This document discusses various classifications of parallel computer systems. It describes:
1. Flynn's taxonomy which divides systems into SISD, SIMD, MISD and MIMD based on their processing structure and instruction streams. SISD refers to traditional CPUs while MIMD allows for multiple independent instruction streams.
2. Examples of parallel architectures like the Cray-1 supercomputer, Connection Machine, and Transputers. The Cray-1 used vector processing to perform operations in parallel while Connection Machine had thousands of simple processors.
3. Different levels of parallelism from bit-level to instruction-level to job-level, with varying granularity of computation. Finer grain allows more
Flynn's taxonomy classifies computer architectures based on the number of instruction and data streams. The main categories are:
1) SISD - Single instruction, single data stream (von Neumann architecture)
2) SIMD - Single instruction, multiple data streams (vector/MMX processors)
3) MIMD - Multiple instruction, multiple data streams (most multiprocessors including multi-core)
Multiprocessor architectures can be organized as shared memory (SMP/UMA) or distributed memory (message passing/DSM). Shared memory allows automatic sharing but can have memory contention issues, while distributed memory requires explicit communication but scales better. Achieving high parallel performance depends on minimizing sequential
SIMD (single instruction, multiple data) parallel processors exploit data-level parallelism by performing the same operation on multiple data points simultaneously using a single instruction. Vector processors are a type of SIMD parallel processor that operate on 1D arrays of data called vectors. They contain vector registers that can hold multiple data elements and functional units that perform arithmetic and logical operations in a pipelined fashion on entire vectors. Array processors are another type of SIMD machine composed of multiple identical processing elements that perform computations in lockstep under the control of a single instruction unit. Early examples include the ILLIAC IV and Cray X1 supercomputers. Multimedia extensions like MMX provide SIMD integer operations to improve performance of multimedia applications.
This document discusses parallel processors, specifically single instruction multiple data (SIMD) processors. It provides details on vector processors and array processors. Vector processors utilize vector instructions that operate on arrays of data called vectors. They have vector registers, functional units, and load/store units. Array processors perform parallel computations on large data arrays using multiple identical processing elements. The document describes dedicated memory and global memory organizations for array processors. It provides examples of early SIMD machines like ILLIAC IV.
Unit IV discusses parallelism and parallel processing architectures. It introduces Flynn's classifications of parallel systems as SISD, MIMD, SIMD, and SPMD. Hardware approaches to parallelism include multicore processors, shared memory multiprocessors, and message-passing systems like clusters, GPUs, and warehouse-scale computers. The goals of parallelism are to increase computational speed and throughput by processing data concurrently across multiple processors.
Array Processors & Architectural Classification Schemes_Computer Architecture...Sumalatha A
This document discusses array processors and architectural classification schemes. It describes how array processors use multiple arithmetic logic units that operate in parallel to achieve spatial parallelism. They are capable of processing array elements and connecting processing elements in various patterns depending on the computation. The document also introduces Flynn's taxonomy, which classifies architectures based on their instruction and data streams as SISD, SIMD, MIMD, or MISD. Feng's classification and Handlers classification schemes are also overviewed.
1) Early parallel architectures included the mainframe approach using a crossbar interconnect and the minicomputer approach using a shared bus. (2) Modern architectures have converged on a distributed memory model connected by a general-purpose network. (3) Programming models have also converged but hardware organizations remain flexible to support different approaches like message passing, shared memory, data parallel and systolic.
The document discusses the importance and applications of high performance computing (HPC). It provides examples of when HPC is needed, such as to perform time-consuming operations more quickly or handle high volumes of data/transactions. It also outlines what HPC studies, including hardware components like computer architecture and networks, as well as software elements like programming paradigms and languages. Additionally, it notes the international competition around developing exascale supercomputers and some of the research areas that utilize HPC, such as finance, weather forecasting, and health care applications involving large datasets.
Parallel computing involves using multiple processing units simultaneously to solve computational problems. It can save time by solving large problems or providing concurrency. The basic design involves memory storing program instructions and data, and a CPU fetching instructions from memory and sequentially performing them. Flynn's taxonomy classifies computer systems based on their instruction and data streams as SISD, SIMD, MISD, or MIMD. Parallel architectures can also be classified based on their memory arrangement as shared memory or distributed memory systems.
Flynn's taxonomy classifies computer architectures based on the number of instruction and data streams. The main categories are:
1) SISD - Single instruction, single data stream (von Neumann architecture)
2) SIMD - Single instruction, multiple data streams (vector/MMX processors)
3) MIMD - Multiple instruction, multiple data streams (most multiprocessors including multi-core)
Multiprocessor architectures can be organized as shared memory (SMP/UMA) or distributed memory (message passing/DSM). Shared memory allows automatic sharing but can have memory contention issues, while distributed memory requires explicit communication but scales better. Achieving high parallel performance depends on minimizing sequential
SIMD (single instruction, multiple data) parallel processors exploit data-level parallelism by performing the same operation on multiple data points simultaneously using a single instruction. Vector processors are a type of SIMD parallel processor that operate on 1D arrays of data called vectors. They contain vector registers that can hold multiple data elements and functional units that perform arithmetic and logical operations in a pipelined fashion on entire vectors. Array processors are another type of SIMD machine composed of multiple identical processing elements that perform computations in lockstep under the control of a single instruction unit. Early examples include the ILLIAC IV and Cray X1 supercomputers. Multimedia extensions like MMX provide SIMD integer operations to improve performance of multimedia applications.
This document discusses parallel processors, specifically single instruction multiple data (SIMD) processors. It provides details on vector processors and array processors. Vector processors utilize vector instructions that operate on arrays of data called vectors. They have vector registers, functional units, and load/store units. Array processors perform parallel computations on large data arrays using multiple identical processing elements. The document describes dedicated memory and global memory organizations for array processors. It provides examples of early SIMD machines like ILLIAC IV.
Unit IV discusses parallelism and parallel processing architectures. It introduces Flynn's classifications of parallel systems as SISD, MIMD, SIMD, and SPMD. Hardware approaches to parallelism include multicore processors, shared memory multiprocessors, and message-passing systems like clusters, GPUs, and warehouse-scale computers. The goals of parallelism are to increase computational speed and throughput by processing data concurrently across multiple processors.
Array Processors & Architectural Classification Schemes_Computer Architecture...Sumalatha A
This document discusses array processors and architectural classification schemes. It describes how array processors use multiple arithmetic logic units that operate in parallel to achieve spatial parallelism. They are capable of processing array elements and connecting processing elements in various patterns depending on the computation. The document also introduces Flynn's taxonomy, which classifies architectures based on their instruction and data streams as SISD, SIMD, MIMD, or MISD. Feng's classification and Handlers classification schemes are also overviewed.
1) Early parallel architectures included the mainframe approach using a crossbar interconnect and the minicomputer approach using a shared bus. (2) Modern architectures have converged on a distributed memory model connected by a general-purpose network. (3) Programming models have also converged but hardware organizations remain flexible to support different approaches like message passing, shared memory, data parallel and systolic.
The document discusses the importance and applications of high performance computing (HPC). It provides examples of when HPC is needed, such as to perform time-consuming operations more quickly or handle high volumes of data/transactions. It also outlines what HPC studies, including hardware components like computer architecture and networks, as well as software elements like programming paradigms and languages. Additionally, it notes the international competition around developing exascale supercomputers and some of the research areas that utilize HPC, such as finance, weather forecasting, and health care applications involving large datasets.
Parallel computing involves using multiple processing units simultaneously to solve computational problems. It can save time by solving large problems or providing concurrency. The basic design involves memory storing program instructions and data, and a CPU fetching instructions from memory and sequentially performing them. Flynn's taxonomy classifies computer systems based on their instruction and data streams as SISD, SIMD, MISD, or MIMD. Parallel architectures can also be classified based on their memory arrangement as shared memory or distributed memory systems.
This document discusses parallel processing and multiple processor architectures. It covers single instruction, single data stream (SISD); single instruction, multiple data stream (SIMD); multiple instruction, single data stream (MISD); and multiple instruction, multiple data stream (MIMD) architectures. It then discusses the taxonomy of parallel processor architectures including tightly coupled symmetric multiprocessors (SMPs), non-uniform memory access (NUMA) systems, and loosely coupled clusters. It covers parallel organizations for these different architectures.
This document discusses different types of parallel processing architectures including single instruction single data stream (SISD), single instruction multiple data stream (SIMD), multiple instruction single data stream (MISD), and multiple instruction multiple data stream (MIMD). It provides details on tightly coupled symmetric multiprocessors (SMPs) and non-uniform memory access (NUMA) systems. It also covers cache coherence protocols like MESI and approaches to improving processor performance through multithreading and chip multiprocessing.
This chapter discusses multicore computers and hardware performance issues with increasing processor complexity. It provides examples of multicore organizations from Intel, AMD, and ARM. The key points are:
1) Multicore architectures address diminishing returns from increasing clock speeds by utilizing parallelism across multiple processor cores on a chip.
2) Examples include the dual-core Intel Core Duo with shared L2 cache, and the quad-core Intel Core i7 with simultaneous multi-threading and shared L3 cache.
3) Effective use of multicore requires applications that can exploit parallelism; overhead from communication and synchronization limits performance gains from additional cores.
This document provides an introduction and overview of embedded systems and embedded system design. It discusses the following key points in 3 sentences:
1. It defines embedded systems and lists their essential components as well as characteristics including low cost, low power usage, and small size.
2. It discusses the requirements of embedded microcontroller cores including memory, ports, timers, interrupts, and serial data transfer standards to interface with real-world peripherals.
3. It also covers embedded programming, real-time operating systems, example applications, and textbooks on embedded systems design.
This chapter discusses asynchronous parallelism in MIMD systems. In asynchronous parallelism, problems are partitioned into independent subproblems that are distributed to autonomous processors. The subproblems may require communication and synchronization. MIMD systems allow for multiple instruction streams and asynchronous execution. Processors can have local memory in a loosely coupled system or shared memory in a tightly coupled system. Processes move between ready, running, and blocked states.
This document provides an overview of parallel computing models and the evolution of computer hardware and software. It discusses:
1) Flynn's taxonomy which classifies computer architectures based on whether they have a single or multiple instruction/data streams. This includes SISD, SIMD, MISD, and MIMD models.
2) The attributes that influence computer performance such as hardware technology, algorithms, data structures, and programming tools. Performance is measured by turnaround time, clock rate, and cycles per instruction.
3) A brief history of computing from mechanical devices to modern electronic computers organized into generations defined by advances in hardware and software.
This document provides an overview of distributed systems and distributed computing. It defines a distributed system as a collection of independent computers that appears as a single coherent system. It discusses the advantages and goals of distributed systems, including connecting users and resources, transparency, openness and scalability. It also covers hardware concepts like multi-processor systems with shared or non-shared memory, and multi-computer systems that can be homogeneous or heterogeneous.
This document discusses operating system structures and components. It describes four main OS designs: monolithic systems, layered systems, virtual machines, and client-server models. For each design, it provides details on how the system is organized and which components are responsible for which tasks. It also discusses some advantages and disadvantages of the different approaches. The document concludes by explaining how client-server models address issues with distributing OS functions to user space by having some critical servers run in the kernel while still communicating with user processes.
This document discusses parallel processors and multicore architecture. It begins with an introduction to parallel processors, including concurrent access to memory and cache coherency. It then discusses multicore architecture, where a single physical processor contains the logic of two or more cores. This allows increasing processing power while keeping clock speeds and power consumption lower than would be needed for a single high-speed core. Cache coherence methods like write-through, write-back, and directory-based approaches are also summarized for maintaining consistency across cores' caches when accessing shared memory.
This document provides an overview of high performance computing infrastructures. It discusses parallel architectures including multi-core processors and graphical processing units. It also covers cluster computing, which connects multiple computers to increase processing power, and grid computing, which shares resources across administrative domains. The key aspects covered are parallelism, memory architectures, and technologies used to implement clusters like Message Passing Interface.
This document discusses parallel processing and multiprocessing. It begins by explaining how processor performance is measured and how increasing clock frequency and instructions per cycle can improve performance. It then provides a taxonomy of parallel processor architectures including SISD, SIMD, MISD, and MIMD models. The MIMD model is further classified based on tightly coupled architectures like symmetric multiprocessors (SMPs) and loosely coupled clusters. Key issues for parallel processing like cache coherence and software/hardware solutions are also summarized.
This document discusses parallel processing and multiprocessing. It begins by explaining how processor performance is measured and how increasing clock frequency and instructions per cycle can improve performance. It then provides a taxonomy of parallel processor architectures including SISD, SIMD, MISD, and MIMD models. The MIMD model is further classified based on tightly-coupled and loosely-coupled systems. Symmetric multiprocessors (SMPs) and issues like cache coherence are discussed for tightly-coupled MIMD systems. Hardware and software solutions to cache coherence are also summarized.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
This document discusses different types of parallel computing architectures including vector architectures, SIMD instruction set extensions for multimedia, and graphics processing units (GPUs). It compares vector architectures to GPUs and multimedia SIMD computers to GPUs. It also covers loop level parallelism and techniques for finding data dependencies, such as using the greatest common divisor test.
This document discusses parallel computer architectures and focuses on multiple instruction multiple data (MIMD) systems. It describes tightly coupled symmetric multiprocessors (SMPs) that share memory and loosely coupled clusters that communicate over a network. SMPs have advantages of performance, availability and scalability but clusters provide greater scalability and availability through their distributed nature. Operating systems for clusters must handle failure management, load balancing and parallelizing applications across nodes.
Computer system Architecture. This PPT is based on computer systemmohantysikun0
This document discusses thread and process-level parallelism. It begins by introducing how improvements to computer performance initially came from manufacturing techniques and exploitation of instruction-level parallelism (ILP), but that ILP is now fully exploited. It states that the way to achieve higher performance now is through exploiting parallelism across multiple processes or threads. It provides examples of how individual transactions in a banking application could be executed in parallel.
This document summarizes a seminar on parallel computing. It defines parallel computing as performing multiple calculations simultaneously rather than consecutively. A parallel computer is described as a large collection of processing elements that can communicate and cooperate to solve problems fast. The document then discusses parallel architectures like shared memory, distributed memory, and shared distributed memory. It compares parallel computing to distributed computing and cluster computing. Finally, it discusses challenges in parallel computing like power constraints and programmability and provides examples of parallel applications like GPU processing and remote sensing.
This chapter discusses several MIMD parallel programming languages: Concurrent Pascal, CSP, occam, Ada, Sequent C, Linda, and Modula P. The languages extend existing sequential languages with parallel constructs like processes, monitors, channels, and tuple spaces to enable synchronization and communication between concurrent processes. Examples show implementations of a volume control program in each language to demonstrate their parallel programming features.
This chapter discusses different network structures that can be used to interconnect processors and memory in parallel computing systems. It describes static networks that use fixed direct connections and dynamic networks that use switched channels. Key network properties like node degree, diameter, average distance and bisection width are defined. Common static network topologies include trees, rings, grids and hypercubes. Dynamic networks include bus-based and multi-stage switching networks like crossbars. Performance factors like bandwidth, latency and scalability are discussed for evaluating different network designs.
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This document discusses parallel processing and multiple processor architectures. It covers single instruction, single data stream (SISD); single instruction, multiple data stream (SIMD); multiple instruction, single data stream (MISD); and multiple instruction, multiple data stream (MIMD) architectures. It then discusses the taxonomy of parallel processor architectures including tightly coupled symmetric multiprocessors (SMPs), non-uniform memory access (NUMA) systems, and loosely coupled clusters. It covers parallel organizations for these different architectures.
This document discusses different types of parallel processing architectures including single instruction single data stream (SISD), single instruction multiple data stream (SIMD), multiple instruction single data stream (MISD), and multiple instruction multiple data stream (MIMD). It provides details on tightly coupled symmetric multiprocessors (SMPs) and non-uniform memory access (NUMA) systems. It also covers cache coherence protocols like MESI and approaches to improving processor performance through multithreading and chip multiprocessing.
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2. Classifications of Parallel Systems
• 2.1 Classification of the parallel computer
systems
• 2.2 SISD: Single Instruction Single Data; The
Cray-1 Super Computer
• 2.3 MISD
• 2.4 SIMD Systems; Synchronous
parallelism>MPP (Massively Parallel Processors),
Data parallel system, DAP (The distributed
array processors) and The connection machine
• 2.5 MIMD System;Asynchronous parallelism>
Transputers, SHARC and Cray T3E
3. • 2.6 Hybrid parallel computer; systems,
Multiple-pipeline, Multiple-SIMD and
Systolic array, Waveform arrays, Very Long
Instruction Word (VLIW) and Same Program
Multiple Data (SPMD)
• 2.7 Some parameters in parallel computers;
Speedup , Efficiency, Latency and Grain
size
• 2.8 Levels of Parallelism; Bit level parallelism,
Instruction level parallelism, Procedure
level and Job or program level parallelism
• 2.9 Parallel operations; Nomadic and Dyadic
operations
4. 2.1 Classification of the parallel
computer systems
• Three different classification system will be
introduced.
– 1. Parallel computers will be classified
according to their processing structure
– 2. Classification of concurrent actions
according to their level of abstractions.
– 3. Parallel operations can be by their
arguments.
5.
6. Computer system Classification
• Flynn’s classification systems divides entire
computer world into four groups.
– 1. SISD
– 2. SIMD
– 3. MISD
– 4. MIMD
7. 2.2 SISD Systems
• Conventional Von Neumann Computer.
– Single processor executes instructions
sequentially.
– The operations are ordered in time and may be
easily traced from start to end.
– Modern uni-processor system uses some from
of pipelining and super scalar techniques.
– Pipelining introduces temporal parallelism by
allowing sequential executions of instruction to
be overlapped in time (Used multiple functional
units).
8. – The need for branching may reduce
effectiveness.
– Very long instruction words can be used to
reduce the impact of branching
– The need for branching may reduce
effectiveness.
– Very long instruction words can be used to
reduce the impact of branching
9. The Cray-1 Super Computer
• Commercial super computer with multiple
pipelines.
– Scalar and vector operations may be performed
concurrently.
– Vector processor capable of 160 Mflop.
– Well suited matrix problems.
– There are twelve functional (pipelined) units
performing addresses, scalar vector and floating
point operations.
11. – Main memory is divided into sixteen memory
bank and banks can be addressed concurrently.
– Each functional unit pipelined and accepts new
set operation in each clock periods.
– Special software is needed
– A vector compiler was developed (Fortran).
– Some dependencies removed by the
reformulating Fortran programs (Yazılım
Önemli).
12. 2.3 MISD systems
• MISD computer may consist of several instruction
units supplying similar number of processors, but
these processors all still obtain their data from
single logical source.
– This concept similar to pipelining architecture
consists of number of processors.
– Stream of data passed from one processor to the
next.
– Each processor possibly performing different
operations.
15. – A, B and C stages corresponds to
different parts of task.
– N-stage pipeline accomplished after load
phase (n-1).
– Only applicable to specific task. (For
example program loops)
– There are instruction interdependence
– List of instruction must be coordinate
with size of pipeline.
16. 2.4 SIMD System
• SISD is von Neumann and MISD covers pipeline
computer system
• Figure 2.1 (Page 5)
17. Synchronous parallelism
• Means that there is only single thread
control.
– A special processor (Master processors)
executes the program.
– Apply a master instruction over vector related
operands.
– Number of processors obeys the same
instruction in strict loc-step.
– Spatial parallelism is provided.
18. MPP (Massively parallel
processors )
• Consists of a control unit (central processor ACU)
and,
• large number of simple processors (such as bit
processors).
• Each processor is independent but only operates
on command from the control unit.
• Each processor executes same instruction on its
memory or data.
• SIMD program is always synchronous.
21. Data parallel system
• is the use of multiple functional units to apply the
same operations simultaneously to elements of a
data set.
22. DAP (The distributed array
processors)
• Consist of 4096 one bit processors,
• Arranged in 64x64 grid, each of which addressed
4 Kbits of memory.
• Two orthogonal (dik) high ways are used to
connect the rows and columns of the processing
elements.
• Registers in the control unit are aligned with the
high ways
• (Fig 3, page 6 , Alan)
23.
24. • The programmer must explicitly partition the data
to ensure efficient processor utilization.
• The size of instruction buffer is 60 words, and this
restricts the number of instructions which
constitute each loop to be executed by array of
processing elements.
• Recently DAP has been updated (DAP 500 32x32
PE and DAP 600 64x64 PE 32 Kbits of memory
is used on SUN and VAX as host computer)
25. The connection machine,
• CM-1 provides up to 64K PE (generally 4096 PE
and 32 Mbytes of memory, 2000 MIPS) , each
4Kbits of memory.
• If the number of PE specified exceeds the number
of physical PE (transparent to user) local memory
sliced and time slicing employed as necessary.
• LISP, Fortran and C.
• CM-2 (fig 4 , page 8 , Alan) 4096 PE and 2048
floating point execution chip, Glf a Gigabyte Ram.
• Data valud hold 10 Giga bytes of data.
26.
27. 2.5 MIMD Systems
• Two interesting class are SIMD (synchronous) and
MIMD (asynchronous).
•
28. Asynchronous parallelism
• Asynchronous parallelism means that there are
multiple threads of control. (data exchange, each
processor executes individual programs).
• MIMD and SIMD according to their inter
connection topology.
• (From page 8, fig 2.4 and 2.5 and 2.2 senkron)
• (Alan page 9, fig 5).
– This class (MIMD) is more general structure
and always work asynchronously).
29. • (From page 8, fig 2.4 and 2.5 and 2.2
senkron)
30. • (From page 8, fig 2.4 and 2.5 and 2.2
senkron)
31. • (From page 8, fig 2.4 and 2.5 and 2.2
senkron)
32. • (From page 8, fig 2.4 and 2.5 and 2.2
senkron)
33. – MIMD computers with shared memory
are known as tightly coupled and,
– Synchronization and information
exchange occur via memory areas which
can be addressed by different processor in
a coordinated manner.
– accesses to the same portion of shared
memory at the same time requires an
arbitration mechanism which must be
used to ensure only one processor
accesses that memory portion at a time.
34. • This problem of memory contention may restrict
the number of processors that can be
interconnected using shared memory model.
– MIMD computers without shared memory are known
as loosely coupled.
– Each PE has its own local memory.
– Synchronization and communication are much more
costly without share memory, because
– messages must be exchanged over the network.
– PE wishes to access another PE’s private memory, it
can only do so by sending a message to the appropriate
PE along the interconnection network.
44. Waveform arrays
• The central clock causes problems in large
systolic array then systolic arrays is
replaced by data flow.
45. Very Long Instruction Word
(VLIW)
• Hybrid form of pipeline and MIMD
computers.
• Parallelism is achieved unusually long
instruction format so that several arithmetic
and logic operations contained in one
instruction word.
• Compiler support is used
46. Same Program Multiple Data
(SPMD)
• Mix of SIMD and MIMD is same program
multiple data.
• Computer system is controlled by single
program then ease of SIMD and flexibility
of MIMD are combined.
• (Data değişiminde senkronizasyon
sağlanır.)
47. 2.7 Some parameters in parallel
computers
• PE, network, memory, speedup, efficiency,
latency and etc.
•
48. Speedup and Efficiency
• Two important metrics are to measure
performance of a parallel system.
• Speedup = elapsed time of a uni-processors (or
functional unit) / elapsed time of the
multiprocessors ( or functional units)
• Efficiency = speed-up X100/ number of
processors (or functional units)
• This two metrics are used to measure performance
of a parallel system.
49. Latency
• is a time measure of the communication overhead
incurred between machine sub system.
– Memory latency,
– Synchronization latency.
– communication latency.
• In general, the execution of a program may
involve combinations of these levels depend on
the application, formulation, algorithm, language,
compilation, and hardware limitations.
•
50. Grain size
• I s measured of computation involved in software
process.
– Simplest measure is to count the number of instructions
in a grain (program segment).
– Grain size commonly describes as fine medium and
coarse.
• Bit level parallelism , instruction level or
expression level parallelism, Procedure level or
subroutines or task or coroutines, program level
job, task or programs.
51. 2.8 Levels of Parallelism
• Computational granularity or level of parallelism
in programs is more fine at lower level and coarse
grained at higher levels.
52. Bit level parallelism
• Parallel executions of bit operations at bit level.
• ALU, individual bits are operated simultaneously
• Fig 2.13, page 14, brunnel
53. Expression (Instruction) level
parallelism
• Fig 2.12, page 14, brunnel
– Typical grain contain less than 20 instruction
– Advantage of fine grain computation lies in the
abundance of parallelism.
– Optimizing compiler automatically detect the
parallelism.
– Communication overhead is problem
– Simple synchronous calculations; such as
matrix calculations
55. Procedure level
• Medium grin size lees than 2000 instructions.
• inter process analysis is much more involved.
• Programmer may need to reconstruct the program
• Multi tasking is belong to this category.
• communication requirement is less in MIMD
execution mode.
• Fig 2.11 , page 13, Brunell
• Fields of Application:
56. – Real time programming,
– Control of time critical techniques; Power
plant
– Process control system
– Simultaneous control of multiple physical
components; Robot control.
– General purpose parallel processing
• Breaking down a problem in to sub-task,
which are distributed onto several
processing elements fro performance
enhancement; example figure;
57.
58. Program (Job) or level
parallelism
• Grain size is larger than ten thousand instructions.
• Multi tasking required.
• Time-sharing and space sharing multiprocessors
explores this level of parallelism.
• Processes may be queued.
• complete programs executed simultaneously.
• demands significant role of programmer and
operating system support.
• less communication.
59. • less parallelism.
• less compiler support.
• communication latency may increases.
• This delay may be hidden or tolerated by using
same.
• message-passing uses medium and coarse grain
computations.
• shared variable often used to support
communication.
• techniques (caching, profiling, multi threading).
• Figure 2.10, page 12, Brunnel
•
60.
61. • In General
– In general The finer the grain size the higher for
parallelism and the higher the communication
and scheduling.
– Fine grain provides higher degree of
parallelism but heavier communication
overhead as compared with coarse grain
computation.
• . Massive parallelism explored at the fine grain
level, such as data parallelism on SIMD and
MIMD computers.
•
62.
63. 2.9 Parallel operations
• Totally different way of viewing parallelism
comes from analyzing mathematical operations on
individual data elements or groups of data.
– Distinguish scalar or vector data and then
processing carried out parallel or sequential.
– Simple operations on vectors (addition of two
vector is a parallel operation.