ArticlePDF Available

Heterogeneous Computing ABSTRACT

Authors:

Abstract

Abstract from article on heterogenous computing. Key aspects on parallel/GPU computing
NB: Written year 2011
Heterogeneous Computing
(GPU+CPU Power) Joaquín Obregón-Cobo joaquin.obregon@gmail.com
ABSTRACT
YESTERDAY
Basic concepts you need to know to see what your CUDA GPU can do will be easy to understand with a
brief historic introduction. That includes concepts as SIMD an some references to Silicon Graphics and
the NVIDA steps to CUDA.
Today
Present situation is described here with references to NVIDIA and AMD. Clearly
stating that, in my opinion, CUDA is leading the technology. Mainly for his SIMT
architecture which has better performance and much easier and powerfull
programming interface (for allowing execution divergence with SIMT instead of
SIMD).
It is also explained here why we consider real parallel programming the GPU as
opposite to multi-core CPUs, and the different roles of them in a
“heterogeneous computing” environment. Advantages of GPU for highly
parallel problems and energy consumption are mentioned.
Now
There are two main concepts to understand what heterogeneous computing is and offers to us:
A. Difference between parallel and serial programming. As we consider CUDA the best option, we will
use it to illustrate the concept, implementing the sum of a vector:
A.1. Serial way. We write a C program which relevant part is:
vectorAdd( int *a, int *b, int *c, int n){for (i = 0 ; i < n ; i++) c[i] = a[i] + b[i]; return;};
A.2. Parallel way. Our equivalent CUDA C program is:
__global__ vectorAdd( int *a, int *b, int *c){int i = threadIdx.x;c[i] = a[i] + b[i]; return;};
B. Performance will be shown with vector sum: the addition of every element In a N sized vector. This is
done in a wide spectrum of computations, for instance in matrix operations. While explaining how to do
it and analyzing results we will see strong points of CUDA
Tomorrow
We see here what we can expect from the future, as the main role to be played by GPUs due to the
energy consumption/dissipation limitations to be faced or the focus on memory management in the
future development of GPUs and interaction between CPU and GPU.
Tomorrow has come
The release of CUDA 4.0 confirmed the focus on memory management new “flat” memory model.
Conclusions
Even for non-optimal problems speedup achieved is very important.
We have easy access to the tools we need, most with an affordable price, and it is probable that your
computer has it inside yet.
We have the opportunity for optimizing our programs to achieve two targets:
I. Execution time reduction.
II. Interactivity, “instant” interaction, a more responsive interface.
We have the opportunity to use this technology to give to the user a better experience, to face bigger or
more complex problems, to solve problems with higher precision… to obtain an advantage from our
competitors.
1
2
+
3
1
0
1
7
+
8
Fig. 5
SIMT
1
2
read
3
0
5
6
7
4
-
-
-
-
read
read
read
1
2
read
3
0
5
6
7
-
-
-
read
read
read
1
2
+
3
0
5
6
7
4
-
-
-
-
+
+
+
1
2
write
3
5
6
7
4
6
8
10
4
write
write
write
ResearchGate has not been able to resolve any citations for this publication.
ResearchGate has not been able to resolve any references for this publication.