Heterogeneous computing refers to systems that use more than one type of processor or core. It allows integration of CPUs and GPUs on the same bus, with shared memory and tasks. This is called the Heterogeneous System Architecture (HSA). The HSA aims to reduce latency between devices and make them more compatible for programming. Programming models for HSA include OpenCL, CUDA, and hUMA. Heterogeneous computing is used in platforms like smartphones, laptops, game consoles, and APUs from AMD. It provides benefits like increased performance, lower costs, and better battery life over traditional CPUs, but discrete CPUs and GPUs can provide more power and new software models are needed.

1. HETEROGENEOUS
COMPUTING
RASHID ANSARI
170847980002
MTECH ACDS

2. INTRODUCTION
Heterogeneous computing refers to systems that use more
than one kind of processor or cores.
System gain performance with dissimilar coprocessors.
Source:- Qualcomm Snapdragon

3. Source:- Qualcomm Snapdragon

4. ISSUES WITH LEGACY GPU COMPUTE
• Multiple memory pools
• Multiple address spaces
• Expert programmers
only
Source:-AMD

5. • Two memory pools remain
• Still queue through the OS
• Still requires expert programmers
Source:-AMD
Physical Integration

6. Heterogeneous System Architecture
It allows integration of central processing units and graphics processors on the
same bus, with shared memory and tasks.
HSA is being developed by HSA Foundation.
Aim to reduce communication latency between CPUs, GPUs and other compute
devices, and make devices more compatible from a programmer’s perspective.

7. • Unified Coherent Memory enables data sharing across all
processors
• Processors architected to operate cooperatively
• Designed to enable the application to run on different processors at
different times
This is HSA
Source:-AMD

8. Source:-AMD

9. Source:-AMD

11. Source:-AMD

12. Programming for HSA
1. OpenCL
2. CUDA
3. hUMA

13. OpenCL
• OpenCL initially developed by Apple Inc.
• OpenCL is a framework for writing programs that execute across
heterogenous platforms
• Allows existing source code to target the CPU and GPU.
CUDA
• CUDA is a parallel computing platform and application programming
interface model created by Nvidia.
• It contains libraries,Compiler directives ,c,c++, and Fortran extensions
• Free but limited to only Nvidia hardware
hUMA
• hUMA stands for Heterogeneous Uniform Memory Access
• Results in support for mainstream languages such as Python, C++ and Java

14. Example Platforms
Samsung Exynos
Apple “A” series
AMD APUs
ARM big.LITTLE CPU architecture

15. Real World Application of HC
AMD APUs
Smartphones
Laptops
PlayStation 3 and PlayStation 4
Xbox One

16. Pros
Small and efficient processor
Better battery life
Lower cost
Increased performance for parallel applications

17. Cons
A discrete CPU and GPU can provide more power
New software design models

18. References
1. Shan, Amar (2006). Heterogeneous Processing: a Strategy for Augmenting Moore's Law.
Linux Journal.
2. A Survey Of Techniques for Architecting and Managing Asymmetric Multicore Processors,
ACM Computing Surveys, 2015.
3. AMD
4. Wikipedia

19. THANK
YOU