Chapter 10

Er. Nawaraj Bhandari
Topic 10
Parallel Processing
Computer
Architecture

P ARALLEL P ROCESSING
 The simultaneous use of more than one CPU to execute a program.
Ideally, parallel processing makes a program run faster because there
are more engines (CPUs) running it.
 In practice, it is often difficult to divide a program in such a way that
separate CPUs can execute different portions without interfering with
each other.
 In computers, parallel processing is the processing of program
instructions by dividing them among multiple processors with the
objective of running a program in less time. In the earliest computers,
only one program ran at a time

Types of Parallel Processor Systems
 Parallel computing is an evolution of serial 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.
Flynn has classified the computer systems based on parallelism in the
instructions and in the data streams. These are:

Types of Parallel Processor Systems
1. Single instruction stream, single data stream (SISD).
2. Single instruction stream, multiple data stream (SIMD).
3. Multiple instruction streams, single data stream (MISD).
4. Multiple instruction stream, multiple data stream (MIMD).

Single instruction stream, single data
stream (SISD).
A single processor executes a single instruction stream to operate on
data stored in a single memory.
Uniprocessors fall into this category.

Single instruction stream, multiple data
stream (SIMD).
Single machine instruction controls the simultaneous execution of a
number of processing elements on a lockstep basis.
Each processing element has an associated data memory, so that
instructions are executed on different sets of data by different
processors.
Vector and array processors fall into this category

Multiple instruction, multiple data (MIMD)
stream
A set of processors simultaneously execute different instruction
sequences on different data sets.
SMPs, clusters, and NUMA systems fit into this category

Multiple instruction, single data (MISD)
stream
A sequence of data is transmitted to a set of processors, each of which
executes a different instruction sequence.
This structure is not commercially implemented

Taxonomy of Parallel Processor
Architecture

SYMMETRIC MULTIPROCESSORS
The term SMP refers to a computer hardware architecture and also to
the operating system behavior that reflects that architecture.
An SMP can be defined as a standalone computer system with the
following characteristics
1. There are two or more similar processors of comparable capability.
2. These processors share the same main memory and I/O facilities
and are inter-connected by a bus or other internal connection
scheme, such that memory access time is approximately the same
for each processor.

3. All processors share access to I/O devices, either through the same
channels or through different channels that provide paths to the same
device.
4. All processors can perform the same functions (hence the term
symmetric).
5. The system is controlled by an integrated operating system that
provides interaction between processors and their programs at the job,
task, file, and data element levels.

The operating system of an SMP schedules processes or threads
across all of the processors. An SMP organization has a number of
potential advantages over a uniprocessor organization, including the
following:
Performance: If the work to be done by a computer can be organized
so that some portions of the work can be done in parallel, then a
system with multiple processors will yield greater performance than
one with a single processor of the same type
Availability: In a symmetric multiprocessor, because all processors can
perform the same functions, the failure of a single processor does not
halt the machine. Instead, the system can continue to function at
reduced performance.

Incremental growth: A user can enhance the performance of a system
by adding an additional processor.
Scaling: Vendors can offer a range of products with different price and
performance characteristics based on the number of processors
configured in the system.

Cache Coherence and the MESI Protocol
1. In contemporary multiprocessor systems, it is customary to have
one or two levels of cache associated with each processor.
2. This organization is essential to achieve reasonable performance. It
does, however, create a problem known as the cache coherence
problem.
3. The essence of the problem is this: Multiple copies of the same data
can exist in different caches simultaneously, and if processors are
allowed to update their own copies freely, an inconsistent view of
memory can result

1. Write back: Write operations are usually made only to the cache.
Main memory is only updated when the corresponding cache line is
flushed from the cache.
2. Write through: All write operations are made to main memory as
well as to the cache, ensuring that main memory is always valid.
3. If two caches contain the same line, and the line is updated in one
cache, the other cache will unknowingly have an invalid value.
Subsequent reads to that invalid line produce invalid results. Even
with the write-through policy, inconsistency can occur unless other
caches monitor the memory traffic or receive some direct
notification of the

Cache coherence approaches have generally been divided into
software and hardware approaches. Some implementations adopt a
strategy that involves both software and hardware elements
Software Solutions:
Software cache coherence schemes attempt to avoid the need for
additional hardware circuitry and logic by relying on the compiler and
operating system to deal with the problem.
Software approaches are attractive because the overhead of detecting
potential problems is transferred from run time to compile time, and
the design complexity is transferred from hardware to software.

Hardware Solutions:
Hardware-based solutions are generally referred to as cache coherence
protocols. These solutions provide dynamic recognition at run time of
potential inconsistency conditions. Because the problem is only dealt
with when it actually arises, there is more effective use of caches,
leading to improved performance over a software approach.
In addition, these approaches are transparent to the programmer and
the compiler, reducing the software development burden.

Chapter 10

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