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Contents. Introduction to Parallel Computing. Introduction to Parallel Computers. Approaches of Parallel Computing. Approaches based on Computation. Types Of parallelism. Classes Of parallel Computing. Uses Of parallel Computing. Application of parallel Computing References. Queries.
Introduction Parallel computing is a form of computation in which many instructions are
carried out simultaneously operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently (in parallel).
With the increased use of computers in every sphere of human activity, computer scientists are faced with two crucial issues today.
1.Processing has to be done faster like never before.
2. Larger or complex computation problems need to be solved.
Power consumption has been a major issue recently, as it causes a problem of processor heating.
The perfect solution is parallelism
Example Diagram
Contd.. The computational problem should be able to be broken apart
into discrete pieces of work that can be solved simultaneously.
Execute multiple program instructions at any moment in time.
Be solved in less time with multiple compute resources than with a single compute resource.
The compute resources are typically: A single computer with multiple processors/cores
An arbitrary number of such computers connected by a network
Parallel Computers: Virtually all stand-alone computers today are parallel from a hardware
perspective:
Multiple functional units (L1 cache, L2 cache, branch, prefetch, decode, floating-point, graphics processing (GPU), integer, etc.)
Multiple execution units/cores.
Multiple hardware threads.
Example Figure
IBM BG/Q Compute Chip with 18 cores (PU) and 16 L2 Cache units (L2).
Example of Network
Networks connect multiple stand-alone computers (nodes) to make larger parallel computer clusters.
Approaches To Parallel Computing
Flynn’s Taxonomy
SISD(Single Instruction Single Data).
SIMD(Single Instruction Multiple Data).
MISD(Multiple Instruction Single Data).
MIMD(Multiple Instruction Multiple Data).
1. Single Instruction Single Data(SISD): A sequential computer exploits no parallelism in either the instruction or data
streams.
Single Control Unit (CU) fetches single Instruction Stream (IS) from memory.
The CU then generates appropriate control signals to direct Single Processing Element (PE) to operate on Single Data Stream (DS) that is one operation at a time.
Examples : Uniprocessor machines like a PC or old mainframes.
2. Single Instruction Multiple Data(SIMD): A computer which exploits multiple data streams against a single instruction
stream to perform operations which may be naturally parallelized.
Examples: An Array processor, GPU( Graphic Processing Unit).
3. Multiple Instruction Single Data (MISD): Multiple instructions operate on a single data stream. Uncommon architecture
which is generally used for fault tolerance.
Heterogeneous systems operate on the same data stream and must agree on the result.
Examples: Space Shuttle flight control computer.
4. Multiple Instruction Multiple Data (MIMD): Multiple autonomous processors simultaneously executing different instructions on
different data.
Distributed systems are generally recognized to be single shared memory space or a distributed memory space.
Examples: A multi-core superscalar Processor is an MIMD processor.
Pictorial Representation:
Approaches Based On Computation
Massively Parallel
Embarrassingly Parallel
Grand Challenge Problems
Massively Parallel Systems:
It signifies the presence of many independent units or entire microprocessors, that run in parallel.
The term massive connotes hundreds if not thousands of such units. The Below figure Shows The Super Computer from 2002-2004 named
“The Earth Simulator”
Embarrassingly Parallel Systems An embarrassingly parallel system is one for which no particular
effort is needed to segment the problem into a very large number of parallel tasks.
Examples include surfing two websites simultaneously , or running two applications on a home computer.
They lie to an end of spectrum of parallelization where tasks can be readily parallelized.
No communication or very little communication between processes. Each process can do its tasks without any interaction with other
processes.
Grand Challenge Problems A grand challenge is a fundamental problem in science or
engineering, with broad applications, whose solution would be enabled by the application of high performance computing resources that could become available in the near future.
Grand Challenges were USA policy terms set as goals in the late 1980s for funding high-performance computing and communications research in part in response to the Japanese 5th Generation (or Next Generation) 10-year project.
Types Of Parallelism
Bit-Level
Instructional
Data
Task
Bit-Level Parallelism Bit-level parallelism is a form of parallel computing based on increasing processor
word size.
From the advent of very-large-scale integration (VLSI) computer chip fabrication technology in the 1970s until about 1986, advancements in computer architecture were done by increasing bit-level parallelism.
Increasing the word size reduces the number of instructions the processor must execute in order to perform an operation on variables whose sizes are greater than the length of the word.
When an 8-bit processor needs to add two 16-bit integers, it's to be done in two steps.
The processor must first add the 8 lower-order bits from each integer using the standard addition instruction.
Then add the 8 higher-order bits using an add-with-carry instruction and the carry bit from the lower order addition.
Instruction Level Parallelism Instruction-level parallelism (ILP) is a measure of how many of the
operations in a computer program can be performed simultaneously. The potential overlap among instructions is called instruction level
parallelism. There are two approaches to instruction level parallelism: Hardware. Software. Hardware level works upon dynamic parallelism whereas, the software
level works on static parallelism. The Pentium processor works on the dynamic sequence of parallel
execution but the Itanium processor works on the static level parallelism. The instructions given to a computer for processing can be divided into
groups, or re-ordered and then processed without changing the final result. This is known as instruction-level parallelism.
An Example
1. e = a + b
2. f = c + d
3. g = e * f Here, instruction 3 is dependent on instruction 1 and 2 . However, instruction 1 and 2 can be independently processed. If we assume that each operation can be completed in one unit
of time then these three instructions can be completed in a total of two units of time, giving an ILP of 3/2.
Data Level parallelism Data parallelism is a form of parallelization of computing across multiple processors
in parallel computing environments. Data parallelism focuses on distributing the data across different parallel computing
nodes. It contrasts to task parallelism as another form of parallelism. Data parallelism emphasizes the distributed (parallelized) nature of the data, as
opposed to the processing (task parallelism). Most real programs fall somewhere on a continuum between task parallelism and data parallelism.
Example: Consider a 2-processor system ( CPUS A and B) in a parallel environment, and we wish to do a task on some data ‘d’. It is possible to tell CPU A to do that task on one part of ‘d’ and CPU B on another part simultaneously, thereby reducing the duration of execution.
The data can be assigned using conditional statements. As a specific example, consider adding two matrices, in a data parallel implementation, CPU A could add all elements from the top half of the matrices, while CPU B could add all elements from the bottom half of the matrices.
Since the two processors work in parallel, the job of performing matrix addition would take one half of the time of performing the same operating in serial using one CPU alone.
Task Parallelism Task parallelism (also known as function parallelism and control
parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments.
Task parallelism focuses on distributing execution processes (threads) across different parallel computing nodes. It contrasts to data parallelism as another form of parallelism.
In a multiprocessor system, task parallelism is achieved when each processor executes a different thread (or process) on the same or different data.
The threads may execute the same or different code. In the general case, different execution threads communicate with one another as they work.
Communication usually takes place by passing data from one thread to the next as part of a workflow.
An Example As a simple example, if we are running code on a 2-processor system
(CPUs "a" & "b") in a parallel environment and we wish to do tasks "A" and "B", it is possible to tell CPU "a" to do task "A" and CPU "b" to do task 'B" simultaneously, thereby reducing the run time of the execution.
Task parallelism emphasizes the distributed (parallelized) nature of the processing (i.e. threads), as opposed to the data (data parallelism). Most real programs fall somewhere on a continuum between task parallelism and data parallelism.
Implementation Of Parallel Computing In Software
When implemented in software(or rather algorithms), the terminology calls it ‘parallel programming’.
An algorithm is split into pieces and then executed, as seen earlier. Important Points to remember: Dependencies: A typical scenario when line 6 of an algorithm is
dependent on lines 2,3,4 and 5. Application checkpoints: Just like saving the algorithm, or like creating
backup point. Automatic parallelization: Identifying dependencies and parallelizing
algorithms automatically. This has achieved limited success. When implemented in hardware, it is called as ‘parallel processing’. Typically, when a chunk of load for execution is divided for processing by
units like cores, processors, CPUs, etc.
Classes Of Parallel Computing1. Multicore Computing: A multicore processor is a processor that includes
multiple execution units ("cores") on the same chip. These processors differ from superscalar processors, which can issue multiple instructions per cycle from one instruction stream (thread); in contrast, a multicore processor can issue multiple instructions per cycle from multiple instruction streams. IBM's Cell microprocessor, designed for use in the Sony PlayStation 3, is another prominent multicore processor.
2. Symmetric Multiprocessing : A symmetric multiprocessor (SMP) is a computer system with multiple identical processors that share memory and connect via a bus. Bus contention prevents bus architectures from scaling. As a result, SMPs generally do not comprise more than 32 processors. Because of the small size of the processors and the significant reduction in the requirements for bus bandwidth achieved by large caches, such symmetric multiprocessors are extremely cost-effective, provided that a sufficient amount of memory bandwidth exists.
3. Distributed Computing : A distributed computer (also known as a distributed memory multiprocessor) is a distributed memory computer system in which the processing elements are connected by a network. Distributed computers are highly scalable.
4. Cluster Computing :A cluster is a group of loosely coupled computers that work together closely, so that in some respects they can be regarded as a single computer. Clusters are composed of multiple standalone machines connected by a network. While machines in a cluster do not have to be symmetric, load balancing is more difficult if they are not.
5. Grid Computing: Grid computing is the most distributed form of parallel computing. It makes use of computers communicating over the Internet to work on a given problem. Because of the low bandwidth and extremely high latency available on the Internet, distributed computing typically deals only with embarrassingly parallel problems. Many distributed computing applications have been created, of which SETI@home and Folding@home are the best-known examples.
Uses Of Parallel Computing Saves Time and Money. Solves Larger/Complex Problems. Provides Concurrency. In a natural world, many complex, interrelated events happen at the
same time, yet within a temporal sequence. Compared to serial computing, parallel computing is much better
suited for modelling, simulating and understanding complex, real world phenomena.
Data is processed quicker.
Applications of Parallel Computing It is used in different fields like..
Science and Engineering.
Industrial and Commercial.
Aerospace.
Medicine.
Weather Forecasting
Digital Media.
There are more than 500 fields where Parallel Computing is being used with great effect.
References:
https://computing.llnl.gov/tutorials/parallel_comp/#Whatis.
http://en.wikipedia.org/wiki/Parallel_computing. Michael Sipser’s book on Theory Of Computation. http://www.journals.elsevier.com/parallel-computing/. http://web.eecs.umich.edu/~qstout/tut/.
Queries?????
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