Flynn’s Classification
Parallel computing is a computing where the jobs are broken into discrete
parts that can be executed concurrently. Each part is further broken down to a
series of instructions. Instructions from each part execute simultaneously on
different CPUs. Parallel systems deal with the simultaneous use of multiple
computer resources that can include a single computer with multiple
processors, a number of computers connected by a network to form a parallel
processing cluster or a combination of both.
Parallel systems are more difficult to program than computers with a single
processor because the architecture of parallel computers varies accordingly
and the processes of multiple CPUs must be coordinated and synchronized.
The crux of parallel processing are CPUs. Based on the number of instruction
and data streams that can be processed simultaneously, computing systems
are classified into four major categories:
Flynn’s classification –
1. Single-instruction, single-data (SISD) systems –
An SISD computing system is a uniprocessor machine which is capable of
executing a single instruction, operating on a single data stream. In SISD,
machine instructions are processed in a sequential manner and computers
adopting this model are popularly called sequential computers. Most
conventional computers have SISD architecture. All the instructions and data
to be processed have to be stored in primary memory.
The speed of the processing element in the SISD model is
limited(dependent) by the rate at which the computer can transfer
information internally. Dominant representative SISD systems are IBM PC,
workstations.
2. Single-instruction, multiple-data (SIMD) systems –
An SIMD system is a multiprocessor machine capable of executing the same
instruction on all the CPUs but operating on different data streams.
Machines based on an SIMD model are well suited to scientific computing
since they involve lots of vector and matrix operations. So that the
information can be passed to all the processing elements (PEs) organized
data elements of vectors can be divided into multiple sets(N-sets for N PE
systems) and each PE can process one data set.
Dominant representative SIMD systems is Cray’s vector processing
machine.
3. Multiple-instruction, single-data (MISD) systems –
An MISD computing system is a multiprocessor machine capable of
executing different instructions on different PEs but all of them operating on
the same dataset .
Example Z = sin(x)+cos(x)+tan(x)
The system performs different operations on the same data set. Machines
built using the MISD model are not useful in most of the application, a few
machines are built, but none of them are available commercially.
4. Multiple-instruction, multiple-data (MIMD) systems –
An MIMD system is a multiprocessor machine which is capable of executing
multiple instructions on multiple data sets. Each PE in the MIMD model has
separate instruction and data streams; therefore machines built using this
model are capable to any kind of application. Unlike SIMD and MISD
machines, PEs in MIMD machines work asynchronously.
MIMD machines are broadly categorized into shared-memory
MIMD and distributed-memory MIMD based on the way PEs are coupled to
the main memory.
In the shared memory MIMD model (tightly coupled multiprocessor
systems), all the PEs are connected to a single global memory and they all
have access to it. The communication between PEs in this model takes
place through the shared memory, modification of the data stored in the
global memory by one PE is visible to all other PEs. Dominant representative
shared memory MIMD systems are Silicon Graphics machines and
Sun/IBM’s SMP (Symmetric Multi-Processing).
In Distributed memory MIMD machines (loosely coupled multiprocessor
systems) all PEs have a local memory. The communication between PEs in
this model takes place through the interconnection network (the inter
process communication channel, or IPC). The network connecting PEs can
be configured to tree, mesh or in accordance with the requirement.
The shared-memory MIMD architecture is easier to program but is less
tolerant to failures and harder to extend with respect to the distributed
memory MIMD model. Failures in a shared-memory MIMD affect the entire
system, whereas this is not the case of the distributed model, in which each
of the PEs can be easily isolated. Moreover, shared memory MIMD
architectures are less likely to scale because the addition of more PEs leads
to memory contention. This is a situation that does not happen in the case of
distributed memory, in which each PE has its own memory. As a result of
practical outcomes and user’s requirement , distributed memory MIMD
architecture is superior to the other existing models.
