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COMPUTER ARCHITECTURE
MICROPROCESSOR
• IT IS ONE OF THE GREATEST
ACHIEVEMENTS OF THE 20TH CENTURY.
• IT USHERED IN THE ERA OF WIDESPREAD
COMPUTE...
EARLY ARCHITECTURE
• VON NEUMANN ARCHITECTURE, 1940
• PROGRAM IS STORED IN MEMORY.
• SEQUENTIAL OPERATION
• ONE INST IS RE...
Conventional 32 bit
Microprocessors
• Higher data throughput with 32 bit wide data bus
• Larger direct addressing range
• ...
Conventional 32 bit
Microprocessors
• More insts and addressing modes to improve software
efficiency
• More registers to s...
Conventional 32 bit
Microprocessors
• To construct a complete general purpose 32 bit
microprocessor, five basic functions ...
Conventional Architecture
• KNOWN AS VON NEUMANN ARCHITECTURE
• ITS MAIN FEATURES ARE:
 A SINGLE COMPUTING ELEMENT INCORP...
Conventional Architecture
• Single processor configuration:
PROCESSOR
MEMORY INPUT-OUTPUT
Conventional Architecture
• Multiple processor configuration with a global bus:
SYSTEM
INPUT-
OUTPUT
GLOBAL
MEMORY
PROCESS...
Conventional Architecture
• THE EARLY COMP ARCHITECTURES WERE
DESIGNED FOR SCIENTIFIC AND COMMERCIAL
CALCULATIONS AND WERE...
Conventional Architecture
• ADVANCEMENT OF TECHNOLOGY ENHANCED
THE SPEED OF EXECUTION OF PROCESSORS BUT
A SINGLE COMM PATH...
Conventional Architecture
• AS HIGHER SPEED MEMORY BECAME
AVAILABLE, THE DELAYS INTRODUCED BY THE
CAPACITANCE AND THE TRAN...
Conventional Architecture
• THE USE OF MULTIPROCESSOR BUSES WITH THE
NEED FOR ARBITRATION BETWEEN COMPUTERS
REQUESTING CON...
Conventional Architecture
• THE GLOBAL BUS AND THE GLOBAL MEMORY
CAN ONLY SERVE ONE PROCESSOR AT A TIME.
• AS MORE PROCESS...
Conventional Architecture
• TYPICALLY, ONLY 3 OR 4 TIMES THE SPEED OF A SINGLE
PROCESSOR CAN BE ACHIEVED IN MULTIPROCESSOR...
PIPELINING
• THE PROC. SEPARATES EACH INST INTO ITS BASIC
OPERATIONS AND USES DEDICATED EXECUTION UNITS
FOR EACH TYPE OF O...
PIPELINING
• TO SHOW THE USE OF A PIPELINE, CONSIDER THE
MULTIPLICATION OF 2 DECIMAL NOS: 3.8 X 102 AND
9.6X103. THE PROC....
PIPELINING
• TO GET A ROUGH INDICATION OF PERFORMANCE
INCREASE THROUGH PIPELINE, THE STAGE EXECUTION
INTERVAL MAY BE TAKEN...
PIPELINING
• NON-SEQUENTIAL INSTS CAUSE THE
INSTRUCTIONS BEHIND IN THE PIPELINE TO BE
EMPTIED AND FILLING TO BE RESTARTED....
CACHE MEMORY
• VON-NEUMANN SYSTEM PERFORMANCE IS
CONSIDERABLY EFFECTED BY MEMORY ACCESS TIME
AND MEMORY BW (MAXIMUM MEMORY...
CACHE MEMORY
• DRAM HAS A GREATER CAPACITY PER CHIP AND
A LOWER COST, BUT EVEN THE FASTEST DRAM
CAN‟T KEEP PACE WITH THE P...
CACHE MEMORY
• CACHE ACTS AS A FAST LOCAL STORAGE
BUFFER BETWEEN THE PROC AND THE MAIN
MEMORY.
• OFF-CHIP BUT ON-BOARD CAC...
CACHE MEMORY
• THE COST OF IMPLEMENTING AN ON-BOARD
CACHE IS MUCH LOWER THAN THE COST OF
FASTER SYSTEM MEMORY REQUIRED TO
...
CACHE MEMORY
• INCREASING THE LINE SIZE INCREASES THE
CHANCE THAT THERE WILL BE A CACHE HIT ON
THE NEXT MEMORY REFERENCE.
...
CACHE MEMORY
• OVERALL MEMORY PERFORMANCE IS A
FUNCTION OF CACHE ACCESS TIME, CACHE HIT
RATIO AND MAIN MEMORY ACCESS TIME ...
CACHE DESIGN
• PROCESSORS WITH DEMAND PAGED VIRTUAL
MEMORY SYSTEMS REQUIRE AN ASSOCIATIVE
CACHE.
• VIRTUAL MEM SYSTEMS ORG...
CACHE DESIGN
• WHEN ACCESSED, THE CACHE CHECKS TO SEE IF IT
CONTAINS THE PAGE ADDRESS (OR TAG FIELD); IF SO, IT
ADDS THE O...
CACHE DESIGN
• A FULLY ASSOCIATIVE CACHE ALLOWS ANY
NUMBER OF PAGES TO USE THE CACHE
SIMULTANEOUSLY.
• A CACHE REQUIRES A ...
MEMORY ARCHITECTURES
• 32 BIT PROCESSORS HAVE INTRODUCED 3 NEW
CONCEPTS IN THE WAY THE MEMORY IS
INTERFACED:
1. LOCAL MEMO...
LOCAL MEM BUS EXTENSIONS
• IT PERMITS LARGER LOCAL MEMORIES TO BE
CONNECTED WITHOUT THE DELAYS CAUSED BY BUS
REQUESTS AND ...
MEMORY INTERLEAVING
• PIPELINED PROCESSORS WITH THE ABILITY TO
GENERATE THE ADDRESS OF THE NEXT MEMORY
REFERENCE WHILE FET...
MEMORY INTERLEAVING
• ONE BOARD CAN BEGIN THE NEXT MEM CYCLE
WHILE THE OTHER BOARD COMPLETES THE
PREVIOUS CYCLE.
• THE SPE...
MEMORY INTERLEAVING
• TWO WAY INTERLEAVING MAY NOT PREVENT
MEM WAIT STATES FOR SOME FAST SIGNAL
PROCESSING APPLICATIONS AN...
Conventional Architecture
• EVEN WITH THESE ENHANCEMENTS, THE SEQUENTIAL
VON NEUMANN ARCHITECTURE REACHED THE LIMITS
IN PR...
HOW TO INCREASE THE SYSTEM SPEED?
1. USING FASTER COMPONENTS. COSTS MORE,
DISSIPATE CONSIDERABLE HEAT.
THE RATE OF GROWTH ...
HOW TO INCREASE THE SYSTEM SPEED?
2. ARCHITECTURAL METHODS:
A. USE PARALLELISM IN SINGLE PROCESSOR
[ OVERLAPPING EXECUTION...
PARALLEL COMPUTERS
• THE IDEA EMERGED AT CIT IN 1981
• A GROUP HEADED BY CHARLES SEITZ AND
GEOFFREY FOX BUILT A PARALLEL C...
PARALLEL COMPUTERS
• BESIDES HIGHER SPEED, OTHER FEATURES OF
PARALLEL COMPUTERS ARE:
 BETTER SOLUTION QUALITY: WHEN ARITH...
CLASSIFICATION OF COMPUTER
ARCHITECTURE
• FLYNN‟S TAXONOMY: IT IS BASED
UPON HOW THE COMPUTER
RELATES ITS INSTRUCTIONS TO ...
FLYNN‟S TAXONOMY
• SISD: CONVENTIONAL VON-NEUMANN SYSTEM.
CONTROL
UNIT
PROCESSOR
INST STREAM
DATA
STREAM
FLYNN‟S TAXONOMY
• SIMD: IT HAS A SINGLE STREAM OF VECTOR
INSTS THAT INITIATE MANY OPERATIONS. EACH
ELEMENT OF A VECTOR IS...
FLYNN‟S TAXONOMY
• MISD: NOT POSSIBLE
C U 1
C U 2
PU 3
PU 2
C U 3
PU 1
INST STREAM 1
INST STREAM 2
INST STREAM 3
DS
FLYNN‟S TAXONOMY
• MIMD: MULTIPROCESSOR CONFIGURATION AND
ARRAY OF PROCESSORS.
CU 1
CU 2
CU 3
IS 1
IS 2
IS 3
DS 1
DS 2
DS 3
FLYNN‟S TAXONOMY
• MIMD COMPUTERS COMPRISE OF INDEPENDENT
COMPUTERS, EACH WITH ITS OWN MEMORY,
CAPABLE OF PERFORMING SEVER...
FLYNN‟S TAXONOMY
• THIS CLASSIFICATION IS TOO BROAD.
• IT PUTS EVERYTHING EXCEPT
MULTIPROCESSORS IN ONE CLASS.
• IT DOES N...
SHORE‟S CLASSIFICATION
• SHORE CLASSIFIED THE COMPUTERS ON THE BASIS
OF ORGANIZATION OF THE CONSTITUENT ELEMENTS
OF THE CO...
SHORE‟S CLASSIFICATION
• TYPE I:
IM CU
HORIZONTAL PU
WORD SLICE DM
NOTE THAT THE PROCESSING IS
CHARACTERISED AS
HORIZONTAL...
SHORE‟S CLASSIFICATION
• MACHINE 2: SAME AS MACHINE 1 EXCEPT THAT
SM FETCHES A BIT SLICE FROM ALL THE WORDS
IN THE MEMORY ...
SHORE‟S CLASSIFICATION
• MACHINE 2:
IM
CU
VERTICAL
PU
BIT SLICE
DM
SHORE‟S CLASSIFICATION
• MACHINE 3: COMBINATION OF 1 AND 2.
• IT COULD BE CHARACTERISED HAVING A
MEMORY AS AN ARRAY OF BIT...
SHORE‟S CLASSIFICATION
• MACHINE 3:
IM
CU
VERTICAL
PU
HORIZON
TAL PU
DM
SHORE‟S CLASSIFICATION
• MACHINE 4: IT IS OBTAINED BY REPLICATING THE PU
AND DM OF MACHINE 1.
• AN ENSEMBLE OF PU AND DM I...
SHORE‟S CLASSIFICATION
• MACHINE 4:
IM
CU
PU PU PU
DM DM DM
SHORE‟S CLASSIFICATION
• MACHINE 5: SIMILAR TO MACHINE 4 WITH THE
ADDITION OF COMMUNICATION BETWEEN
PE.EXAMPLE: ILLIAC IV
...
SHORE‟S CLASSIFICATION
• MACHINE 6:
• MACHINES 1 TO 5 MAINTAIN SEPARATION
BETWEEN DM AND PU WITH SOME DATA BUS OR
CONNECTI...
SHORE‟S CLASSIFICATION
• MACHINE 6:
IM
CU
PU + DM
FENG‟S CLASSIFICATION
• FENG PROPOSED A SCHEME ON THE BASIS OF
DEGREE OF PARALLELISM TO CLASSIFY
COMPUTER ARCHITECTURE.
• ...
FENG‟S CLASSIFICATION
• BASED ON FENG‟S SCHEME, WE HAVE
SEQUENTIAL AND PARALLEL OPERATIONS AT
BIT AND WORD LEVELS TO PRODU...
HANDLER‟S CLASSIFICATION
• FENG‟S SCHEME, WHILE INDICATING THE
DEGREE OF PARALLELISM DOES NOT ACCOUNT
FOR THE CONCURRENCY ...
HANDLER‟S CLASSIFICATION
• HANDLER DEFINED SOME OF THE TERMS AS:
• PCU – PROCESSOR CONTROL UNITS
• ALU – ARITHMETIC LOGIC ...
COMPUTER PROGRAM ORGANIZATION
• BROADLY, THEY MAY BE CLASSIFIED AS:
 CONTROL FLOW PROGRAM
ORGANIZATION
 DATAFLOW PROGRAM...
COMPUTER PROGRAM ORGANIZATION
• IT USES EXPLICIT FLOWS OF CONTROL INFO TO
CAUSE THE EXECUTION OF INSTS.
• DATAFLOW COMPS U...
COMPUTER PROGRAM ORGANIZATION
• THE THREE BASIC FORMS OF COMP PROGRAM
ORGANIZATION MAY BE DESCRIBED IN TERMS
OF THEIR DATA...
COMPUTER PROGRAM ORGANIZATION
• CONTROL FLOW PROCESSORS HAVE A “BY
REFERENCE” DATA MECHANISM (WHICH USES
REFERENCES EMBEDD...
COMPUTER PROGRAM ORGANIZATION
• DATAFLOW COMPUTERS HAVE A “BY VALUE”
DATA MECHANISM (WHICH GENERATES AN
ARGUMENT AT RUN-TI...
COMPUTER PROGRAM ORGANIZATION
• TWO BASIC TYPES OF REDUCTION PROGRAM
ORGANIZATIONS HAVE BEEN DEVELOPED:
A. STRING REDUCTIO...
COMPUTER PROGRAM ORGANIZATION
• CONTROL-FLOW AND DATA-FLOW PROGRAMS
ARE BUILT FROM FIXED SIZE PRIMITIVE INSTS
WITH HIGHER ...
COMPUTER PROGRAM ORGANIZATION
• THE RELATIONSHIP OF THEDATA AND CONTROL
MECHANISMS TO THE BASIC COMPUTER
PROGRAM ORGANIZAT...
MACHINE ORGANIZATION
• MACHINE ORGANIZATION CAN BE CLASSIFIED
AS FOLLOWS:
 CENTRALIZED: CONSISTING OF A SINGLE
PROCESSOR,...
MACHINE ORGANIZATION
 PACKET COMMUNICATION: USING A CIRCULAR
INST EXECUTION PIPELINE IN WHICH
PROCESSORS, COMMUNICATIONS ...
MACHINE ORGANIZATION
• EXPRESSION MANIPULATION WHICH USES IDENTICAL
RESOURCES IN A REGULAR STRUCTURE, EACH
RESOURCE CONTAI...
MULTIPROCESSING SYSTEMS
• IT MAKES USE OF SEVERAL PROCESSORS, EACH
OBEYING ITS OWN INSTS, USUALLY
COMMUNICATING VIA A COMM...
MULTIPROCESSING SYSTEMS
• INTERCONNECTION BY A SHARED MEMORY IS LESS
TIGHTLY COUPLED AND A MULTIPORT MEMORY MAY
BE USED TO...
MULTIPROCESSING SYSTEMS
• DEGREE OF COUPLING:
NETWORK
SW
NETWORK
SW
NETWORK LINK
OS OSCLUSTER LINK
SYSTEM
MEMORY
SYSTEM
ME...
MULTIPROCESSING SYSTEMS
• MULTIPROCESSORS MAY ALSO BE CLASSIFIED
AS AUTOCRATIC OR EGALITARIAN.
• AUTOCRATIC CONTROL IS SHO...
MULTIPROCESSING SYSTEMS
• MULTIPROCESSING SYSTEMS WITH SEPARATE
PROCESSORS AND MEMORIES MAY BE
CLASSIFIED AS „DANCE HALL‟ ...
MULTIPROCESSING SYSTEMS
• DANCE HALL CONFIGURATION:
CPU 1
CPU 2
CPU 3
CPU 4
SWITCHI
NG
NETWO
RK
MEM 1
MEM 2
MEM 3
MEM 4
MULTIPROCESSING SYSTEMS
• ANOTHER CONFIGURATION IS „BOUDOIR CONFIG‟ IN WHICH EACH
PROCESSOR IS CLOSELY COUPLED WITHITS OWN...
MULTIPROCESSING SYSTEMS
• ANOTHER TERM, WHICH IS USED TO DESCRIBE A
FORM OF PARALLEL COMPUTING IS
CONCURRENCY.
• IT DENOTE...
SYSTOLIC ARRAYS
• IT MAY BE TERMED AS MISD SYSTEM.
• IT IS A REGULAR ARRAY OF PROCESSING ELEMENTS,
EACH COMMUNICATING WITH...
WAVEFRONT ARRAY
• IT IS A REGULAR ARRAY OF PROCESSING ELEMENTS,
EACH COMMUNICATING WITH ITS NEAREST
NEIGHBOURS BUT OPERATI...
WAVEFRONT ARRAY
• PROCESSING WAVEFRONTS DEVELOP ACROSS
THE ARRAY AS PROCESSORS PASS ON THE
OUTPUT DATA TO THEIR NEIGHBOUR....
GRANULARITY OF PARALLELISM
• PARALLEL PROCESSING EMPHASIZES THE USE OF
SEVERAL PROCESSING ELEMENTS WITH THE
MAIN OBJECTIVE...
GRANULARITY OF PARALLELISM
• THE PARALLEL PROCESSING EMPHASIZES THE
EXPLOITATION OF CONCURRENCY AVAILABLE
IN A PROBLEM FOR...
GRANULARITY OF PARALLELISM
• THE FOLLOWING GRANULARITIES OF PARALLELISM
MAY BE IDENTIFIED IN ANY EXISTING SYSTEM:
o PROGRA...
GRANULARITY OF PARALLELISM
• THE GRANULARITIES ARE LISTED IN THE
INCREASING DEGREE OF FINENESS.
• GRANULARITIES AT LEVELS ...
GRANULARITY OF PARALLELISM
• SINCE A PROCESS REPRESENTS A SIZABLE CODE IN
TERMS OF EXECUTION TIME, THE OVERLOADS IN
EXPLOI...
GRANULARITY OF PARALLELISM
• THE LAST THREE LEVELS ARE BEST HANDLED BY
HARDWARE. SEVERAL MACHINES HAVE BEEN BUILT TO
PROVI...
PARALLEL ARCHITECTURES
• THERE ARE NUMEROUS ARCHITECTURES THAT HAVE
BEEN USED IN THE DESIGN OF HIGH SPEED COMPUTERS.
IT FA...
PARALLEL ARCHITECTURES
• THE IMPORTANT ARCHITECTURAL IDEAS BEING
USED IN DESIGNING GEN PURPOSE HIGH SPEED
COMPUTERS ARE:
...
PARALLEL ARCHITECTURES
• THE SPECIAL PURPOSE MACHINES HAVE TO EXCEL FOR
WHAT THEY HAVE BEEN DESIGNED. IT MAY OR MAY
NOT DO...
ARRAY PROCESSORS
• IT CONSISTS OF SEVERAL PE, ALL OF WHICH EXECUTE
THE SAME INST ON DIFFERENT DATA.
• THE INSTS ARE FETCHE...
ARRAY PROCESSORS
• THERE ARE SEVERAL WAYS OF CONNECTING PE
• THESE MACHINES REQUIRE SPECIAL
PROGRAMMING EFFORTS TO ACHIEVE...
ARRAY PROCESSORS
• USING AN INTERCONNECTION NETWORK:
PE1 PE2 PE3 PE4 PEn
CU AND
SCALAR
PROCESS
OR
INTERCONNECTION
NETWORK
ARRAY PROCESSORS
• USING AN ALIGNMENT NETWORK:
PE0 PE1 PE2 PEn
ALIGNMENT NETWORK
MEM O MEM1 MEM2 MEMK
CONTROL
UNIT AND
SCA...
CONVENTIONAL MULTI-PROCESSORS
• ASYNCHRONOUS MULTIPROCESSORS
• BASED ON MULTIPLE CPUs AND MEM BANKS
CONNECTED THROUGH EITH...
CONVENTIONAL MULTI-PROCESSORS
• IN SUCH SYSTEMS, EACH CPU OPERATES
INDEPENDENTLY ON THE QUANTUM OF WORK GIVEN
TO IT
• IT H...
CONVENTIONAL MULTI-PROCESSORS
• IT WORKS WELL FOR TASKS RUNNING MORE OR
LESS INDEPENDENTLY ie., FOR TASKS HAVING
LOW COMMU...
CONVENTIONAL MULTI-PROCESSORS
• SHARED MEMORY ARCHITECTURE:
MEMORY
CPU CPU CPU
COMMON BUS ARCHITECTURE
MEM0 MEM1 MEMn
PROC...
CONVENTIONAL MULTI-PROCESSORS
• MESSAGE BASED ARCHITECTURE:
………………
PE 1 PE 2 PE n
CONNECTION NETWORK
CONVENTIONAL MULTI-PROCESSORS
• HYBRID ARCHITECTURE:
PE 1 PE 2 PE n
CONNECTION NETWORK
MEM 1 MEM 2 MEM k
CONVENTIONAL MULTI-PROCESSORS
• ON A SINGLE BUS SYSTEM, THERE IS A LIMIT ON THE
NUMBER OF PROCESSORS THAT CAN BE OPERATED ...
DATA FLOW COMPUTERS
• A NEW FINE GRAIN PARALLEL PROCESSING
APPROACH BASED ON DATAFLOW COMPUTING
MODEL HAS BEEN SUGGESTED B...
DATA FLOW COMPUTERS
• THE OPERATORS FORM THE NODES OF THE
GRAPH WHILE THE ARCS REPRESENT THE DATA
MOVEMENT BETWEEN NODES.
...
DATA FLOW COMPUTERS
• THIS IS KNOWN AS FIRING OF THE NODE.
• A NODE CAN FIRE ONLY WHEN ALL OF ITS INPUT
ARCS HAVE TOKENS A...
DATA FLOW COMPUTERS
• DATA FLOW GRAPH FOR THE COMPUTATION:
• A = 5 + C – D
5
C
D
+ -
COMPUTATION PROGRESSES
AS PER DATAAVA...
DATA FLOW COMPUTERS
• MANY CONVENTIONAL MACHINES EMPLOYING
MULTIPLE FUNCTIONAL UNITS EMPLOY THE DATA
FLOW MODEL FOR SCHEDU...
DATA FLOW COMPUTERS
• IT MAY PROVIDE AN EFFECTIVE SOLUTION FOR USING
VERY LARGE NUMBER OF COMPUTING ELEMENTS IN
PARALLEL.
...
SYSTOLIC ARCHITECTURES
• THE ADVENT OF VLSI HAS MADE IT POSSIBLE TO
DEVELOP SPECIAL ARCHITECTURES SUITABLE
FOR DIRECT IMPL...
SYSTOLIC ARCHITECTURES
• CONVENTIONAL ARCHITECTURES OPERATE ON
THE DATA USING LOAD AND STORE OPERATIONS
FROM THE MEMORY.
•...
SYSTOLIC ARCHITECTURES
• CONVENTIONAL PROCESSING:
MEMORY
F1 F2 Fn
MEMORY
F1 F2 Fn
• SYSTOLIC PROCESSING
SYSTOLIC ARCHITECTURES
• IN SYSTOLIC PROCESSING, DATA TO BE PROCESSED
FLOWS THROUGH VARIOUS OPERATION STAGES AND
THEN FINA...
PERFORMANCE OF
PARALLEL COMPUTERS
• AN IMPORTANT MEASURE OF PARALLEL
ARCHITECTURE IS SPEEDUP.
• LET n = NO. OF PROCESSORS;...
AMDAHL‟S LAW
• 1967
• BASED ON A VERY SIMPLE OBSERVATION.
• A PROGRAM REQUIRING TOTAL TIME T FOR
SEQUENTIAL EXECUTION SHAL...
AMDAHL‟S LAW
• LET f = SEQ. FRACTION FOR A GIVEN PROGRAM.
• AMDAHL‟S LAW STATES THAT THE SPEED UP OF
A PARALLEL COMPUTER I...
AMDAHL‟S LAW
• CONSIDER TWO PARALLEL COMPS. Me AND Mi.
Me IS BUILT USING POWERFUL PROCS. CAPABLE
OF EXECUTING AT A SPEED O...
AMDAHL‟S LAW
• PROOF:
LET W = TOTAL WORK; M = SPEED OF Mi (IN Mflops)
R.m = SPEED OF PE OF Ma; f.W = SEQ WORK OF JOB;
T(Ma...
AMDAHL‟S LAW
• T(Ma) = TIME FOR SEQ PART + TIME FOR
PARALLEL PART
= ((f.W)/(r.M)) + [((1-f).W/n)/(r.M)] = (W/M).(f/r) IF n...
AMDAHL‟S LAW
• THE THEOREM IMPLIES THAT A SEQ COMPONENT
FRACTION ACCEPTABLE FOR THE MACHINE Mi
MAY NOT BE ACCEPTABLE FOR T...
AMDAHL‟S LAW
• RELATION BETWEEN EFFICIENCY e AND SEQ FRACTION
r:
• S <= 1/[f + (1 – f )/n]
• EFFICIENCY e = S/n
• SO, e <=...
MINSKY‟S CONJECTURE
• 1970
• FOR A PARALLEL COMPUTER WITH n PROCS, THE
SPEEDUP S SHALL BE PROPORTIONAL TO log2n.
• MINSKY‟...
PARALLEL ALGORITHMS
• IMP MEASURE OF THE PERFORMANCE OF ANY
ALGO IS ITS TIME AND SPACE COMPLEXITY.
THEY ARE SPECIFIED AS S...
PARALLEL ALGORITHMS
• PARALLEL ALGOS ARE THE ALGOS TO BE RUN
ON PARALLEL MACHINE.
• SO, COMPLEXITY OF COMM AMONGST
PROCESO...
PARALLEL ALGORITHMS
• DUE TO THIS REASON, MAPPING OF THE ALGO
ON THE ARCHITECTURE IS AN IMP ACTIVITY IN
THE STUDY OF PARAL...
PARALLEL ALGORITHMS
• A PARALLEL ALGO FOR A GIVEN PROBLEM
MAY BE DEVELOPED USING ONE OR MORE OF
THE FOLLOWING:
1. DETECT A...
DISTRIBUTED PROCESSING
• PARALLEL PROCESSING DIFFERS FROM DISTRIBUTED
PROCESSING IN THE SENSE THAT IT HAS (1) CLOSE
COUPLI...
PIPELINING PROCESSING
• A PIPELINE CAN WORK WELL WHEN:
1. THE TIME TAKEN BY EACH STAGE IS NEARLY
THE SAME.
2. IT REQUIRES ...
PIPELINING PROCESSING
• IN FACT, A JOB WHICH CAN BE BROKEN INTO A NO OF
SEQUENTIAL STEPS IS THE BASIS OF PIPELINE
PROCESSI...
PIPELINING PROCESSING
• THE FOURTH IMP THING IS THAT THE STAGES IN
THE PIPELINE ARE SPECIALIZED TO DO
PARTICULAR SUBFUNCTI...
PERFORMANCE MEASURES
OF PIPELINE
• EFFICIENCY, SPEEDUP AND THROUGHPUT
• EFFICIENCY: LET n BE THE LENGTH OF PIPE AND
m BE T...
PERFORMANCE MEASURES
OF PIPELINE
• SPEEDUP = S = [((n.ts).m)/((m+n-1).ts)]
= [(m.n)/(n+m-1)]
 WHEN n>>m, S=m (NO. OF TASK...
PERFORMANCE MEASURES
OF PIPELINE
• THROUGHPUT = Th = [m/((n+m-1).ts)] = e/ts WHERE ts IS
TIME THAT ELAPSES AT 1 STAGE.
• W...
OPTIMAL PIPE SEGMENTATION
• IN HOW MANY SUBFUNCTIONS A FUNCTION
SHOULD BE DIVIDED?
• LET n = NO OF STAGES, T= TIME FOR NON...
OPTIMAL PIPE SEGMENTATION
• SPEED (TIME PER OUTPUT) = (T/n + D)
• ONE OF THE IMPORTANT PERFORMANCE
MEASURE IS THE PRODUCT ...
PIPELINE CONTROL
• IN A NON-PIPELINED SYSTEM, ONE INST IS FULLY EXECUTED
BEFORE THE NEXT ONE STARTS, THUS MATCHING THE ORD...
PIPELINE CONTROL
• THERE ARE 2 TYPES OF CONTROL STRUCTURES
IMPLEMENTED ON COMMERCIAL SYSTEMS.
• THE FIRST ONE IS CHARACTER...
PIPELINE CONTROL
• THE INTERLOCK IS NECESSARY BECAUSE THE PIPE IS
ASYNCHRONOUS DUE TO VARIATIONS IN THE SPEEDS
OF DIFFEREN...
PIPELINE CONTROL
• IN SUCH SYSTEMS, WHEN A STAGE HAS TO SUSPEND THE
FLOW OF A PARTICULAR INSTRUCTION, IT ALLOWS OTHER
INST...
PIPELINE HAZARDS
• THE HARDWARE TECHNIQUE THAT DETECTS AND
RESOLVES HAZARDS IS CALLED INTERLOCK.
• A HAZARD OCCURS WHENEVE...
PIPELINE HAZARDS
• ASSUME THAT AN INST j LOGICALLY FOLLOWS AN INST i.
• RAW HAZARD: IT OCCURS BETWEEN 2 INSTS WHEN INST j
...
PIPELINE HAZARDS
• THE DOMAIN (READ SET) OF AN INST k, DENOTED BY Dk,
IS THE SET OF ALL OBJECTS WHOSE CONTENTS ARE
ACCESSE...
HAZARD DETECTION & REMOVAL
• TECHNIQUES USED FOR HAZARD DETECTION CAN BE
CLASSIFIED INTO 2 CLASSES:
 CENTRALIZE ALL THE H...
HAZARD DETECTION & REMOVAL
• FIRST APPROACH IS SIMPLE BUT SUSPENDS THE
INST FLOW IN THE IU ITSELF, IF THE INST
FETCHED IS ...
HAZARD DETECTION & REMOVAL
• THERE ARE 2 APPROACHS FOR HAZARD REMOVAL:
 SUSPEND THE PIPELINE INITIATION AT THE POINT OF
H...
HAZARD DETECTION & REMOVAL
• THE FIRST APPROACH IS SIMPLE BUT PENALIZES ALL THE
INSTS FOLLOWING j.
• SECOND APPROACH IS EX...
HAZARD DETECTION & REMOVAL
• THE WAITING INST THEN IS FROZEN AT THIS STAGE UNTIL
THE DATA IS AVAILABLE.
• SINCE THE STAGE ...
HAZARD DETECTION & REMOVAL
• THIS TECHNIQUE IS KNOWN BY THE NAME “INTERNAL
FORWARDING” SINCE THE STAGES ARE DESIGNED TO
CA...
MULTIPROCESSOR SYSTEMS
• IT IS A COMPUTER SYSTEM COMPRISING OF TWO OR MORE
PROCESSORS.
• AN INTERCONNECTION NETWORK LINKS ...
CLASSIFICATION OF MULTI-PROCESSORS
• MULTI-PROCESSOR ARCHITECTURE:
TIGHTLY COUPLED LOOSELY COUPLED
UMA NUMA NORMA
NO REMOT...
SYMMETRIC
MULTIPROCESSOR
• IN UMA SYSTEM, THE ACCESS TIME FOR MEM IS EQUAL
FOR ALL THE PROCESSORS.
• A SMP SYSTEM IS AN UM...
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Computer architecture abhmail

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Transcript of "Computer architecture abhmail"

  1. 1. COMPUTER ARCHITECTURE
  2. 2. MICROPROCESSOR • IT IS ONE OF THE GREATEST ACHIEVEMENTS OF THE 20TH CENTURY. • IT USHERED IN THE ERA OF WIDESPREAD COMPUTERIZATION.
  3. 3. EARLY ARCHITECTURE • VON NEUMANN ARCHITECTURE, 1940 • PROGRAM IS STORED IN MEMORY. • SEQUENTIAL OPERATION • ONE INST IS RETRIEVED AT A TIME, DECODED AND EXECUTED • LIMITED SPEED
  4. 4. Conventional 32 bit Microprocessors • Higher data throughput with 32 bit wide data bus • Larger direct addressing range • Higher clock frequencies and operating speeds as a result of improvements in semiconductor technology • Higher processing speeds because larger registers require fewer calls to memory and reg-to-reg transfers are 5 times faster than reg-to-memory transfers
  5. 5. Conventional 32 bit Microprocessors • More insts and addressing modes to improve software efficiency • More registers to support High-level languages • More extensive memory management & coprocessor capabilities • Cache memories and inst pipelines to increase processing speed and reduce peak bus loads
  6. 6. Conventional 32 bit Microprocessors • To construct a complete general purpose 32 bit microprocessor, five basic functions are necessary:  ALU  MMU  FPU  INTERRUPT CONTROLLER  TIMING CONTROL
  7. 7. Conventional Architecture • KNOWN AS VON NEUMANN ARCHITECTURE • ITS MAIN FEATURES ARE:  A SINGLE COMPUTING ELEMENT INCORPORATING A PROCESSOR, COMM. PATH AND MEMORY  A LINEAR ORGANIZATION OF FIXED SIZE MEMORY CELLS  A LOW-LEVEL MACHINE LANGUAGE WITH INSTS PERFORMING SIMPLE OPERATIONS ON ELEMENTARY OPERANDS  SEQUENTIAL, CENTRALIZED CONTROL OF COMPUTATION
  8. 8. Conventional Architecture • Single processor configuration: PROCESSOR MEMORY INPUT-OUTPUT
  9. 9. Conventional Architecture • Multiple processor configuration with a global bus: SYSTEM INPUT- OUTPUT GLOBAL MEMORY PROCESSORS WITH LOCAL MEM & I/O
  10. 10. Conventional Architecture • THE EARLY COMP ARCHITECTURES WERE DESIGNED FOR SCIENTIFIC AND COMMERCIAL CALCULATIONS AND WERE DEVEPOED TO INCREASE THE SPEED OF EXECUTION OF SIMPLE INSTRUCTIONS. • TO MAKE THE COMPUTERS PERFORM MORE COMPLEX PROCESSES, MUCH MORE COMPLEX SOFTWARE WAS REQUIRED.
  11. 11. Conventional Architecture • ADVANCEMENT OF TECHNOLOGY ENHANCED THE SPEED OF EXECUTION OF PROCESSORS BUT A SINGLE COMM PATH HAD TO BE USED TO TRANSFER INSTS AND DATA BETWEEN THE PROCESSOR AND THE MEMORY. • MEMORY SIZE INCREASED. THE RESULT WAS THE DATA TRANSFER RATE ON THE MEMORY INTERFACE ACTED AS A SEVERE CONSTRAINT ON THE PROCESSING SPEED.
  12. 12. Conventional Architecture • AS HIGHER SPEED MEMORY BECAME AVAILABLE, THE DELAYS INTRODUCED BY THE CAPACITANCE AND THE TRANSMISSION LINE DELAYS ON THE MEMORY BUS AND THE PROPAGATION DELAYS IN THE BUFFER AND ADDRESS DECODING CIRCUITRY BECAME MORE SIGNIFICANT AND PLACED AN UPPER LIMIT ON THE PROCESSING SPEED.
  13. 13. Conventional Architecture • THE USE OF MULTIPROCESSOR BUSES WITH THE NEED FOR ARBITRATION BETWEEN COMPUTERS REQUESTING CONTROL OF THE BUS REDUCED THE PROBLEM BUT INTRODUCED SEVERAL WAIT CYCLES WHILE THE DATA OR INST WERE FETCHED FROM MEMORY. • ONE METHOD OF INCREASING PROCESSING SPEED AND DATA THROUGHPUT ON THE MEMORY BUS WAS TO INCREASE THE NUMBER OF PARALLEL BITS TRANSFERRED ON THE BUS.
  14. 14. Conventional Architecture • THE GLOBAL BUS AND THE GLOBAL MEMORY CAN ONLY SERVE ONE PROCESSOR AT A TIME. • AS MORE PROCESSORS ARE ADDED TO INCREASE THE PROCESSING SPEED, THE GLOBAL BUS BOTTLENECK BECAME WORSE. • IF THE PROCESSING CONSISTS OF SEVERAL INDEPENDENT TASKS, EACH PROC WILL COMPETE FOR GLOBAL MEMORY ACCESS AND GLOBAL BUS TRANSFER TIME.
  15. 15. Conventional Architecture • TYPICALLY, ONLY 3 OR 4 TIMES THE SPEED OF A SINGLE PROCESSOR CAN BE ACHIEVED IN MULTIPROCESSOR SYSTEMS WITH GLOBAL MEMORY AND A GLOBAL BUS. • TO REDUCE THE EFFECT OF GLOBAL MEMORY BUS AS A BOTTLENECK, (1) THE LENGTH OF THE PIPE WAS INCREASED SO THAT THE INST COULD BE BROKEN DOWN INTO BASIC ELEMENTS TO BE MANIPULATED SIMULTANEOUSLY AND (2) CACHE MEM WAS INTRODUCED SO THAT INSTS AND/OR DATA COULD BE PREFETCHED FROM GLOBAL MEMORY AND STORED IN HIGH SPEED LOCAL MEMORY.
  16. 16. PIPELINING • THE PROC. SEPARATES EACH INST INTO ITS BASIC OPERATIONS AND USES DEDICATED EXECUTION UNITS FOR EACH TYPE OF OPERATION. • THE MOST BASIC FORM OF PIPELINING IS TO PREFETCH THE NEXT INSTRUCTION WHILE SIMULTANEOULY EXECUTING THE PREVIOUS INSTRUCTION. • THIS MAKES USE OF THE BUS TIME WHICH WOULD OTHERWISE BE WASTED AND REDUCES INSTRUCTION EXECUTION TIME.
  17. 17. PIPELINING • TO SHOW THE USE OF A PIPELINE, CONSIDER THE MULTIPLICATION OF 2 DECIMAL NOS: 3.8 X 102 AND 9.6X103. THE PROC. PERFORMS 3 OPERATIONS: • A: MULTIPLIES THE MANTISSA • B: ADDS THE EXPONENTS • C: NORMALISES THE RESULT TO PLACE THE DECIMAL POINT IN THE CORRECT POSITION. • IF 3 EXECUTION UNITS PERFORMED THESE OPERATIONS, OPS.A & B WOULD DO NOTHING WHILE C IS BEING PERFORMED. • IF A PIPELINE WERE IMPLEMENTED, THE NEXT NUMBER COULD BE PROCESSED IN EXECUTION UNITS A AND B WHILE C WAS BEING PERFORMED.
  18. 18. PIPELINING • TO GET A ROUGH INDICATION OF PERFORMANCE INCREASE THROUGH PIPELINE, THE STAGE EXECUTION INTERVAL MAY BE TAKEN TO BE THE EXECUTION TIME OF THE SLOWEST PIPELINE STAGE. • THE PERFORMANCE INCREASE FROM PIPELINING IS ROUGHLY EQUAL TO THE SUM OF THE AVERAGE EXECUTION TIMES FOR ALL STAGES OF THE PIPELINE, DIVIDED BY THE AVERAGE VALUE OF THE EXECUTION TIME OF THE SLOWEST PIPELINE STAGE FOR THE INST MIX CONSIDERED.
  19. 19. PIPELINING • NON-SEQUENTIAL INSTS CAUSE THE INSTRUCTIONS BEHIND IN THE PIPELINE TO BE EMPTIED AND FILLING TO BE RESTARTED. • NON-SEQUENTIAL INSTS. TYPICALLY COMPRISE 15 TO 30% OF INSTRUCTIONS AND THEY REDUCE PIPELINE PERFORMANCE BY A GREATER PERCENTAGE THAN THEIR PROBABILITY OF OCCURRENCE.
  20. 20. CACHE MEMORY • VON-NEUMANN SYSTEM PERFORMANCE IS CONSIDERABLY EFFECTED BY MEMORY ACCESS TIME AND MEMORY BW (MAXIMUM MEMORY TRANSFER RATE). • THESE LIMITATIONS ARE SPECIALLY TIGHT FOR 32 BIT PROCESSORS WITH HIGH CLOCK SPEEDS. • WHILE STATIC RAM WITH 25ns ACCESS TIMES ARE CAPABLE OF KEEPING PACE WITH PROC SPEED, THEY MUST BE LOCATED ON THE SAME BOARD TO MINIMISE DELAYS, THUS LIMITING THE AMOUNT OF HIGH SPEED MEMORY AVAILABLE.
  21. 21. CACHE MEMORY • DRAM HAS A GREATER CAPACITY PER CHIP AND A LOWER COST, BUT EVEN THE FASTEST DRAM CAN‟T KEEP PACE WITH THE PROCESSOR, PARTICULARLY WHEN IT IS LOCATED ON A SEPARATE BOARD ATTACHED TO A MEMORY BUS. • WHEN A PROC REQUIRES INST/DATA FROM/TO MEMORY, IT ENTERS A WAIT STATE UNTIL IT IS AVAILABLE. THIS REDUCES PROCESSORS PERFORMANCE.
  22. 22. CACHE MEMORY • CACHE ACTS AS A FAST LOCAL STORAGE BUFFER BETWEEN THE PROC AND THE MAIN MEMORY. • OFF-CHIP BUT ON-BOARD CACHE MAY REQUIRE SEVERAL MEMORY CYCLES WHEREAS ON-CHIP CACHE MAY ONLY REQUIRE ONE MEMORY CYCLE, BUT ON-BOARD CACHE CAN PREVENT THE EXCESSIVE NO. OF WAIT STATES IMPOSED BY MEMORY ON THE SYSTEM BUS AND IT REDUCES THE SYSTEM BUS LOAD.
  23. 23. CACHE MEMORY • THE COST OF IMPLEMENTING AN ON-BOARD CACHE IS MUCH LOWER THAN THE COST OF FASTER SYSTEM MEMORY REQUIRED TO ACHIEVE THE SAME MEMORY PERFORMANCE. • CACHE PERFORMANCE DEPENDS ON ACCESS TIME AND HIT RATIO, WHICH IS DEPENDENT ON THE SIZE OF THE CACHE AND THE NO. OF BYTES BROUGHT INTO CACHE ON ANY FETCH FROM THE MAIN MEMORY (THE LINE SIZE).
  24. 24. CACHE MEMORY • INCREASING THE LINE SIZE INCREASES THE CHANCE THAT THERE WILL BE A CACHE HIT ON THE NEXT MEMORY REFERENCE. • IF A 4K BYTE CACHE WITH A 4 BYTE LINE SIZE HAS A HIT RATIO OF 80%, DOUBLING THE LINE SIZE MIGHT INCREASE THE HIT RATIO TO 85% BUT DOUBLING THE LINE SIZE AGAIN MIGHT ONLY INCREASE THE HIT RATIO TO 87%.
  25. 25. CACHE MEMORY • OVERALL MEMORY PERFORMANCE IS A FUNCTION OF CACHE ACCESS TIME, CACHE HIT RATIO AND MAIN MEMORY ACCESS TIME FOR CACHE MISSES. • A SYSTEM WITH 80% CACHE HIT RATIO AND 120ns CACHE ACCESS TIME ACCESSES MAIN MEMORY 20% OF THE TIME WITH AN ACCESS TIME OF 600 ns. THE AV ACCESS TIME IN ns WILL BE (0.8x120) +[0.2x(600 + 120)]= 240
  26. 26. CACHE DESIGN • PROCESSORS WITH DEMAND PAGED VIRTUAL MEMORY SYSTEMS REQUIRE AN ASSOCIATIVE CACHE. • VIRTUAL MEM SYSTEMS ORGANIZE ADDRESSES BY THE START ADDRESSES FOR EACH PAGE AND AN OFFSET WHICH LOCATES THE DATA WITHIN THE PAGE. • AN ASSOCIATIVE CACHE ASSOCIATES THE OFFSET WITH THE PAGE ADDRESS TO FIND THE DATA NEEDED.
  27. 27. CACHE DESIGN • WHEN ACCESSED, THE CACHE CHECKS TO SEE IF IT CONTAINS THE PAGE ADDRESS (OR TAG FIELD); IF SO, IT ADDS THE OFFSET AND, IF A CACHE HIT IS DETECTED, THE DATA IS FETCHED IMMEDIATELY FROM THE CACHE. • PROBLEMS CAN OCCUR IN A SINGLE SET-ASSOCIATIVE CACHE IF WORDS WITHIN DIFFERENT PAGES HAVE THE SAME OFFSET. • TO MINIMISE THIS PROBLEM A 2-WAY SET-ASSOCIATIVE CACHE IS USED. THIS IS ABLE TO ASSOCIATE MORE THAN ONE SET OF TAGS AT A TIME ALLOWING THE CACHE TO STORE THE SAME OFFSET FROM TWO DIFFERENT PAGES.
  28. 28. CACHE DESIGN • A FULLY ASSOCIATIVE CACHE ALLOWS ANY NUMBER OF PAGES TO USE THE CACHE SIMULTANEOUSLY. • A CACHE REQUIRES A REPLACEMENT ALGORITHM TO FIND REPLACEMENT CACHE LINES WHEN A MISS OCCURS. • PROCESSORS THAT DO NOT USE DEMAND PAGED VIRTUAL MEMORY, CAN EMPLOY A DIRECT MAPPED CACHE WHICH CORRESPONDS EXACTLY TO THE PAGE SIZE AND ALLOWS DATA FROM ONLY ONE PAGE TO BE STORED AT A TIME.
  29. 29. MEMORY ARCHITECTURES • 32 BIT PROCESSORS HAVE INTRODUCED 3 NEW CONCEPTS IN THE WAY THE MEMORY IS INTERFACED: 1. LOCAL MEMORY BUS EXTENSIONS 2. MEMORY INTEREAVING 3. VIRTUAL MEMORY MANAGEMENT
  30. 30. LOCAL MEM BUS EXTENSIONS • IT PERMITS LARGER LOCAL MEMORIES TO BE CONNECTED WITHOUT THE DELAYS CAUSED BY BUS REQUESTS AND BUS ARBITRATION FOUND ON MULTIPROCESSOR BUSES. • IT HAS BEEN PROVIDED TO INCREASE THE SIZE OF THE LOCAL MEMORY ABOVE THAT WHICH CAN BE ACCOMODATED ON THE PROCESSOR BOARD. • BY OVERLAPPING THE LOCAL MEM BUS AND THE SYSTEM BUS CYCLES IT IS POSSIBLE TO ACHIEVE HIGHER MEM ACCESS RATES FROM PROCESSORS WITH PIPELINES WHICH PERMIT THE ADDRESS OF THE NEXT MEMORY REFERENCE TO BE GENERATED WHILE THE PREVIOUS DATA WORD IS BEING FETCHED.
  31. 31. MEMORY INTERLEAVING • PIPELINED PROCESSORS WITH THE ABILITY TO GENERATE THE ADDRESS OF THE NEXT MEMORY REFERENCE WHILE FETCHING THE PREVIOUS DATA WORD WOULD BE SLOWED DOWN IF THE MEMORY WERE UNABLE TO BEGIN THE NEXT MEMORY ACCESS UNTIL THE PREVIOUS MEM CYCLE HAD BEEN COMPLETED. • THE SOLUTION IS TO USE TWO-WAY MEMORY INTERLEAVING. IT USES 2 MEM BOARDS- 1 FOR ODD ADDRESSES AND 1 FOR EVEN ADDRESSES.
  32. 32. MEMORY INTERLEAVING • ONE BOARD CAN BEGIN THE NEXT MEM CYCLE WHILE THE OTHER BOARD COMPLETES THE PREVIOUS CYCLE. • THE SPEED ADV IS GREATEST WHEN MULTIPLE SEQUENTIAL MEM ACCESSES ARE REQUIRED FOR BURST I/O TRANSFERS BY DMA. • DMA DEFINES A BLOCK TRANSFER IN TERMS OF A STARTING ADDRESS AND A WORD COUNT FOR SEQUENTIAL MEM ACCESSES.
  33. 33. MEMORY INTERLEAVING • TWO WAY INTERLEAVING MAY NOT PREVENT MEM WAIT STATES FOR SOME FAST SIGNAL PROCESSING APPLICATIONS AND SYSTEMS HAVE BEEN DESIGNED WITH 4 OR MORE WAY INTERLEAVING IN WHICH THE MEM BOARDS ARE ASSIGNED CONSECUTIVE ADDRESSES BY A MEMORY CONTROLLER.
  34. 34. Conventional Architecture • EVEN WITH THESE ENHANCEMENTS, THE SEQUENTIAL VON NEUMANN ARCHITECTURE REACHED THE LIMITS IN PROCESSING SPEED BECAUSE THE SEQUENTIAL FETCHING OF INSTS AND DATA THROUGH A COMMON MEMORY INTERFACE FORMED THE BOTTLENECK. • THUS, PARALLEL PROC ARCHITECTURES CAME INTO BEING WHICH PERMIT LARGE NUMBER OF COMPUTING ELEMENTS TO BE PROGRAMMED TO WORK TOGETHER SIMULTANEOUSLY. THE USEFULNESS OF PARALLEL PROCESSOR DEPENDS UPON THE AVAILABILITY OF SUITABLE PARALLEL ALGORITHMS.
  35. 35. HOW TO INCREASE THE SYSTEM SPEED? 1. USING FASTER COMPONENTS. COSTS MORE, DISSIPATE CONSIDERABLE HEAT. THE RATE OF GROWTH OF SPEED USING BETTER TECHNOLOGY IS VERY SLOW. eg., IN 80‟S BASIC CLOCK RATE WAS 50 MHz AND TODAY IT IS AROUND 2 GHz, DURING THIS PERIOD SPEED OF COMPUTER IN SOLVING INTENSIVE PROBLEMS HAS GONE UP BY A FACTOR OF 100,000. IT IS DUE TO THE INCREASED ARCHITECTURE.
  36. 36. HOW TO INCREASE THE SYSTEM SPEED? 2. ARCHITECTURAL METHODS: A. USE PARALLELISM IN SINGLE PROCESSOR [ OVERLAPPING EXECUTION OF NO OF INSTS (PIPELINING)] B. OVERLAPPING OPERATION OF DIFFERENT UNITS C. INCREASE SPEED OF ALU BY EXPLOITING DATA/TEMPORAL PARALLELISM D. USING NO OF INTERCONNECTED PROCESSORS TO WORK TOGETHER
  37. 37. PARALLEL COMPUTERS • THE IDEA EMERGED AT CIT IN 1981 • A GROUP HEADED BY CHARLES SEITZ AND GEOFFREY FOX BUILT A PARALLEL COMPUTER IN 1982 • 16 NOS 8085 WERE CONNECTED IN A HYPERCUBE CONFIGURATION • ADV WAS LOW COST PER MEGAFLOP
  38. 38. PARALLEL COMPUTERS • BESIDES HIGHER SPEED, OTHER FEATURES OF PARALLEL COMPUTERS ARE:  BETTER SOLUTION QUALITY: WHEN ARITHMETIC OPS ARE DISTRIBUTED, EACH PE DOES SMALLER NO OF OPS, THUS ROUNDING ERRORS ARE REDUCED  BETTER ALGORITHMS  BETTER AND FASTER STORAGE  GREATER RELIABILITY
  39. 39. CLASSIFICATION OF COMPUTER ARCHITECTURE • FLYNN‟S TAXONOMY: IT IS BASED UPON HOW THE COMPUTER RELATES ITS INSTRUCTIONS TO THE DATA BEING PROCESSED – SISD SIMD MISD MIMD
  40. 40. FLYNN‟S TAXONOMY • SISD: CONVENTIONAL VON-NEUMANN SYSTEM. CONTROL UNIT PROCESSOR INST STREAM DATA STREAM
  41. 41. FLYNN‟S TAXONOMY • SIMD: IT HAS A SINGLE STREAM OF VECTOR INSTS THAT INITIATE MANY OPERATIONS. EACH ELEMENT OF A VECTOR IS REGARDED AS A MEMBER OF A SEPARATE DATA STREAM GIVING MULTIPLE DATA STREAMS. CONTROL UNIT INST STREAM PROCESSOR PROCESSOR PROCESSOR DATA STREAM 1 DATA STREAM 2 DATA STREAM 3SYNCHRONOUS MULTIPROCESSOR
  42. 42. FLYNN‟S TAXONOMY • MISD: NOT POSSIBLE C U 1 C U 2 PU 3 PU 2 C U 3 PU 1 INST STREAM 1 INST STREAM 2 INST STREAM 3 DS
  43. 43. FLYNN‟S TAXONOMY • MIMD: MULTIPROCESSOR CONFIGURATION AND ARRAY OF PROCESSORS. CU 1 CU 2 CU 3 IS 1 IS 2 IS 3 DS 1 DS 2 DS 3
  44. 44. FLYNN‟S TAXONOMY • MIMD COMPUTERS COMPRISE OF INDEPENDENT COMPUTERS, EACH WITH ITS OWN MEMORY, CAPABLE OF PERFORMING SEVERAL OPERATIONS SIMULTANEOUSLY. • MIMD COMPS MAY COMPRISE OF A NUMBER OF SLAVE PROCESSORS WHICH MAY BE INDIVIUALLY CONNECTED TO MULTI-ACCESS GLOBAL MEMORY BY A SWITCHING MATRIX UNDER THE CONTROL OF MASTER PROCESSOR.
  45. 45. FLYNN‟S TAXONOMY • THIS CLASSIFICATION IS TOO BROAD. • IT PUTS EVERYTHING EXCEPT MULTIPROCESSORS IN ONE CLASS. • IT DOES NOT REFLECT THE CONCURRENCY AVAILABLE THROUGH THE PIPELINE PROCESSING AND THUS PUTS VECTOR COMPUTERS IN SISD CLASS.
  46. 46. SHORE‟S CLASSIFICATION • SHORE CLASSIFIED THE COMPUTERS ON THE BASIS OF ORGANIZATION OF THE CONSTITUENT ELEMENTS OF THE COMPUTER. • SIX DIFFERENT KINDS OF MACHINES WERE RECOGNIZED: 1. CONVENTIONAL VON NEWMANN ARCHITECTURE WITH 1 CU, 1 PU, IM AND DM. A SINGLE DM READ PRODUCES ALL BITS FOR PROCESSING BY PU. THE PU MAY CONTAIN MULTIPLE FUNCTIONAL UNITS WHICH MAY OR MAY NOT BE PIPELINED. SO, IT INCLUDES BOTH THE SCALAR COMPS (IBM 360/91, CDC7600)AND PIPELINED VECTOR COMPUTERS (CRAY 1, CYBER 205)
  47. 47. SHORE‟S CLASSIFICATION • TYPE I: IM CU HORIZONTAL PU WORD SLICE DM NOTE THAT THE PROCESSING IS CHARACTERISED AS HORIZONTAL (NO OF BITS IN PARALLEL AS A WORD)
  48. 48. SHORE‟S CLASSIFICATION • MACHINE 2: SAME AS MACHINE 1 EXCEPT THAT SM FETCHES A BIT SLICE FROM ALL THE WORDS IN THE MEMORY AND PU IS ORGANIZED TO PERFORM THE OPERATIONS IN A BIT SERIAL MANNER ON ALL THE WORDS. • IF THE MEMORY IS REGARDED AS A 2D ARRAY OF BITS WITH ONE WORD STORED PER ROW, THEN THE MACHINE 2 READS VERTICAL SLICE OF BITS AND PROCESSES THE SAME, WHEREAS THE MACHINE 1 READS AND PROCESSES HORIZONTAL SLICE OF BITS. EX. MPP, ICL DAP
  49. 49. SHORE‟S CLASSIFICATION • MACHINE 2: IM CU VERTICAL PU BIT SLICE DM
  50. 50. SHORE‟S CLASSIFICATION • MACHINE 3: COMBINATION OF 1 AND 2. • IT COULD BE CHARACTERISED HAVING A MEMORY AS AN ARRAY OF BITS WITH BOTH HORIZONTAL AND VERTICAL READING AND PROCESSING POSSIBLE. • SO, IT WILL HAVE BOTH VERTICAL AND HORIZONTAL PROCESSING UNITS. • EXAMPLE IS OMENN 60 (1973)
  51. 51. SHORE‟S CLASSIFICATION • MACHINE 3: IM CU VERTICAL PU HORIZON TAL PU DM
  52. 52. SHORE‟S CLASSIFICATION • MACHINE 4: IT IS OBTAINED BY REPLICATING THE PU AND DM OF MACHINE 1. • AN ENSEMBLE OF PU AND DM IS CALLED PROCESSING ELEMENT (PE). • THE INSTS ARE ISSUED TO THE PEs BY A SINGLE CU. PEs COMMUNICATE ONLY THROUGH CU. • ABSENCE OF COMM BETWEEN PEs LIMITS ITS APPLICABILITY • EX: PEPE(1976)
  53. 53. SHORE‟S CLASSIFICATION • MACHINE 4: IM CU PU PU PU DM DM DM
  54. 54. SHORE‟S CLASSIFICATION • MACHINE 5: SIMILAR TO MACHINE 4 WITH THE ADDITION OF COMMUNICATION BETWEEN PE.EXAMPLE: ILLIAC IV IM CU PU PU PU DM DM DM
  55. 55. SHORE‟S CLASSIFICATION • MACHINE 6: • MACHINES 1 TO 5 MAINTAIN SEPARATION BETWEEN DM AND PU WITH SOME DATA BUS OR CONNECTION UNIT PROVIDING THE COMMUNICATION BETWEEN THEM. • MACHINE 6 INCLUDES THE LOGIC IN MEMORY ITSELF AND IS CALLED ASSOCIATIVE PROCESSOR. • MACHINES BASED ON SUCH ARCHITECTURES SPAN A RANGE FROM SIMPLE ASSOCIATIVE MEMORIES TO COMPLEX ASSOCIATIVE PROCS.
  56. 56. SHORE‟S CLASSIFICATION • MACHINE 6: IM CU PU + DM
  57. 57. FENG‟S CLASSIFICATION • FENG PROPOSED A SCHEME ON THE BASIS OF DEGREE OF PARALLELISM TO CLASSIFY COMPUTER ARCHITECTURE. • MAXIMUM NO OF BITS THAT CAN BE PROCESSED EVERY UNIT OF TIME BY THE SYSTEM IS CALLED “MAXIMUM DEGREE OF PARALLELISM”
  58. 58. FENG‟S CLASSIFICATION • BASED ON FENG‟S SCHEME, WE HAVE SEQUENTIAL AND PARALLEL OPERATIONS AT BIT AND WORD LEVELS TO PRODUCE THE FOLLOWING CLASSIFICATION:  WSBS NO CONCEIVABLE IMPLEMENTATION  WPBS STARAN  WSBP CONVENTIONAL COMPUTERS  WPBP ILLIAC IV o THE MAX DEGREE OF PARALLELISM IS GIVEN BY THE PRODUCT OF THE NO OF BITS IN THE WORD AND NO OF WORDS PROCESSED IN PARALLEL
  59. 59. HANDLER‟S CLASSIFICATION • FENG‟S SCHEME, WHILE INDICATING THE DEGREE OF PARALLELISM DOES NOT ACCOUNT FOR THE CONCURRENCY HANDLED BY THE PIPELINED DESIGNS. • HANDLER‟S SCHEME ALLOWS THE PIPELINING TO BE SPECIFIED. • IT ALLOWS THE IDENTIFICATION OF PARALLELISM AND DEGREE OF PIPELINING BUILT IN THE HARDWARE STRUCTURE
  60. 60. HANDLER‟S CLASSIFICATION • HANDLER DEFINED SOME OF THE TERMS AS: • PCU – PROCESSOR CONTROL UNITS • ALU – ARITHMETIC LOGIC UNIT • BLC – BIT LEVEL CIRCUITS • PE – PROCESSING ELEMENTS • A COMPUTING SYSTEM C CAN THEN BE CHARATERISED BY A TRIPLE AS T(C) = (KxK', DxD', WxW') • WHERE K=NO OF PCU, K'= NO OF PROCESSORS THAT ARE PIPELINED, D=NO OF ALU,D'= NO OF PIPELINED ALU, W=WORDLENGTH OF ALU OR PE AND W'= NO OF PIPELINE STAGES IN ALU OR PE
  61. 61. COMPUTER PROGRAM ORGANIZATION • BROADLY, THEY MAY BE CLASSIFIED AS:  CONTROL FLOW PROGRAM ORGANIZATION  DATAFLOW PROGRAM ORGANIZATION  REDUCTION PROGRAM ORGANIATION
  62. 62. COMPUTER PROGRAM ORGANIZATION • IT USES EXPLICIT FLOWS OF CONTROL INFO TO CAUSE THE EXECUTION OF INSTS. • DATAFLOW COMPS USE THE AVAILABILITY OF OPERANDS TO TRIGGER THE EXECUTION OF OPERATIONS. • REDUCTION COMPUTERS USE THE NEED FOR A RESULT TO TRIGGER THE OPERATION WHICH WILL GENERATE THE REQUIRED RESULT.
  63. 63. COMPUTER PROGRAM ORGANIZATION • THE THREE BASIC FORMS OF COMP PROGRAM ORGANIZATION MAY BE DESCRIBED IN TERMS OF THEIR DATA MECHANISM (WHICH DEFINES THE WAY A PERTICULAR ARGUMENT IS USED BY A NUMBER OF INSTRUCTIONS) AND THE CONTROL MECHANISM (WHICH DEFINES HOW ONE INST CAUSES THE EXECUTION OF ONE OR MORE OTHER INSTS AND THE RESULTING CONTROL PATTERN).
  64. 64. COMPUTER PROGRAM ORGANIZATION • CONTROL FLOW PROCESSORS HAVE A “BY REFERENCE” DATA MECHANISM (WHICH USES REFERENCES EMBEDDED IN THE INSTS BEING EXECUTED TO ACCESS THE CONTENTS OF THE SHARED MEMORY) AND TYPICALLY A „SEQUENTIAL‟ CONTROL MECHANISM ( WHICH PASSES A SINGLE THREAD OF CONTROL FROM INSTRUCTION TO INSTRUCTION).
  65. 65. COMPUTER PROGRAM ORGANIZATION • DATAFLOW COMPUTERS HAVE A “BY VALUE” DATA MECHANISM (WHICH GENERATES AN ARGUMENT AT RUN-TIME WHICH IS REPLICATED AND GIVEN TO EACH ACCESSING INSTRUCTION FOR STORAGE AS A VALUE) AND A „PARALLEL‟ CONTROL MECHANISM. • BOTH MECHANISMS ARE SUPPORTED BY DATA TOKENS WHICH CONVEY DATA FROM PRODUCER TO CONSUMER INSTRUCTIONS AND CONTRIBUTE TO THE ACTIVATION OF CONSUMER INSTS.
  66. 66. COMPUTER PROGRAM ORGANIZATION • TWO BASIC TYPES OF REDUCTION PROGRAM ORGANIZATIONS HAVE BEEN DEVELOPED: A. STRING REDUCTION WHICH HAS A „BY VALUE‟ DATA MECHANISM AND HAS ADVANTAGES WHEN MANIPULATING SIMPLE EXPRESSIONS. B. GRAPH REDUCTION WHICH HAS A „BY REFERENCE‟ DATA MECHANISM AND HAS ADVANTAGES WHEN LARGER STRUCTURES ARE INVOLVED.
  67. 67. COMPUTER PROGRAM ORGANIZATION • CONTROL-FLOW AND DATA-FLOW PROGRAMS ARE BUILT FROM FIXED SIZE PRIMITIVE INSTS WITH HIGHER LEVEL PROGRAMS CONSTRUCTED FROM SEQUENCES OF THESE PRIMITIVE INSTRUCTIONS AND CONTROL OPERATIONS. • REDUCTION PROGRAMS ARE BUILT FROM HIGH LEVEL PROGRAM STRUCTURES WITHOUT THE NEED FOR CONTROL OPERATORS.
  68. 68. COMPUTER PROGRAM ORGANIZATION • THE RELATIONSHIP OF THEDATA AND CONTROL MECHANISMS TO THE BASIC COMPUTER PROGRAM ORGANIZATIONS CAN BE SHOWN AS UNDER: DATA MECHANISM BY VALUE BY REFERENCE CONTROL MECHANISM SEQUENTIAL VON-NEUMANN CON.FLOW PARALLEL DATA FLOW PARALLEL CONTROL FLOW RECURSIVE STRING REDUCTION GRAPH REDUCTION
  69. 69. MACHINE ORGANIZATION • MACHINE ORGANIZATION CAN BE CLASSIFIED AS FOLLOWS:  CENTRALIZED: CONSISTING OF A SINGLE PROCESSOR, COMM PATH AND MEMORY. A SINGLE ACTIVE INST PASSES EXECUTION TO A SPECIFIC SUCCESSOR INSTRUCTION. o TRADITIONAL VON-NEUMANN PROCESSORS HAVE CENTRALIZED MACHINE ORGANIZATION AND A CONTROL FLOW PROGRAM ORGANIZATION.
  70. 70. MACHINE ORGANIZATION  PACKET COMMUNICATION: USING A CIRCULAR INST EXECUTION PIPELINE IN WHICH PROCESSORS, COMMUNICATIONS AND MEMORIES ARE LINKED BY POOLS OF WORK. o NEC 7281 HAS A PACKET COMMUNICATION MACHINE ORGANIZATION AND DATAFLOW PROGRAM ORGANIZATION.
  71. 71. MACHINE ORGANIZATION • EXPRESSION MANIPULATION WHICH USES IDENTICAL RESOURCES IN A REGULAR STRUCTURE, EACH RESOURCE CONTAINING A PROCESSOR, COMMUNICATION AND MEMORY. THE PROGRAM CONSISTS OF ONE LARGE STRUCTURE, PARTS OF WHICH ARE ACTIVE WHILE OTHER PARTS ARE TEMPORARILY SUSPENDED. • AN EXPRESSION MANIPULATION MACHINE MAY BE CONSTRUCTED FROM A REGULAR STRUCTURE OF T414 TRANSPUTERS, EACH CONTAINING A VON-NEUMANN PROCESSOR, MEMORY AND COMMUNICATION LINKS.
  72. 72. MULTIPROCESSING SYSTEMS • IT MAKES USE OF SEVERAL PROCESSORS, EACH OBEYING ITS OWN INSTS, USUALLY COMMUNICATING VIA A COMMON MEMORY. • ONE WAY OF CLASSIFYING THESE SYSTEMS IS BY THEIR DEGREE OF COUPLING. • TIGHTLY COUPLED SYSTEMS HAVE PROCESSORS INTERCONNECTED BY A MULTIPROCESSOR SYSTEM BUS WHICH BECOMES A PERFORMANCE BOTTLENECK.
  73. 73. MULTIPROCESSING SYSTEMS • INTERCONNECTION BY A SHARED MEMORY IS LESS TIGHTLY COUPLED AND A MULTIPORT MEMORY MAY BE USED TO REDUCE THE BUS BOTTLENECK. • THE USE OF SEVERAL AUTONOMOUS SYSTEMS, EACH WITH ITS OWN OS, IN A CLUSTER IS MORE LOOSELY COUPLED. • THE USE OF NETWORK TO INTERCONNECT SYSTEMS, USING COMM SOFTWARE, IS THE MOST LOOSELY COUPLED ALTERNATIVE.
  74. 74. MULTIPROCESSING SYSTEMS • DEGREE OF COUPLING: NETWORK SW NETWORK SW NETWORK LINK OS OSCLUSTER LINK SYSTEM MEMORY SYSTEM MEMORY SYSTEM BUS CPU CPUMULTIPROCESSOR BUS
  75. 75. MULTIPROCESSING SYSTEMS • MULTIPROCESSORS MAY ALSO BE CLASSIFIED AS AUTOCRATIC OR EGALITARIAN. • AUTOCRATIC CONTROL IS SHOWN WHERE A MASTER-SLAVE RELATIONSHIP EXISTS BETWEEN THE PROCESSORS. • EGALITARIAN CONTROL GIVES ALL PROCESSORS EQUAL CONTROL OF SHARED BUS ACCESS.
  76. 76. MULTIPROCESSING SYSTEMS • MULTIPROCESSING SYSTEMS WITH SEPARATE PROCESSORS AND MEMORIES MAY BE CLASSIFIED AS „DANCE HALL‟ CONFIGURATIONS IN WHICH THE PROCESSORS ARE LINED UP ON ONE SIDE WITH THE MEMORIES FACING THEM. • CROSS CONNECTIONS ARE MADE BY A SWITCHING NETWORK.
  77. 77. MULTIPROCESSING SYSTEMS • DANCE HALL CONFIGURATION: CPU 1 CPU 2 CPU 3 CPU 4 SWITCHI NG NETWO RK MEM 1 MEM 2 MEM 3 MEM 4
  78. 78. MULTIPROCESSING SYSTEMS • ANOTHER CONFIGURATION IS „BOUDOIR CONFIG‟ IN WHICH EACH PROCESSOR IS CLOSELY COUPLED WITHITS OWN MEMORY AND A NETWORK OF SWITCHES IS USED TO LINK THE PROCESSOR-MEMORY PAIRS. CPU 1 MEM 1 CPU 2 MEM 2 CPU 3 MEM 3 CPU 4 MEM 4 SWITCHING NETWORK
  79. 79. MULTIPROCESSING SYSTEMS • ANOTHER TERM, WHICH IS USED TO DESCRIBE A FORM OF PARALLEL COMPUTING IS CONCURRENCY. • IT DENOTES INDEPENDENT, AYNCHRONOUS OPERATION OF A COLLECTION OF PARALLEL COMPUTING DEVICES RATHER THAN THE SYNCHRONOUS OPERATION OF DEVICES IN A MULTIPROCESSOR SYSTEM.
  80. 80. SYSTOLIC ARRAYS • IT MAY BE TERMED AS MISD SYSTEM. • IT IS A REGULAR ARRAY OF PROCESSING ELEMENTS, EACH COMMUNICATING WITH ITS NEAREST NEIGHBOURS AND OPERATING SYNCHRONOUSLY UNDER THE CONTROL OF A COMMON CLOCK WITH A RATE LIMITED BY THE SLOWEST PROCESSOR IN THE ARRAY. • THE ERM SYSTOLIC IS DERIVED FROM THR RHYTHMIC CONTRACTION OF THE HEART, ANALOGOUS TO THE RHYTHMIC PUMPING OF DATA THROUGH AN ARRAY OF PROCESSING ELEMENTS.
  81. 81. WAVEFRONT ARRAY • IT IS A REGULAR ARRAY OF PROCESSING ELEMENTS, EACH COMMUNICATING WITH ITS NEAREST NEIGHBOURS BUT OPERATING WITH NO GLOBAL CLOCK. • IT EXHIBITS CONCURRENCY AND IS DATA DRIVEN. • THE OPERATION OF EACH PROCESSOR IS CONTROLLED LOCALLY AND IS ACTIVATED BY THE ARRIVAL OF DATA AFTER ITS PREVIOUS OUTPUT HAS BEEN DELIVERED TO THE APPROPRIATE NEIGHBOURING PROCESSOR.
  82. 82. WAVEFRONT ARRAY • PROCESSING WAVEFRONTS DEVELOP ACROSS THE ARRAY AS PROCESSORS PASS ON THE OUTPUT DATA TO THEIR NEIGHBOUR. HENCE THE NAME.
  83. 83. GRANULARITY OF PARALLELISM • PARALLEL PROCESSING EMPHASIZES THE USE OF SEVERAL PROCESSING ELEMENTS WITH THE MAIN OBJECTIVE OF GAINING SPEED IN CARRYING OUT A TIME CONSUMING COMPUTING JOB • A MULTI-TASKING OS EXECUTES JOB CONCURRENTLY BUT THE OBJECTIVE IS TO EFFECT THE CONTINUED PROGRESS OF ALL THE TASKS BY SHARING THE RESOURCES IN AN ORDERLY MANNER.
  84. 84. GRANULARITY OF PARALLELISM • THE PARALLEL PROCESSING EMPHASIZES THE EXPLOITATION OF CONCURRENCY AVAILABLE IN A PROBLEM FOR CARRYING OUT THE COMPUTATION BY EMPLOYING MORE THAN ONE PROCESSOR TO ACHIEVE BETTER SPEED AND/OR THROUGHPUT. • THE CONCURRENCY IN THE COMPUTING PROCESS COULD BE LOOKED UPON FOR PARALLEL PROCESSING AT VARIOUS LEVELS (GRANULARITY OF PARALLELISM) IN THE SYSTEM.
  85. 85. GRANULARITY OF PARALLELISM • THE FOLLOWING GRANULARITIES OF PARALLELISM MAY BE IDENTIFIED IN ANY EXISTING SYSTEM: o PROGRAM LEVEL PARALLELISM o PROCESS OR TASK LEVEL PARALLELISM o PARALLELISM AT THE LEVEL OF GROUP OF STATEMENTS o STATEMENT LEVEL PARALLELISM o PARALLELISM WITHIN A STATEMENT o INSTRUCTION LEVEL PARALLELISM o PARALLELISM WITHIN AN INSTRUCTION o LOGIC AND CIRCUIT LEVEL PARALLELISM
  86. 86. GRANULARITY OF PARALLELISM • THE GRANULARITIES ARE LISTED IN THE INCREASING DEGREE OF FINENESS. • GRANULARITIES AT LEVELS 1,2 AND 3 CAN BE EASILY IMPLEMENTED ON A CONVENTIONAL MULTIPROCESSOR SYSTEM. • MOST MULTI-TASKING OS ALLOW CREATION AND SCHEDULING OF PROCESSES ON THE AVAILABLE RESOURCES.
  87. 87. GRANULARITY OF PARALLELISM • SINCE A PROCESS REPRESENTS A SIZABLE CODE IN TERMS OF EXECUTION TIME, THE OVERLOADS IN EXPLOITING THE PARALLELISM AT THESE GRANULARITIES ARE NOT EXCESSIVE. • IF THE SAME PRINCIPLE IS APPLIED TO THE NEXT FEW LEVELS, INCREASED SCHEDULING OVERHEADS MAY NOT WARRANT PARALLEL EXECUTION • IT IS SO BECAUSE THE UNIT OF WORK OF A MULTI- PROCESSOR IS CURRENTLY MODELLED AT THE LEVEL OF A PROCESS OR TASK AND IS REASONABLY SUPPORTED ON THE CURRENT ARCHITECTURES.
  88. 88. GRANULARITY OF PARALLELISM • THE LAST THREE LEVELS ARE BEST HANDLED BY HARDWARE. SEVERAL MACHINES HAVE BEEN BUILT TO PROVIDE THE FINE GRAIN PARALLELISM IN VARYING DEGREES. • A MACHINE HAVING INST LEVEL PARALLELISM EXECUTES SEVERAL INSTS SIMULTANEOUSLY. EXAMPLES ARE PIPELINE INST PROCESSORS, SYNCHRONOUS ARRAY PROCESSORS, ETC. • CIRCUIT LEVEL PARALLELISM EXISTS IN MOST MACHINES IN THE FORM OF PROCESSING MULTIPLE BITS/BYTES SIMULTANEOUSLY.
  89. 89. PARALLEL ARCHITECTURES • THERE ARE NUMEROUS ARCHITECTURES THAT HAVE BEEN USED IN THE DESIGN OF HIGH SPEED COMPUTERS. IT FALLS BASICALLY INTO 2 CLASSES:  GENERAL PURPOSE &  SPECIAL PURPOSE o GENERAL PURPOSE ARCHITECTURES ARE DESIGNED TO PROVIDE THE RATED SPEEDS AND OTHER COMPUTING REQUIREMENTS FOR VARIETY OF PROBLEMS WITH SAME PERFORMANCE.
  90. 90. PARALLEL ARCHITECTURES • THE IMPORTANT ARCHITECTURAL IDEAS BEING USED IN DESIGNING GEN PURPOSE HIGH SPEED COMPUTERS ARE:  PIPELINED ARCHITECTURES  ASYNCHRONOUS MULTI-PROCESSORS  DATA-FLOW COMPUTERS
  91. 91. PARALLEL ARCHITECTURES • THE SPECIAL PURPOSE MACHINES HAVE TO EXCEL FOR WHAT THEY HAVE BEEN DESIGNED. IT MAY OR MAY NOT DO SO FOR OTHER APPLICATIONS. SOME OF THE IMPORTANT ARCHITECTURAL IDEAS FOR DEDICATED COMPUTERS ARE:  SYNCHRONOUS MULTI-PROCESSORS(ARRAY PROCESSOR)  SYSTOLIC ARRAYS  NEURAL NETWORKS
  92. 92. ARRAY PROCESSORS • IT CONSISTS OF SEVERAL PE, ALL OF WHICH EXECUTE THE SAME INST ON DIFFERENT DATA. • THE INSTS ARE FETCHED AND BROADCAST TO ALL THE PE BY A COMMON CU. • THE PE EXECUTE INSTS ON DATA RESIDING IN THEIR OWN MEMORY. • THE PE ARE LINKED VIA AN INTERCONNECTION NETWORK TO CARRY OUT DATA COMMUNICATION BETWEEN THEM.
  93. 93. ARRAY PROCESSORS • THERE ARE SEVERAL WAYS OF CONNECTING PE • THESE MACHINES REQUIRE SPECIAL PROGRAMMING EFFORTS TO ACHIEVE THE SPEED ADVANTAGE • THE COMPUTATIONS ARE CARRIED OUT SYNCHRONOUSLY BY THE HW AND THEREFORE SYNC IS NOT AN EXPLICIT PROBLEM
  94. 94. ARRAY PROCESSORS • USING AN INTERCONNECTION NETWORK: PE1 PE2 PE3 PE4 PEn CU AND SCALAR PROCESS OR INTERCONNECTION NETWORK
  95. 95. ARRAY PROCESSORS • USING AN ALIGNMENT NETWORK: PE0 PE1 PE2 PEn ALIGNMENT NETWORK MEM O MEM1 MEM2 MEMK CONTROL UNIT AND SCALAR PROCESSOR
  96. 96. CONVENTIONAL MULTI-PROCESSORS • ASYNCHRONOUS MULTIPROCESSORS • BASED ON MULTIPLE CPUs AND MEM BANKS CONNECTED THROUGH EITHER A BUS OR CONNECTION NETWORK IS A COMMONLY USED TECHNIQUE TO PROVIDE INCREASED THROUGHPUT AND/OR RESPONSE TIME IN A GENERAL PURPOSE COMPUTING ENVIRONMENT.
  97. 97. CONVENTIONAL MULTI-PROCESSORS • IN SUCH SYSTEMS, EACH CPU OPERATES INDEPENDENTLY ON THE QUANTUM OF WORK GIVEN TO IT • IT HAS BEEN HIGHLY SUCCESSFUL IN PROVIDING INCREASED THROUGHPUT AND/OR RESPONSE TIME IN TIME SHARED SYSTEMS. • EFFECTIVE REDUCTION OF THE EXECUTION TIME OF A GIVEN JOB REQUIRES THE JOB TO BE BROKEN INTO SUB-JOBS THAT ARE TO BE HANDLED SEPARATELY BY THE AVAILABLE PHYSICAL PROCESSORS.
  98. 98. CONVENTIONAL MULTI-PROCESSORS • IT WORKS WELL FOR TASKS RUNNING MORE OR LESS INDEPENDENTLY ie., FOR TASKS HAVING LOW COMMUNICATION AND SYNCHRONIZATION REQUIREMENTS. • COMM AND SYNC IS IMPLEMENTED EITHER THROUGH THE SHARED MEMORY OR BY MESSAGE SYSTEM OR THROUGH THE HYBRID APPROACH.
  99. 99. CONVENTIONAL MULTI-PROCESSORS • SHARED MEMORY ARCHITECTURE: MEMORY CPU CPU CPU COMMON BUS ARCHITECTURE MEM0 MEM1 MEMn PROCESSOR MEMORY SWITCH CPU CPU CPU SWITCH BASED MULTIPROCESSOR
  100. 100. CONVENTIONAL MULTI-PROCESSORS • MESSAGE BASED ARCHITECTURE: ……………… PE 1 PE 2 PE n CONNECTION NETWORK
  101. 101. CONVENTIONAL MULTI-PROCESSORS • HYBRID ARCHITECTURE: PE 1 PE 2 PE n CONNECTION NETWORK MEM 1 MEM 2 MEM k
  102. 102. CONVENTIONAL MULTI-PROCESSORS • ON A SINGLE BUS SYSTEM, THERE IS A LIMIT ON THE NUMBER OF PROCESSORS THAT CAN BE OPERATED IN PARALLEL. • IT IS USUALLY OF THE ORDER OF 10. • COMM NETWORK HAS THE ADVANTAGE THAT THE NO OF PROCESSORS CAN GROW WITHOUT LIMIT, BUT THE CONNECTION AND COMM COST MAY DOMINATE AND THUS SATURATE THE PERFORMANCE GAIN. • DUE TO THIS REASON, HYBRID APPROACH MAY BE FOLLOWED • MANY SYSTEMS USE A COMMON BUS ARCH FOR GLOBAL MEM, DISK AND I/O WHILE THE PROC MEM TRAFFIC IS HANDLED BY SEPARATE BUS.
  103. 103. DATA FLOW COMPUTERS • A NEW FINE GRAIN PARALLEL PROCESSING APPROACH BASED ON DATAFLOW COMPUTING MODEL HAS BEEN SUGGESTED BY JACK DENNIS IN 1975. • HERE, A NO OF DATA FLOW OPERATORS, EACH CAPABLE OF DOING AN OPERATION ARE EMPLOYED. • A PROGRAM FOR SUCH A MACHINE IS A CONNECTION GRAPH OF THE OPERATORS.
  104. 104. DATA FLOW COMPUTERS • THE OPERATORS FORM THE NODES OF THE GRAPH WHILE THE ARCS REPRESENT THE DATA MOVEMENT BETWEEN NODES. • AN ARC IS LABELED WITH A TOKEN TO INDICATE THAT IT CONTAINS THE DATA. • A TOKEN IS GENERATED ON THE OUTPUT OF A NODE WHEN IT COMPUTES THE FUNCTION BASED ON THE DATA ON ITS INPUT ARCS.
  105. 105. DATA FLOW COMPUTERS • THIS IS KNOWN AS FIRING OF THE NODE. • A NODE CAN FIRE ONLY WHEN ALL OF ITS INPUT ARCS HAVE TOKENS AND THERE IS NO TOKEN ON THE OUTPUT ARC. • WHEN A NODE FIRES, IT REMOVES THE INPUT TOKENS TO SHOW THAT THE DATA HAS BEEN CONSUMED. • USUALLY, COMPUTATION STARTS WITH ARRIVAL OF DATA ON THE INPUT NODES OF THE GRAPH.
  106. 106. DATA FLOW COMPUTERS • DATA FLOW GRAPH FOR THE COMPUTATION: • A = 5 + C – D 5 C D + - COMPUTATION PROGRESSES AS PER DATAAVAILABILITY
  107. 107. DATA FLOW COMPUTERS • MANY CONVENTIONAL MACHINES EMPLOYING MULTIPLE FUNCTIONAL UNITS EMPLOY THE DATA FLOW MODEL FOR SCHEDULING THE FUNCTIONAL UNITS. • EXAMPLE EXPERIMENTAL MACHINES ARE MANCHESTER MACHINE (1984) AND MIT MACHINE. • THE DATA FLOW COMPUTERS PROVIDE FINE GRANULARITY OF PARALLEL PROCESSING, SINCE THE DATA FLOW OPERATORS ARE TYPICALLY ELEMENTARY ARITHMETIC AND LOGIC OPERATORS.
  108. 108. DATA FLOW COMPUTERS • IT MAY PROVIDE AN EFFECTIVE SOLUTION FOR USING VERY LARGE NUMBER OF COMPUTING ELEMENTS IN PARALLEL. • WITH ITS ASYNCHRONOUS DATA DRIVEN CONTROL, IT HAS A PROMISE FOR EXPLOITATION OF THE PARALLELISM AVAILABLE BOTH IN THE PROBLEM AND THE MACHINE. • CURRENT IMPLEMENTATIONS ARE NO BETTER THAN CONVENTIONAL PIPELINED MACHINES EMPLOYING MULTIPLE FUNCTIONAL UNITS.
  109. 109. SYSTOLIC ARCHITECTURES • THE ADVENT OF VLSI HAS MADE IT POSSIBLE TO DEVELOP SPECIAL ARCHITECTURES SUITABLE FOR DIRECT IMPLEMENTATION IN VLSI. • SYSTOLIC ARCHITECTURES ARE BASICALLY PIPELINES OPERATING IN ONE OR MORE DIMENSIONS. • THE NAME SYSTOLIC HAS BEEN DERIVED FROM THE ANALOGY OF THE OPERATION OF BLOOD CIRCULATION SYSTEM THROUGH THE HEART.
  110. 110. SYSTOLIC ARCHITECTURES • CONVENTIONAL ARCHITECTURES OPERATE ON THE DATA USING LOAD AND STORE OPERATIONS FROM THE MEMORY. • PROCESSING USUALLY INVOLVES SEVERAL OPERATIONS. • EACH OPERATION ACCESSES THE MEMORY FOR DATA, PROCESSES IT AND THEN STORES THE RESULT. THIS REQUIRES A NO OF MEM REFERENCES.
  111. 111. SYSTOLIC ARCHITECTURES • CONVENTIONAL PROCESSING: MEMORY F1 F2 Fn MEMORY F1 F2 Fn • SYSTOLIC PROCESSING
  112. 112. SYSTOLIC ARCHITECTURES • IN SYSTOLIC PROCESSING, DATA TO BE PROCESSED FLOWS THROUGH VARIOUS OPERATION STAGES AND THEN FINALLY IT IS PUT IN THE MEMORY. • SUCH AN ARCHITECTURE CAN PROVIDE BERY HIGH COMPUTING THROUGHPUT DUE TO REGULAR DATAFLOW AND PIPELINE OPERATION. • IT MAY BE USEFUL IN DESIGNING SPECIAL PROCESSORS FOR GRAPHIC, SIGNAL & IMAGE PROCESSING.
  113. 113. PERFORMANCE OF PARALLEL COMPUTERS • AN IMPORTANT MEASURE OF PARALLEL ARCHITECTURE IS SPEEDUP. • LET n = NO. OF PROCESSORS; Ts = SINGLE PROC. EXEC TIME; Tn = N PROC. EXEC. TIME, • THEN • SPEEDUP S = Ts/Tn
  114. 114. AMDAHL‟S LAW • 1967 • BASED ON A VERY SIMPLE OBSERVATION. • A PROGRAM REQUIRING TOTAL TIME T FOR SEQUENTIAL EXECUTION SHALL HAVE SOME PART WHICH IS INHERENTLY SEQUENTIAL. • IN TERMS OF TOTAL TIME TAKEN TO SOLVE THE PROBLEM, THIS FRACTION OF COMPUTING TIME IS AN IMPORTANT PARAMETER.
  115. 115. AMDAHL‟S LAW • LET f = SEQ. FRACTION FOR A GIVEN PROGRAM. • AMDAHL‟S LAW STATES THAT THE SPEED UP OF A PARALLEL COMPUTER IS LIMITED BY S <= 1/[f + (1 – f )/n]  SO, IT SAYS THAT WHILE DESIGNING A PARALLEL COMP, CONNECT SMALL NO OF EXTREMELY POWERFUL PROCS AND LARGE NO OF INEXPENSIVE PROCS.
  116. 116. AMDAHL‟S LAW • CONSIDER TWO PARALLEL COMPS. Me AND Mi. Me IS BUILT USING POWERFUL PROCS. CAPABLE OF EXECUTING AT A SPEED OF M MEGAFLOPS. • THE COMP Mi IS BUILT USING CHEAP PROCS. AND EACH PROC. OF Mi EXECUTES r.M MEGAFLOPS, WHERE 0 < r < 1 • IF THE MACHINE Me ATTEMPTS A COMPUTATION WHOSE INHERENTLY SEQ.FRACTION f > r THEN Mi WILL EXECUTE COMPS. MORE SLOWLY THAN A SINGLE PROC. OF Mi.
  117. 117. AMDAHL‟S LAW • PROOF: LET W = TOTAL WORK; M = SPEED OF Mi (IN Mflops) R.m = SPEED OF PE OF Ma; f.W = SEQ WORK OF JOB; T(Ma) = TIME TAKEN BY Ma FOR THE WORK W, T(Mi) = TIME TAKEN BY Mi FOR THE WORK W, THEN TIME TAKEN BY ANY COMP = T = AMOUNT OF WORK/SPEED
  118. 118. AMDAHL‟S LAW • T(Ma) = TIME FOR SEQ PART + TIME FOR PARALLEL PART = ((f.W)/(r.M)) + [((1-f).W/n)/(r.M)] = (W/M).(f/r) IF n IS INFINITELY LARGE. T(Me) = (W/M) [ASSUMING ONLY 1 PE] SO IF f > r, THEN T(Ma) > T(Mi)
  119. 119. AMDAHL‟S LAW • THE THEOREM IMPLIES THAT A SEQ COMPONENT FRACTION ACCEPTABLE FOR THE MACHINE Mi MAY NOT BE ACCEPTABLE FOR THE MACHINE Ma. • IT IS NOT GOOD TO HAVE A LARGER PROCESSING POWER THAT GOES AS A WASTE. PROCS MUST MAINTAIN SOME LEVEL OF EFFICIENCY.
  120. 120. AMDAHL‟S LAW • RELATION BETWEEN EFFICIENCY e AND SEQ FRACTION r: • S <= 1/[f + (1 – f )/n] • EFFICIENCY e = S/n • SO, e <= 1/[f.n + 1 – f ] • IT SAYS THAT FOR CONSTANT EFFICIENCY, THE FRACTION OF SEQ COMP OF AN ALGO MUST BE INVERSELY PROPORTIONAL TO THE NO OF PROCESSORS. • THE IDEA OF USING LARGE NO OF PROCS MAY THUS BE GOOD FOR ONLY THOSE APPLICATIONS FOR WHICH IT IS KNOWN THAT THE ALGOS HAVE A VERY SMALL SEQ FRACTION f.
  121. 121. MINSKY‟S CONJECTURE • 1970 • FOR A PARALLEL COMPUTER WITH n PROCS, THE SPEEDUP S SHALL BE PROPORTIONAL TO log2n. • MINSKY‟S CONJECTURE WAS VERY BAD FOR THE PROPONENTS OF LARGE SCALE PARALLEL ARCHITECTURES. • FLYNN & HENNESSY (1980) THEN GAVE THAT SPEEDUP OF n PROCESSOR PARALEL SYSTEM IS LIMITED BY S<= [n/(log2n)]
  122. 122. PARALLEL ALGORITHMS • IMP MEASURE OF THE PERFORMANCE OF ANY ALGO IS ITS TIME AND SPACE COMPLEXITY. THEY ARE SPECIFIED AS SOME FUNCTION OF THE PROBLEM SIZE. • MANY TIMES, THEY DEPEND UPON THE USED DATA STRUCTURE. • SO, ANOTHER IMP MEASURE IS THE PREPROCESSING TIME COMPLEXITY TO GENERATE THE DESIRED DATA STRUCTURE.
  123. 123. PARALLEL ALGORITHMS • PARALLEL ALGOS ARE THE ALGOS TO BE RUN ON PARALLEL MACHINE. • SO, COMPLEXITY OF COMM AMONGST PROCESORS ALSO BECOMES AN IMPORTANT MEASURE. • SO, AN ALGO MAY FARE BADLY ON ONE MACHINE AND MUCH BETTER ON THE OTHER.
  124. 124. PARALLEL ALGORITHMS • DUE TO THIS REASON, MAPPING OF THE ALGO ON THE ARCHITECTURE IS AN IMP ACTIVITY IN THE STUDY OF PARALLEL ALGOS. • SPEEDUP AND EFFICIENCY ARE ALSO IMP PERFORMANCE MEASURES FOR A PARALLEL ALGO WHEN MAPPED ON TO A GIVEN ARCHITECTURE.
  125. 125. PARALLEL ALGORITHMS • A PARALLEL ALGO FOR A GIVEN PROBLEM MAY BE DEVELOPED USING ONE OR MORE OF THE FOLLOWING: 1. DETECT AND EXPLOIT THE INHERENT PARALLELISM AVAILABLE IN THE EXISTING SEQUENTIAL ALGORITHM 2. INDEPENDENTLY INVENT A NEW PARALLEL ALGORITHM 3. ADAPT AN EXISTING PARALLEL ALGO THAT SOLVES A SIMILAR PROBLEM.
  126. 126. DISTRIBUTED PROCESSING • PARALLEL PROCESSING DIFFERS FROM DISTRIBUTED PROCESSING IN THE SENSE THAT IT HAS (1) CLOSE COUPLING BETWEEN THE PROCESSORS & (2) COMMUNICATION FAILURES MATTER A LOT. • PROBLEMS MAY ARISE IN DISTRIBUTED PROCESSING BECAUSE OF (1) TIME UNCERTAINTY DUE TO DIFFERING TIME IN LOCAL CLOCKS, (2) INCOMPLETE INFO ABOUT OTHER NODES IN THE SYSTEM, (3) DUPLICATE INFO WHICH MAY NOT BE ALWAYS CONSISTENT.
  127. 127. PIPELINING PROCESSING • A PIPELINE CAN WORK WELL WHEN: 1. THE TIME TAKEN BY EACH STAGE IS NEARLY THE SAME. 2. IT REQUIRES A STEADY STEAM OF JOBS, OTHERWISE UTILIZATION WILL BE POOR. 3. IT HONOURS THE PRECEDENCE CONSTRAINTS OF SUB-STEPS OF JOBS. IT IS THE MOST IMP PROPERTY OF PIPELINE. IT ALLOWS PARALLEL EXECUTION OF JOBS WHICH HAVE NO PARALLELISM WITHIN INDIVIDUAL JOBS THEMSELVES.
  128. 128. PIPELINING PROCESSING • IN FACT, A JOB WHICH CAN BE BROKEN INTO A NO OF SEQUENTIAL STEPS IS THE BASIS OF PIPELINE PROCESSING. • THIS IS DONE BY INTRODUCING TEMPORAL PARALLELISM WHICH MEANS EXECUTING DIFFERENT STEPS OF DIFFERENT JOBS INSIDE THE PIPELINE. • THE PERFORMANCE IN TERMS OF THROUGHPUT IS GUARANTEED IF THERE ARE ENOUGH JOBS TO BE STREAMED THROUGH THE PIPELINE, ALTHOUGH AN INDIVIDUAL JOB FINISHES WITH A DELAY EQUALLING THE TOTAL DELAY OF ALL THE STAGES.
  129. 129. PIPELINING PROCESSING • THE FOURTH IMP THING IS THAT THE STAGES IN THE PIPELINE ARE SPECIALIZED TO DO PARTICULAR SUBFUNCTIONS, UNLIKE IN CONVENTIONAL PARALLEL PROCESSORS WERE EQUIPMENT IS REPLICATED. • IT AMOUNTS TO SAYING THAT DUE TO SPECIALIZATION, THE STAGE PROC COULD BE DESIGNED WITH BETTER COST AND SPEED, OPTIMISED FOR THE SPECIALISED FUNCTION OF THE STAGE
  130. 130. PERFORMANCE MEASURES OF PIPELINE • EFFICIENCY, SPEEDUP AND THROUGHPUT • EFFICIENCY: LET n BE THE LENGTH OF PIPE AND m BE THE NO OF TASKS RUN ON THE PIPE, THEN EFFICIENCY e CAN BE DEFINED AS • e = [(m.n)/((m+n-1).(n))] • WHEN n>>m, e TENDS TO m/n (A SMALL FRACTION) • WHEN n<<m, e TENDS TO 1 • WHEN n = m, e IS APPROX 0.5 (m,n > 4)
  131. 131. PERFORMANCE MEASURES OF PIPELINE • SPEEDUP = S = [((n.ts).m)/((m+n-1).ts)] = [(m.n)/(n+m-1)]  WHEN n>>m, S=m (NO. OF TASKS RUN)  WHEN n<<m, S=n (NO OF STAGES)  WHEN n = m, S=n/2 (m,n > 4)
  132. 132. PERFORMANCE MEASURES OF PIPELINE • THROUGHPUT = Th = [m/((n+m-1).ts)] = e/ts WHERE ts IS TIME THAT ELAPSES AT 1 STAGE. • WHEN n>>m, Th = m/(n.ts) • WHEN n<<m, Th = 1/ts • WHEN n = m, Th = 1/(2.ts) (n,m > 4) • SO, SPEEDUP IS A FUNCTION OF n AND ts. FOR A GIVEN TECHNOLOGY ts IS FIXED, SO AS LONG AS ONE IS FREE TO CHOOSE n, THERE IS NO LIMIT ON THE SPEEDUP OBTAINABLE FROM A PIPELINED MECHANISM.
  133. 133. OPTIMAL PIPE SEGMENTATION • IN HOW MANY SUBFUNCTIONS A FUNCTION SHOULD BE DIVIDED? • LET n = NO OF STAGES, T= TIME FOR NON- PIPELINED IMPLEMENTATION, D = LATCH DELAY AND c = COST OF EACH STAGE • STAGE COMPUTE TIME = T/n (SINCE T IS DIVIDED EQUALLY FOR n STAGES) • PIPELINE COST= c.n + k WHERE k IS A CONSTANT REFLECTING SOME COST OVERHEAD.
  134. 134. OPTIMAL PIPE SEGMENTATION • SPEED (TIME PER OUTPUT) = (T/n + D) • ONE OF THE IMPORTANT PERFORMANCE MEASURE IS THE PRODUCT OF SPEED AND COST DENOTED BY p. • p = [(T/n) + D).(c.n +k)] = T.c +D.c.n + (k.T)/n + k.D • TO OBTAIN A VALUE OF n WHICH GIVES BEST PERFORMANCE, WE DIFFERENTIATE p w r t n AND EQUATE IT TO ZERO • dp/dn = D.c –(k.T)/n2 = 0 • n = SQRT [(k.T)/(D.c)]
  135. 135. PIPELINE CONTROL • IN A NON-PIPELINED SYSTEM, ONE INST IS FULLY EXECUTED BEFORE THE NEXT ONE STARTS, THUS MATCHING THE ORDER OF EXECUTION. • IN A PIPELINED SYSTEM, INST EXECUTION IS OVERLAPPED. SO, IT CAN CAUSE PROBLEMS IF NOT CONSIDERED PROPERLY IN THE DESIGN OF CONTROL. • EXISTENCE OF SUCH DEPENDENCIES CAUSES “HAZARDS” • CONTROL STRUCTURE PLAYS AN IMP ROLE IN THE OPERATIONAL EFFICIENCY AND THROUGHPUT OF THE MACHINE.
  136. 136. PIPELINE CONTROL • THERE ARE 2 TYPES OF CONTROL STRUCTURES IMPLEMENTED ON COMMERCIAL SYSTEMS. • THE FIRST ONE IS CHARACTERISED BY A STREAMLINE FLOW OF THE INSTS IN THE PIPE. • IN THIS, INSTS FOLLOW ONE AFTER ANOTHER SUCH THAT THE COMPLETION ORDERING IS THE SAME AS THE ORDER OF INITIATION. • THE SYSTEM IS CONCEIVED AS A SEQUENCE OF FUNCTIONAL MODULES THROUGH WHICH THE INSTS FLOW ONE AFTER ANOTHER WITH AN “INTERLOCK” BETWEEN THE ADJACENT STAGES TO ALLOW THE TRANSFER OF DATA FROM ONE STAGE TO ANOTHER.
  137. 137. PIPELINE CONTROL • THE INTERLOCK IS NECESSARY BECAUSE THE PIPE IS ASYNCHRONOUS DUE TO VARIATIONS IN THE SPEEDS OF DIFFERENT STAGES. • IN THESE SYSTEMS, THE BOTTLRNECKS APPEAR DYNAMICALLY AT ANY STAGE AND THE INPUT TO IT IS HALTED TEMPORARILY. • THE SECOND TYPE OF CONTROL IS MORE FLEXIBLE, POWERFUL BUT EXPENSIVE.
  138. 138. PIPELINE CONTROL • IN SUCH SYSTEMS, WHEN A STAGE HAS TO SUSPEND THE FLOW OF A PARTICULAR INSTRUCTION, IT ALLOWS OTHER INSTS TO PASS THROUGH THE STAGE RESULTING IN AN OUT- OF-TURN EXECUTION OF THE INSTS. • THE CONTROL MECHANISM IS DESIGNED SUCH THAT EVEN THOUGH THE INSTS ARE EXECUTED OUT-OF-TURN, THE BEHAVIOUR OF THE PROGRAM IS SAME AS IF THEY WERE EXECUTED IN THE ORIGINAL SEQUENCE. • SUCH CONTROL IS DESIRABLE IN A SYSTEM HAVING MULTIPLE ARITHMETIC PIPELINES OPERATING IN PARALLEL.
  139. 139. PIPELINE HAZARDS • THE HARDWARE TECHNIQUE THAT DETECTS AND RESOLVES HAZARDS IS CALLED INTERLOCK. • A HAZARD OCCURS WHENEVER AN OBJECT WITHIN THE SYSTEM (REF, FLAG, MEM LOCATION) IS ACCESSED OR MODIFIED BY 2 SEPARATE INSTS THAT ARE CLOSE ENOUGH IN THE PROGRAM SUCH THAT THEY MAY BE ACTIVE SIMULTANEOUSLY IN THE PIPELINE. • HAZARDS ARE OF 3 KINDS: RAW, WAR AND WAW
  140. 140. PIPELINE HAZARDS • ASSUME THAT AN INST j LOGICALLY FOLLOWS AN INST i. • RAW HAZARD: IT OCCURS BETWEEN 2 INSTS WHEN INST j ATTEMPTS TO READ SOME OBJECT THAT IS BEING MODIFIED BY INST i. • WAR HAZARD: IT OCCURS BETWEEN 2 INSTS WHEN THE INST j ATTEMPTS TO WRITE ONTO SOME OBJECT THAT IS BEING READ BY THE INST i. • WAW HAZARD: IT OCCURS WHEN THE INST j ATTEMPTS TO WRITE ONTO SOME OBJECT THAT IS ALSO REQUIRED TO BE MODIFIED BY THE INST i.
  141. 141. PIPELINE HAZARDS • THE DOMAIN (READ SET) OF AN INST k, DENOTED BY Dk, IS THE SET OF ALL OBJECTS WHOSE CONTENTS ARE ACCESSED BY THE INST k. • THE RANGE (WRITE SET) OF AN INST k, DENOTED BY Rk, IS THE SET OF ALL OBJECTS UPDATED BY THE INST k. • A HAZARD BETWEEN 2 INSTS i AND j (WHERE j FOLLOWS i) OCCURS WHENEVER ANY OF THE FOLLOWING HOLDS: • Ri * Dj <>{ } (RAW) • Di * Rj <> { } (WAR) • Ri * Rj <> { } (WAW), WHERE * IS INTERSECTION OPERATION AND { } IS EMPTY SET.
  142. 142. HAZARD DETECTION & REMOVAL • TECHNIQUES USED FOR HAZARD DETECTION CAN BE CLASSIFIED INTO 2 CLASSES:  CENTRALIZE ALL THE HAZARD DETECTION IN ONE STAGE (USUALLY IU) AND COMPARE THE DOMAIN AND RANGE SETS WITH THOSE OF ALL THE INSTS INSIDE THE PIPELINE  ALLOW THE INSTS TO TRAVEL THROUGH THE PIPELINE UNTIL THE OBJECT EITHER FROM THE DOMAIN OR RANGE IS REQUIRED BY THE INST. AT THIS POINT, CHECK IS MADE FOR A POTENTIAL HAZARD WITH ANY OTHER INST INSIDE THE PIPELINE.
  143. 143. HAZARD DETECTION & REMOVAL • FIRST APPROACH IS SIMPLE BUT SUSPENDS THE INST FLOW IN THE IU ITSELF, IF THE INST FETCHED IS IN HAZARD WITH THOSE INSIDE THE PIPELINE. • THE SECOND APPROACH IS MORE FLEXIBLE BUT THE HARDWARE REQUIRED GROWS AS A SQUARE OF THE NO OF STAGES.
  144. 144. HAZARD DETECTION & REMOVAL • THERE ARE 2 APPROACHS FOR HAZARD REMOVAL:  SUSPEND THE PIPELINE INITIATION AT THE POINT OF HAZARD. THUS, IF AN INST j DISCOVERS THAT THERE IS A HAZARD WITH THE PREVIOUSLY INITIATED INST i, THEN ALL THE INSTS j+1, j+2,… ARE STOPPED IN THEIR TRACKS TILL THE INST i HAS PASSED THE POINT OF HAZARD.  SUSPEND j BUT ALLOW THE INSTS j+1, j+2, … TO FLOW.
  145. 145. HAZARD DETECTION & REMOVAL • THE FIRST APPROACH IS SIMPLE BUT PENALIZES ALL THE INSTS FOLLOWING j. • SECOND APPROACH IS EXPENSIVE. • IF THE PIPELINE STAGES HAVE ADDITIONAL BUFFERS BESIDES A STAGING LATCH, THEN IT IS POSSIBLE TO SUSPEND AN INST BECAUSE OF HAZARD. • AT EACH POINT IN THE PIPELINE, WHERE DATA IS TO BE ACCESSED AS AN INPUT TO SOME STAGE AND THERE IS A RAW HAZARD, ONE CAN LOAD ONE OF THE STAGING LATCH NOT WITH THE DATA BUT ID OF THE STAGE THAT WILL PRODUCE IT.
  146. 146. HAZARD DETECTION & REMOVAL • THE WAITING INST THEN IS FROZEN AT THIS STAGE UNTIL THE DATA IS AVAILABLE. • SINCE THE STAGE HAS MULTIPLE STAGING LATCHES IT CAN ALLOW OTHER INSTS TO PASS THROUGH IT WHILE THE RAW DEPENDENT ONE IS FROZEN. • ONE CAN INCLUDE LOGIC IN THE STAGE TO FORWARD THE DATA WHICH WAS IN RAW HAZARD TO THE WAITING STAGE. • THIS FORM OF CONTROL ALLOWS HAZARD RESOLUTION WITH THE MINIMUM PENALTY TO OTHER INSTS.
  147. 147. HAZARD DETECTION & REMOVAL • THIS TECHNIQUE IS KNOWN BY THE NAME “INTERNAL FORWARDING” SINCE THE STAGES ARE DESIGNED TO CARRY OUT AUTOMATIC ROUTING OF THE DATA TO THE REQUIRED PLACE USING IDENTIFICATION CODES (IDs). • IN FACT, MANY OF THE DATA DEPENDENT COMPUTATIONS ARE CHAINED BY MEANS OF ID TAGS SO THAT UNNECESSARY ROUTING IS ALSO AVOIDED.
  148. 148. MULTIPROCESSOR SYSTEMS • IT IS A COMPUTER SYSTEM COMPRISING OF TWO OR MORE PROCESSORS. • AN INTERCONNECTION NETWORK LINKS THESE PROCESSORS. • THE MAIN OBJECTIVE IS TO ENHANCE THE PERFORMANCE BY MEANS OF PARALLEL PROCESSING. • IT FALLS UNDER THE MIMD ARCHITECTURE. • BESIDES HIGH PERFORMANCE, IT PROVIDES THE FOLLOWING BENEFITS: FAULT TOLERANCE & GRACEFUL DEGRADATION; SCALABILITY & MODULAR GROWTH
  149. 149. CLASSIFICATION OF MULTI-PROCESSORS • MULTI-PROCESSOR ARCHITECTURE: TIGHTLY COUPLED LOOSELY COUPLED UMA NUMA NORMA NO REMOTE MEMORY ACCESS IN A TIGHTLY COUPLED MULTI-PROCESSOR, MULTIPLE PROCS SHARE INFO VIA COMMON MEM. HENCE, ALSO KNOWN AS SHARED MEM MULTI- PROCESSOR SYSTEM. BESIDES GLOBAL MEM, EACH PROC CAN ALSO HAVE LOCAL MEM DEDICATED TO IT. DISTRIBUTED MEM MULTI- PROCESSOR SYSTEM
  150. 150. SYMMETRIC MULTIPROCESSOR • IN UMA SYSTEM, THE ACCESS TIME FOR MEM IS EQUAL FOR ALL THE PROCESSORS. • A SMP SYSTEM IS AN UMA SYSTEM WITH IDENTICAL PROCESSORS, EQUALLY CAPABLE IN PERFORMING SIMILAR FUNCTIONS IN AN IDENTICAL MANNER. • ALL THE PROCS. HAVE EQUAL ACCESS TIME FOR THE MEM AND I/O RESOURCES. • FOR THE OS, ALL SYSTEMS ARE SIMILAR AND ANY PROC. CAN EXECUTE IT. • THE TERMS UMA AND SMP ARE INTERCHANGABLY USED.
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