1. CUDA
By: Areeb Ahmed Khan

2. Topics:
• What is CUDA?
• What is FLOPS?
• CUDA C.
• Processing flow of CUDA.
• What is GPGPU?
• An Overview of Parallel Computing.
• Serial Computing VS Parallel Computing.
• World’s Most Powerful GPU.

3. What is CUDA?
CUDA stands for “Compute Unified Device Architecture”.
It is a Parallel computing platform developed by NVIDIA (Graphics Card
Manufacturer) in 2006 and implemented in their GPUs. CUDA gives developers,
access to the instruction set and memory of the parallel computation elements in
GPUs.
CUDA enabled GPUs can be used for General Purpose Processing (GPGPU).

4. CUDA Platform:
The CUDA platform is accessible to programmers by several CUDA accelerated
libraries and by several extended standard programming languages like:
• C extended to CUDA C.
• C++ extended to CUDA C++.
• Fortran extended to CUDA Fortran
NVIDIA CUDA™ technology is the world’s only C language environment that enables
programmers and developers to write software to solve complex computational
problems in a fraction of the time by tapping into the many-core parallel processing
power of GPU's.

5. CUDA C:
The user writes a C code, while the compiler divides the code into two portions.
One portion is delivered to CPU (because CPU is best for such tasks), while the
other portion, involving extensive calculations (FLOPS), is delivered to the GPU(s),
that executes the code in parallel. Because C is a familiar programming language,
CUDA results in very steep learning curve and hence it is becoming a favorite tool
for accelerating various applications.
FLOPS:
FLOPS an acronym for “FLoating - point Operations Per Second” is a unit to measure
computer performance, useful in fields of scientific calculations that make heavy use of
floating-point calculations. For such cases it is a more accurate measure than the generic
instructions per second.

6. CUDA C Sample Code:
#include < stdio.h >
#include < cuda.h >
__global__ void kernel_function(int a, int b )
{
printf(“ The value is %d”,a*b);
}
int main( void )
{
kernel_function<<<1,1>>>(5,2);
return 0;
}

7. Processing Flow of CUDA:
• CPU copies data from main
memory to GPU memory.
• CPU instructs the processes to
GPU.
• GPU executes the processes
parallel in each core.
• CPU copies the result from GPU
memory to main memory.

8. What is GPGPU?
GPGPU stands for “General purpose Graphics processing unit” . It is an approach
which describes the use of a GPU for those computations in applications which are
generally handled by the Central processing Unit (CPU). As in CPU, there are
several no. of cores working parallel where as in a GPU there are hundred of cores
working in parallel which could be very useful if we utilize them in general
computations. It is also called GPU Accelerated Computing.

9. Parallel Computing:
It is a form of computation in which many calculations are carried out
simultaneously which results in quick completion of calculations.
All the GPUs works on parallel computing whereas in CPUs , parallel computing is in
the form of multiple cores as they can entertain many processes simultaneously for
an time efficient solution.

10. Serial Computing VS Parallel Computing
Serial Computing:
• To be run on a single computer having a single Central Processing Unit (CPU);
• A problem is broken into a discrete series of instructions.
• Instructions are executed one after another.
• Only one instruction may execute at any moment in time.
Parallel Computing:
• Problem is broken into discrete parts that can be solved concurrently
• Each part is further broken down to a series of instructions
• Instructions from each part execute simultaneously on different processors
• An overall control/coordination mechanism.

11. World’s Most Powerful GPU

12. THANK YOU !