Parallel Terminology 2

8/14/2019 Parallel Terminology 2

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Some General Parallel Terminology

Like everything else, parallel computing has its own "jargon".

Some of the more commonly used terms associated with parallel computing are listed

below. Most of these will be discussed in more detail later.

Task

A logically discrete section of computational work. A task is typically a program or

program-like set of instructions that is executed by a processor. A taskis "an execution

path through address space". In other words, a set ofprograminstructions that are loadedinmemory. The address registers have been loaded with the initial address of the

program. At the next clock cycle, the CPU will start execution, in accord with the

program. The sense is that some part of 'a plan is being accomplished'. As long as the

program remains in this part of the address space, the task can continue, in principle,indefinitely, unless the program instructions contain ahalt,exit, orreturn.

In the computer field, "task" has the sense of a real-time application, asdistinguished fromprocess, which takes up space (memory), and execution time.Seeoperating system.

o Both "task" and "process" should be distinguished from event, which takes

place at a specific time and place, and which can be planned for in acomputer program.

In a computergraphical user interface(GUI), an event can be as

simple as a mouse click or keystroke.

Parallel Task

A task that can be executed by multiple processors safely (yields correct results). Taskparallelism (also known as function parallelism and control parallelism) is a form of

parallelization of computer code across multipleprocessorsinparallel computingenvironments. Task parallelism focuses on distributing execution processes (threads)

across different parallel computing nodes. It contrasts to data parallelism as another form

of parallelism.

Serial Execution

Execution of a program sequentially, one statement at a time. In the simplest

sense, this is what happens on a one processor machine. However, virtually allparallel tasks will have sections of a parallel program that must be executed

serially.

Parallel Execution

Execution of a program by more than one task, with each task being able to execute thesame or different statement at the same moment in time. The simultaneous use of more

than one CPU or processor core to execute a program or multiple computational threads.
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Ideally, parallel processing makes programs run faster because there are more engines

(CPUs or cores) running it. In practice, it is often difficult to divide a program in such a

way that separate CPUs or cores can execute different portions without interfering witheach other. Most computers have just one CPU, but some models have several, and multi-

core processor chips are becoming the norm. There are even computers with thousands of

CPUs.

With single-CPU, single-core computers, it is possible to perform parallel processing byconnecting the computers in a network. However, this type of parallel processing requires

very sophisticated software called distributed processing software.

Note that parallel processing differs from multitasking, in which a CPU provides the

illusion of simultaneously executing instructions from multiple different programs byrapidly switching between them, or "interleaving" their instructions.

Parallel processing is also called parallel computing. In the quest of cheaper computing

alternatives parallel processing provides a viable option. The idle time of processor cyclesacross network can be used effectively by sophisticated distributed computing software.

Pipelining

Breaking a task into steps performed by different processor units, with inputs streaming

through, much like an assembly line; a type of parallel computing. Incomputing, a

pipeline is a set of data processing elements connected in series, so that the output of one

element is the input of the next one. The elements of a pipeline are often executed in

parallel or in time-sliced fashion; in that case, some amount ofbuffer storageis often

inserted between elements. As the assembly line example shows, pipelining doesn'tdecrease the time for a single datum to be processed; it only increases the throughput of

the system when processing a stream of data.

High pipelining leads to increase of latency - the time required for a signal to propagatethrough a full pipe.

A pipelined system typically requires more resources (circuit elements, processing units,

computer memory, etc.) than one that executes one batch at a time, because its stages

cannot reuse the resources of a previous stage. Moreover, pipelining may increase thetime it takes for an instruction to finish.

Shared Memory

From a strictly hardware point of view, describes a computer architecture where allprocessors have direct (usually bus based) access to common physical memory. In a

programming sense, it describes a model where parallel tasks all have the same "picture"

of memory and can directly address and access the same logical memory locations
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regardless of where the physical memory actually exists. In computing,shared memoryis memory that may be simultaneously accessed by multiple programs with an intent to

provide communication among them or avoid redundant copies. Depending on context,programs may run on a single processor or on multiple separate processors. Using

memory for communication inside a single program, for example among its multiple

threads, is generally not referred to asshared memory.

Symmetric Multi-Processor (SMP)

Hardware architecture where multiple processors share a single address space and access

to all resources; shared memory computing. Incomputing, symmetric multiprocessing

orSMP involves amultiprocessorcomputer architecture where two or more identicalprocessors can connect to a single shared main memory. Most common multiprocessor

systems today use an SMP architecture. In the case ofmulti-core processors, the SMP

architecture applies to the cores, treating them as separate processors.

SMP systems allow any processor to work on any task no matter where the data for that

task are located in memory; with properoperating system support, SMP systems can

easily move tasks between processors to balance the workload efficiently. SMP has many

uses in science, industry, and business which often use custom-programmed software formultithreaded (multitasked) processing. However, most consumer products such as word

processors and computer games are written in such a manner that they cannot gain large

benefits from concurrent systems. For games this is usually because writing a program toincrease performance on SMP systems can produce a performance loss onuniprocessor

systems. Recently, however, multi-core chips are becoming more common in new

computers, and the balance between installed uni- and multi-core computers may change

in the coming years.

The nature of the different programming methods would generally require two separate

code-trees to support both uniprocessor and SMP systems with maximum performance.

Programs running on SMP systems may experience a performance increase even whenthey have been written for uniprocessor systems. This is because hardware interrupts that

usually suspend program execution while the kernel handles them can execute on an idle

processor instead. The effect in most applications (e.g. games) is not so much aperformance increase as the appearance that the program is running much more smoothly.

In some applications, particularlycompilers and somedistributed computingprojects,

one will see an improvement by a factor of (nearly) the number of additional processors.

In situations where more than one program executes at the same time, an SMP systemwill have considerably better performance than a uni-processor because different

programs can run on different CPUs simultaneously.

Systems programmers must build support for SMP into the operating system: otherwise,

the additional processors remain idle and the system functions as a uniprocessor system.
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In cases where an SMP environment processes many jobs, administrators often

experience a loss of hardware efficiency. Software programs have been developed to

schedule jobs so that the processor utilization reaches its maximum potential. Goodsoftware packages can achieve this maximum potential by scheduling each CPU

separately, as well as being able to integrate multiple SMP machines and clusters.

Access to RAM is serialized; this andcache coherencyissues causes performance to lag

slightly behind the number of additional processors in the system.

Distributed Memory

In hardware, refers to network based memory access for physical memory that isnot common. As a programming model, tasks can only logically "see" local

machine memory and must use communications to access memory on other

machines where other tasks are executing.CommunicationsParallel tasks typically need to exchange data. There are several ways this can be

accomplished, such as through a shared memory bus or over a network, however

the actual event of data exchange is commonly referred to as communicationsregardless of the method employed.

Synchronization

The coordination of parallel tasks in real time, very often associated withcommunications. Often implemented by establishing a synchronization point

within an application where a task may not proceed further until another task(s)

reaches the same or logically equivalent point.

Synchronization usually involves waiting by at least one task, and can thereforecause a parallel application's wall clock execution time to increase.

Granularity

In parallel computing, granularity is a qualitative measure of the ratio of

computation to communication.

Coarse: relatively large amounts of computational work are done between

communication events

Fine: relatively small amounts of computational work are done between

communication events

Parallel Overhead

The amount of time required to coordinate parallel tasks, as opposed to doing

useful work. Parallel overhead can include factors such as:

Task start-up time

Synchronizations
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Data communications

Software overhead imposed by parallel compilers, libraries, tools,

operating system, etc.

Task termination time

Massively ParallelRefers to the hardware that comprises a given parallel system - having many

processors. The meaning of "many" keeps increasing, but currently, the largestparallel computers can be comprised of processors numbering in the hundreds of

thousands.

Embarrassingly Parallel

Solving many similar, but independent tasks simultaneously; little to no need forcoordination between the tasks. Inparallel computing, an embarrassingly parallelworkload (orembarrassingly parallel problem) is one for which little or no effort is

required to separate the problem into a number of parallel tasks. This is often the case

where there exists no dependency (or communication) between those parallel tasks.[1]

Embarrassingly parallel problems are ideally suited to distributed computing and are also

easy to perform on server farms which do not have any of the special infrastructure used

in a true supercomputercluster.

Scalability

Refers to a parallel system's (hardware and/or software) ability to demonstrate a

proportionate increase in parallel speedup with the addition of more processors.Factors that contribute to scalability include:

Hardware - particularly memory-cpu bandwidths and network

communications

Application algorithm

Parallel overhead related

Characteristics of your specific application and coding

Multi-core Processors

Multiple processors (cores) on a single chip. A multi-core processor is a processing

system composed of two or more independent cores (orCPUs). The cores are typically

integrated onto a single integrated circuitdie (known as a chip multiprocessor or CMP),or they may be integrated onto multiple dies in a singlechip package. A many-coreprocessor is one in which the number of cores is large enough that traditional multi-

processor techniques are no longer efficient this threshold is somewhere in the rangeof several tens of cores and likely requires a network on chip.

Cluster Computing
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Use of a combination of commodity units (processors, networks or SMPs) to

build a parallel system. A computer cluster is a group of linkedcomputers,

working together closely so that in many respects they form a single computer.The components of a cluster are commonly, but not always, connected to each

other through fast local area networks. Clusters are usually deployed to improve

performance and/or availability over that provided by a single computer, whiletypically being much more cost-effective than single computers of comparable

speed or availability.[1

Supercomputing / High Performance Computing

supercomputer (a mainframe computer that is one of the most powerful available at a

given time). Use of the world's fastest, largest machines to solve large problems. A

supercomputer is a computerthat is at the frontline of current processing capacity,

particularly speed of calculation. Supercomputers introduced in the 1960s were designedprimarily by Seymour Crayat Control Data Corporation(CDC), and led the market into

the 1970s until Cray left to form his own company, Cray Research. He then took over the

supercomputer market with his new designs, holding the top spot in supercomputing for

five years (19851990). In the 1980s a large number of smaller competitors entered themarket, in parallel to the creation of the minicomputermarket a decade earlier, but many

of these disappeared in the mid-1990s "supercomputer market crash".

Today, supercomputers are typically one-of-a-kind custom designs produced by"traditional" companies such asCray, IBM and Hewlett-Packard, who had purchased

many of the 1980s companies to gain their experience. As of July 2009, theIBM

Roadrunner, located at Los Alamos National Laboratory, is the fastest supercomputer inthe world.

The termsupercomputeritself is rather fluid, and today's supercomputer tends to become

tomorrow's ordinary computer. CDC's early machines were simply very fastscalarprocessors, some ten times the speed of the fastest machines offered by other companies.In the 1970s most supercomputers were dedicated to running a vector processor, and

many of the newer players developed their own such processors at a lower price to enter

the market. The early and mid-1980s saw machines with a modest number of vectorprocessors working in parallel to become the standard. Typical numbers of processors

were in the range of four to sixteen. In the later 1980s and 1990s, attention turned from

vector processors to massiveparallel processing systems with thousands of "ordinary"

CPUs, some being off the shelf units and others being custom designs. Today, paralleldesigns are based on "off the shelf" server-class microprocessors, such as the PowerPC,

Opteron, orXeon, and most modern supercomputers are now highly-tuned computer

clusters using commodity processors combined with custom interconnects.

Grid computing:
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Grid computing is the most distributed form of parallel computing. It makes use of

computers communicating over theInternet to work on a given problem. Because of the

low bandwidth and extremely high latency available on the Internet, grid computingtypically deals only with embarrassingly parallel problems. Many grid computing

applications have been created, of which SETI@homeand Folding@Home are the best-

known examples.[31]

Most grid computing applications use middleware, software that sits between theoperating system and the application to manage network resources and standardize the

software interface. The most common grid computing middleware is the Berkeley Open

Infrastructure for Network Computing(BOINC). Often, grid computing software makesuse of "spare cycles", performing computations at times when a computer is idling.
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Parallel Terminology 2