
Fragment
Processor
Frame
Buffer(s)
Texture
35. 1/29/2015 35
CPU memory vs GPU memory
CPU GPU
Registers Read/write Read/write
Local Mem Read/write stack None
Global Mem Read/write heap Read-only during
computation.
Write-only at end (to
pre-computed
address)
Disk Read/write disk None
38. 1/29/2015 38
New…..
NVIDIA's new graphics processing unit,
the GeForce 8X ULTRA, said to represent
the very latest in visual effects
technologies.