1. Single Instruction Multiple Data
SIMD
Single Instruction Multiple Data
Roughly follows Tannenbaum
Copyright © Curt Hill

2. SIMD Only successful when the data is highly parallel
Where there is a very large amount of time spent on array processing
The array element processing is somewhat independent
Such as adding corresponding array elements of two arrays
There are plenty of applications but they are specialized, usually scientific
Copyright © Curt Hill

3. Data level parallelism
Suppose, we have two arrays of 32 floating point operands and we want to add them
A single processor will go down the line summing one at a time
If it is superscalar and it has two FPUs it can do this slightly more than 16 units of time otherwise 32
Not bad but generally outperformed by array and vector processors
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4. Array Processor Single control unit that drives multiple ALUs
The ALUs usually have individual memories
In the previous case it will take units
Here if there are 32 floating point units and the vector register contains 32 slots it will take one unit
When adding two scalar variables the two would be the same speed, but when adding two array variables (length<=32) then the vector processor would be 32 times faster
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5. Why
In most applications such parallelism would be a waste, but in many scientific applications an array of size 32 is pretty small and substantial use could be made of this parallelism
An array processor is a large number of identical processors that perform the same instruction on different pieces of data
Single control unit for the many processors
Parallel memories for the parallel processors
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6. Examples Thinking Machines Connection Machine 1 and 2
ILLIAC IV was the first in the late 60s
Largely used by NASA for fluid dynamics calculations
Very large amount of parallelism in this application
Thinking Machines Connection Machine 1 and 2
Goodrich Massively Parallel Processor
MasPar MP 1 and 2
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7. Disadvantages: Hardware heavy – expensive Difficult to program
Never mass produced since they fit a niche market
Register/memory configuration is unusual
Difficult to program
Most languages have no support
High Performance FORTRAN is usual choice
Exceptional performance but only on truly parallel computations
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8. Vector processor
Essentially a normal processor, usually superscalar, heavily pipelined
What it has different are vector registers
A normal register contains a single value, either integer or floating point of some size
A vector register contains an array of these items that can be added with array arithmetic
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9. Crays Most of the early Cray super computers were vector processors
Cray also makes MIMDs
Programmed more like a regular processor
There was usually a vector load/store instruction
The number of values in a vector register was usually modest: 4-8
This made the cost more reasonable
The performance was not so lopsided for vector operations
Copyright © Curt Hill

10. Commercially
The market for these sorts of array and vector processors is very limited
There are few organizations that will always be able to utilize them
Usually national laboratories and sophisticated engineering companies
In general it is a niche market
However there are some common examples as well
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11. Intel MMX instructions
The Pentium should not be considered a vector processor
Yet it has vector operations in the MMX subset
The SSE sets extend these
These allow one 32 bit register to be considered four eight-bit registers or two 16 bit registers
This allows array processing of 8 bit pixels or 16 bit sound samples
Copyright © Curt Hill

12. GPU The graphics processing unit is the most common vector processor
The pixel manipulation present in a GPU is an ideal SIMD environment
Shading, for example, can be easily done in parallel
Lets consider one GPU: ATI Radeon HD 4870
This is now several years old
They are faster now
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13. Radeon HD 4870 There are 10 cores Each core has 256 registers
Each is a SIMD core
Each core has 256 registers
Each of these registers is actually a vector register of size 64
The contents of one of these slots is a 4 byte float
Multiply this out and it is 2.5MB of register storage
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14. Exploiting the GPU There is substantial power sitting in the GPU
If 3D moving displays (such as games) or video playing most of this power is sitting idle
A number of options are now available to use this for scientific computing
GPGPU – General Purpose computing on Graphics Processing Unit
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15. Super Computers
A number of groups have organized clusters of GPUs to achieve super computers
Example: Chinese Mole 8.5 (2011)
2200 NVIDIA Tesla GPUs
Used to simulate an H1N1 influenza virus
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16. Finally The scientific big computers are a niche market
Supercomputers have been fabricated using clusters of GPUs
This is likely the future of SIMD
Copyright © Curt Hill