2.15 Module 16: advanced architectures  (Page 2/2)

Maximum speedup of O(log n)

Maximum speedup of O(n / ln n)

• These “bounds” were based on runtime performance of applications and were not necessarily valid in all cases
• They reinforced the computer industry’s hesitancy to “get into” parallel processing

3. parallel processors

The machines are the true parallel processors (also called concurrent processors)

These paralle machines fall into Flynn’s taxonomy classes of SIMD and MIMD systems

– SIMD: Single Instruction stream and Multiple Data streams

– MIMD: Multiple Instruction streams and Multiple Data streams

Simd overview

• Single “control unit” computer and anarray of “computational” computers
• Control unit executes control-flowinstructions and scalar operations and passes vector instructions to the processor array
• Processor instruction types:

– Extensions of scalar instructions

Adds, stores, multiplies, etc. become vector operations executed in all processors concurrently

– Must add the ability to transfer vector and scalar data between processors to the instruction set -- attributes of a “parallel language”

• SIMD Examples

Vector addition

C(I) = A(I) + B(I)

Complexity O(n) in SISD systems for I=1 to n do

C(I) = A(I) + B(I)

Complexity O(1) in SIMD systems

Matrix multiply

A, B, and C are n-by-n matrices

Compute C= AxB

Complexity O(n3) in SISD systems

n2 dot products, each of which is O(n)

Complexity O(n2) in SIMD systems

Perform n dot products in parallel across M the array

Image smoothing

– Smooth an n-by-n pixel image to reduce “noise”

– Each pixel is replaced by the average of itself

and its 8 nearest neighbors

– Complexity O(n2) in SISD systems

– Complexity O(n) in SIMD systems

Pixel and 8 neighbors

Mimd systems overview

• MIMD systems differ from SIMD ones in that the “lock-step” operation requirement is removed
• Each processor has its own control unit and can execute an independent stream of

instructions

– Rather than forcing all processors to perform the same task at the same time, processors can be assigned different tasks that, when taken as a whole, complete the assigned application

• SIMD applications can be executed on an MIMD structure

– Each processor executes its own copy of the SIMD algorithm

• Application code can be decomposed into communicating processes

– Distributed simulations is a good example of a

very hard MIMD application

• Keys to high MIMD performance are

– Process synchronization

– Process scheduling

• Process synchronization targets keeping all processors busy and not suspended

awaiting data from another processor

• Process scheduling can be performed

– By the programmer through the use of parallel language constructs

Specify apriori what processes will be instantiated and where they will be

executed

– During program execution by spawning processes off for execution on available processors.

Fork-join construct in some languages

• System examples

SIMD

– Illiac IV

One of the first massively parallel systems 64 processors

– Goodyear Staran: 256 bit-serial processors

– Current system from Cray Computer Corp.uses supercomputer (Cray 3) front end coupled to an SIMD array of 1000s of processors

MIMD

– Intel hypercube series:

Supported up to several hundred CISC processors

Next-gen Paragon

– Cray Research T3D

Cray Y-MP coupled to a massive array of Dec Alphas

Target: sustained teraflop performance

4. discussions

• Problems

– Hardware is relatively easy to build

– Massively parallel systems just take massive amounts of money to build

– How should/can the large numbers of processors be interconnected

– The real trick is building software that will exploit the capabilities of the system

• Reality check:

– Outside of a limited number of high-profile applications, parallel processing is still a“young” discipline

– Parallel software is still fairly sparse

– Risky for companies to adopt parallel strategies, just wait for the next new SISD system.