7/14/10 1 | Heterogeneous Computing -> Fusion | June 2010 1 Heterogeneous Computing -> Fusion Phil Rogers AMD Corporate Fellow | Heterogeneous Computing -> Fusion | June 2010 2 Definitions Heterogenous Computing – A system comprised of two or more compute engines with signficant structural differences – In our case, a low latency x86 CPU and a high throughput Radeon GPU Fusion – Bringing together two or more components and joining them into a single unified whole – In our case, combining CPUs and GPUs on a single silicon die for higher performance and lower power | Heterogeneous Computing -> Fusion | June 2010 3 AMD Balanced Platform Advantage Other Highly Parallel Workloads Graphics Workloads Serial/Task-Parallel Workloads CPU is ideal for scalar processing Out of order x86 cores with low latency memory access Optimized for sequential and branching algorithms Runs existing applications very well GPU is ideal for parallel processing GPU shaders optimized for throughput computing Ready for emerging workloads Media processing, simulation, natural UI, etc | Heterogeneous Computing -> Fusion | June 2010 4 Three Eras of Processor Performance Single-Core Era Single-thread Performance ? Time we are here o Enabled by: Moore’s Law Voltage Scaling MicroArchitecture Constrained by: Power Complexity Multi-Core Era Throughput Performance Time (# of Processors) we are here o Enabled by: Moore’s Law Desire for Throughput 20 years of SMP arch Constrained by: Power Parallel SW availability Scalability Heterogeneous Systems Era Targeted Application Performance Time (Data-parallel exploitation) we are here o Enabled by: Moore’s Law Abundant data parallelism Power efficient GPUs Temporarily constrained by: Programming models Communication overheads
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7/14/10
1
| Heterogeneous Computing -> Fusion | June 2010 1
Heterogeneous Computing -> Fusion
Phil Rogers AMD Corporate Fellow
| Heterogeneous Computing -> Fusion | June 2010 2
Definitions
Heterogenous Computing
– A system comprised of two or more compute engines with signficant structural differences
– In our case, a low latency x86 CPU and a high throughput Radeon GPU
Fusion
– Bringing together two or more components and joining them into a single unified whole
– In our case, combining CPUs and GPUs on a single silicon die for higher performance and lower power
| Heterogeneous Computing -> Fusion | June 2010 3
AMD Balanced Platform Advantage
Other Highly Parallel Workloads
Graphics Workloads
Serial/Task-Parallel Workloads
CPU is ideal for scalar processing
Out of order x86 cores with low latency memory access
Optimized for sequential and branching algorithms
Runs existing applications very well
GPU is ideal for parallel processing
GPU shaders optimized for throughput computing
Ready for emerging workloads
Media processing, simulation, natural UI, etc
| Heterogeneous Computing -> Fusion | June 2010 4
Three Eras of Processor Performance
Single-Core Era
Sin
gle-
thre
ad P
erfo
rman
ce
?
Time
we are here
o
Enabled by: Moore’s Law Voltage Scaling MicroArchitecture
Constrained by: Power Complexity
Multi-Core Era
Thro
ughp
ut P
erfo
rman
ce
Time (# of Processors)
we are here
o
Enabled by: Moore’s Law Desire for Throughput 20 years of SMP arch
Constrained by: Power Parallel SW availability Scalability
Heterogeneous Systems Era
Targ
eted
App
licat
ion
P
erfo
rman
ce
Time (Data-parallel exploitation)
we are here
o
Enabled by: Moore’s Law Abundant data parallelism Power efficient GPUs
Temporarily constrained by: Programming models Communication overheads
7/14/10
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| Heterogeneous Computing -> Fusion | June 2010 5
Emerging Application Spaces
Category Characteristics Application Examples
Massive Data Mining
Full 64b addressing Huge data sets New data types
Image, Video, Audio processing Pattern analytics and search
Natural User Interfaces
Massive “behind-the-scenes”
computing
Face and gesture recognition Real time video & audio proc Physical world interpretation