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Fifth Lecture: Chapter 3: Dataflow Processors - Hybrids

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Chapter 3: Dataflow Processors - Hybrids Please recall: Dataflow model: the execution is driven only by the availability of operands! Static & dynamic dataflow – PowerPoint PPT presentation

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Title: Fifth Lecture: Chapter 3: Dataflow Processors - Hybrids


1
Fifth Lecture Chapter 3 Dataflow Processors -
Hybrids
  • Please recall
  • Dataflow model the execution is driven only by
    the availability of operands!
  • Static dynamic dataflow
  • Dynamic dataflow pipeline
  • Token buffer ? Token Matching, Instruction Fetch,
    Execute New Tag, Form New Tokens (max. of 2) ?
    Token buffer or network
  • A successor instruction can only be executed when
    the previous instruction finished (token
    recycling!) ? no data or control hazards in
    pipeline
  • Hazards in dataflow model
  • Data hazards due to
  • true dependences ? dataflow principle
  • name (false) dependences ? not present due to
    single assignment rule in dataflow languages
  • Control hazards ? transformed into data
    dependences
  • Structural hazards mostly ignored in dataflow
    literature
  • Explicit token store removes associative matching
    need

2
Augmenting Dataflow with Control-Flow
  • poor sequential code performance by dynamic
    dataflow computers
  • Why?
  • an instruction of the same thread is issued to
    the dataflow pipeline after the completion of its
    predecessor instruction.
  • In the case of an 8-stage pipeline, instructions
    of the same thread can be issued at most every
    eight cycles.
  • Low workload the utilization of the dataflow
    processor drops to one eighth of its maximum
    performance.
  • Another drawback the overhead associated with
    token matching.
  • before a dyadic instruction is issued to the
    execution stage, two result tokens have to be
    present.
  • The first token is stored in the waiting-matching
    store, thereby introducing a bubble in the
    execution stage(s) of the dataflow processor
    pipeline.
  • measured pipeline bubbles on Monsoon up to 28.75
  • no use of registers possible!

3
Augmenting Dataflow with Control-Flow
  • Solution combine dataflow with control-flow
    mechanisms
  • threaded dataflow,
  • large-grain dataflow,
  • dataflow with complex machine operations,
  • further hybrids

4
Threaded Dataflow
  • Threaded dataflow the dataflow principle is
    modified so that instructions of certain
    instruction streams are processed in succeeding
    machine cycles.
  • A subgraph that exhibits a low degree of
    parallelism is transformed into a sequential
    thread.
  • The thread of instructions is issued
    consecutively by the matching unit without
    matching further tokens except for the first
    instruction of the thread.
  • Threaded dataflow covers
  • the repeat-on-input technique used in Epsilon-1
    and Epsilon-2 processors,
  • the strongly connected arc model of EM-4, and
  • the direct recycling of tokens in Monsoon.

5
Threaded Dataflow (continued)
  • Data passed between instructions of the same
    thread is stored in registers instead of written
    back to memory.
  • Registers may be referenced by any succeeding
    instruction in the thread.
  • Single-thread performance is improved.
  • The total number of tokens needed to schedule
    program instructions is reduced which in turn
    saves hardware resources.
  • Pipeline bubbles are avoided for dyadic
    instructions within a thread.
  • Two threaded dataflow execution techniques can be
    distinguished
  • direct token recycling (Monsoon),
  • consecutive execution of the instructions of a
    single thread (Epsilon EM).

6
Direct token recycling of Monsoon
  • Cycle-by-cycle instruction interleaving of
    threads similar to multithreaded von Neumann
    computers!
  • 8 register sets can be used by 8 different
    threads.
  • Dyadic instructions within a thread (except for
    the start instruction!) refer to at least one
    register, ? need only a single token to be
    enabled.
  • A result token of a particular thread is recycled
    ASAP in the 8-stage pipeline, i.e. every 8th
    cycle the next instruction of a thread is fired
    and executed.
  • This implies that at least 8 threads must be
    active for a full pipeline utilization.
  • Threads and fine-grain dataflow instructions can
    be mixed in the pipeline.

7
Epsilon and EM-4
  • Instructions of a thread are executed
    consecutively.
  • The circular pipeline of fine-grain dataflow is
    retained.
  • The matching unit is enhanced with a mechanism
    that, after firing the first instruction of a
    thread, delays matching of further tokens in
    favor of consecutive issuing of all instructions
    of the started thread.
  • Problem implementation of an efficient
    synchronization mechanism

8
Large-Grain (coarse-grain) Dataflow
  • A dataflow graph is enhanced to contain
    fine-grain (pure) dataflow nodes and macro
    dataflow nodes.
  • A macro dataflow node contains a sequential block
    of instructions.
  • A macro dataflow node is activated in the
    dataflow manner, its instruction sequence is
    executed in the von Neumann style!
  • Off-the-shelf microprocessors can be used to
    support the execution stage.
  • Large-grain dataflow machines typically decouple
    the matching stage (sometimes called signal
    stage, synchronization stage, etc.) from the
    execution stage by use of FIFO-buffers.
  • Pipeline bubbles are avoided by the decoupling
    and FIFO-buffering.

9
Dataflow with Complex Machine Operations
  • Use of complex machine instructions, e.g. vector
    instructions
  • ability to exploit parallelism at the
    subinstruction level
  • Instructions can be implemented by pipeline
    techniques as in vector computers.
  • The use of a complex machine operation may spare
    several nested loops.
  • Structured data is referenced in block rather
    than element-wise and can be supplied in a burst
    mode.

10
Dataflow with Complex Machine Operations and
combined with LGDF
  • Often use of FIFO-buffers to decouple the firing
    stage and the execution stage
  • bridges different execution times within a mixed
    stream of simple and complex instructions.
  • Major difference to pure dataflow tokens do not
    carry data (except for the values true or false).
  • Data is only moved and transformed within the
    execution stage.
  • Applied in Decoupled Graph/Computation
    Architecture, the Stollmann Dataflow Machine, and
    the ASTOR architecture.
  • These architectures combine complex machine
    instructions with large-grain dataflow.

11
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12
Lessons Learned from Dataflow
  • Superscalar microprocessors display an
    out-of-order dynamic execution that is referred
    to as local dataflow or micro dataflow.
  • Colwell and Steck 1995, in the first paper on the
    PentiumPro The flow of the Intel Architecture
    instructions is predicted and these instructions
    are decoded into micro-operations (?ops), or
    series of ?ops, and these ?ops are
    register-renamed, placed into an out-of-order
    speculative pool of pending operations, executed
    in dataflow order (when operands are ready), and
    retired to permanent machine state in source
    program order.
  • State-of-the-art microprocessors typically
    provide 32 (MIPS R10000), 40 (Intel PentiumPro)
    or 56 (HP PA-8000) instruction slots in the
    instruction window or reorder buffer.
  • Each instruction is ready to be executed as soon
    as all operands are available.

13
Dataflow and Superscalar
  • Pros and Cons
  • next

14
Comparing dataflow computers with superscalar
microprocessors
  • Superscalar microprocessors are von Neumann
    based (sequential) thread of instructions as
    input ? not enough fine-grained parallelism to
    feed the multiple functional units ? speculation
  • dataflow approach resolves any threads of control
    into separate instructions that are ready to
    execute as soon as all required operands become
    available.
  • The fine-grained parallelism generated by
    dataflow principle is far larger than the
    parallelism available for microprocessors.
  • However, locality is lost ? no caching, no
    registers

15
Lessons Learned from Dataflow (Pipeline Issues)
  • Microprocessors Data and control dependences
    potentially cause pipeline hazards that are
    handled by complex forwarding logic.
  • Dataflow Due to the continuous context switches,
    pipeline hazards are avoided disadvantage poor
    single thread performance.
  • Microprocessors Antidependences and output
    dependences are removed by register renaming that
    maps the architectural registers to the physical
    registers.
  • Thereby the microprocessor internally generates
    an instruction stream that satisfies the single
    assignment rule of dataflow.
  • The main difference between the dependence graphs
    of dataflow and the code sequence in an
    instruction window of a microprocessor branch
    prediction and speculative execution.
  • Microprocessors rerolling execution in case of a
    wrongly predicted path is costly in terms of
    processor cycles.

16
Lessons Learned from Dataflow (Continued)
  • Dataflow The idea of branch prediction and
    speculative execution has never been evaluated in
    the dataflow environment.
  • Dataflow was considered to produce an abundance
    of parallelism while speculation leads to
    speculative parallelism which is inferior to real
    parallelism.
  • Microprocessors Due to the single thread of
    control, a high degree of data and instruction
    locality is present in the machine code.
  • Microprocessors The locality allows to employ a
    storage hierarchy that stores the instructions
    and data potentially executed in the next cycles
    close to the executing processor.
  • Dataflow Due to the lack of locality in a
    dataflow graph, a storage hierarchy is difficult
    to apply.

17
Lessons Learned from Dataflow (Continued)
  • Microprocessors The operand matching of
    executable instructions in the instruction window
    is restricted to a part of the instruction
    sequence.
  • Because of the serial program order, the
    instructions in this window are likely to become
    executable soon. ? The matching hardware can be
    restricted to a small number of slots.
  • Dataflow the number of tokens waiting for a
    match can be very high. ? A large
    waiting-matching store is required.
  • Dataflow Due to the lack of locality, the
    likelihood of the arrival of a matching token is
    difficult to estimate, ? caching of tokens to
    be matched soon is difficult.

18
Lessons Learned from Dataflow (Memory Latency)
  • Microprocessors An unsolved problem is the
    memory latency caused by cache misses.
  • Example SGI Origin 2000
  • latencies are 11 processor cycles for a L1 cache
    miss,
  • 60 cycles for a L2 cache miss,
  • and can be up to 180 cycles for a remote memory
    access.
  • In principle, latencies should be multiplied by
    the degree of superscalar.
  • Microprocessors Only a small part of the memory
    latency can be hidden by out-of-order execution,
    write buffer, cache preload hardware, lockup free
    caches, and a pipelined system bus.
  • Microprocessors often idle and are unable to
    exploit the high degree of internal parallelism
    provided by a wide superscalar approach.
  • Dataflow The rapid context switching avoids
    idling by switching execution to another context.

19
Lessons Learned from Dataflow (Continued)
  • Microprocessors Finding enough fine-grain
    parallelism to fully exploit the processor will
    be the main problem for future superscalars.
  • Solution enlarge the instruction window to
    several hundred instruction slots two draw-backs
  • Most of the instructions in the window will be
    speculatively assigned with a very deep
    speculation level (today's depth is normally four
    at maximum). ? most of the instruction
    execution will be speculative. The principal
    problem here arises from the single instruction
    stream that feeds the instruction window.
  • If the instruction window is enlarged, the
    updating of the instruction states in the slots
    and matching of executable instructions lead to
    more complex hardware logic in the issue stage of
    the pipeline thus limiting the cycle rate.

20
Lessons Learned from Dataflow (Continued)
  • Solutions
  • the decoupling of the instruction window with
    respect to different instruction classes,
  • the partitioning of the issue stage into several
    pipeline stages,
  • and alternative instruction window organizations.
  • Alternative instruction window organization the
    dependence-based microprocessor
  • Instruction window is organized as multiple
    FIFOs.
  • Only the instructions at the heads of a number of
    FIFO buffers can be issued to the execution units
    in the next cycle.
  • The total parallelism in the instruction window
    is restricted in favor of a less costly issue
    that does not slow down processor cycle rate.
  • Thereby the potential fine-grained parallelism is
    limited ? somewhat similar to the threaded
    dataflow approach.

21
Lessons Learned from Dataflow (alternative
instruction window organizations)
  • Look at dataflow matching store implementations
  • Look into dataflow solutions like threaded
    dataflow (e.g. repeat-on-input technique or
    strongly-connected arcs model)
  • Repeat-on-input strategy issues
    compiler-generated code sequences serially (in an
    otherwise fine-grained dataflow computer). ?
    Transferred to the local dataflow in an
    instruction window
  • an issue string might be used
  • a serie of data dependent instructions is
    generated by a compiler and issued serially after
    the issue of the leading instruction.
  • However, the high number of speculative
    instructions in the instruction window remains.

22
CSIDC 2001
  • The second annual Computer Society International
    Design Competition is now accepting applications
    from student teams.
  • Mobiles Gerät mit Bluetooth-Technologie
  • Vortreffen geänderter Termin Do 23.11. 1600
    Geb. 20.20 Raum 267
  • http//computer.org/ (xxx)

23
Chapter 3 CISC Processors
  • A brief look at CISC Processors
  • Out-of-order execution
  • Scoreboarding technique
  • CDC 6600 (with scoreboarding)
  • Alternate technique Tomasulo scheduling

24
Prerequisites for CISC Processors
  • Technology in the 1960s and early 1970s was
    dominated by high hardware cost, in particular by
    high cost for memory.
  • Only a small main memory and slow memory access!
  • Instruction fetch was done from main memory and
    could be overlapped with decode and execution of
    previous instructions.
  • Observation the number of cycles per instruction
    was determined by the number of cycles taken to
    fetch the instruction.
  • CISC (complex instruction set computer) approach
  • it is acceptable to increase the average number
    of cycles taken to decode and execute an
    instruction.
  • reduce the number of instructions and
  • encode these instructions densely.
  • Multiple-cycle instructions reduce the overall
    number of instructions, and thus reduce the
    overall execution time because they reduce the
    instruction-fetch time.

25
A Brief Look at CISC Processors
  • All mainframes in the 1960s and 1970s were CISCs,
    namely e.g. CDC 6600, and the IBM 360/370
    family
  • CISC microprocessor lines
  • Intel x86 family line from Intel 8088 to Pentium
    III
  • Motorola from 6800 to 68060
  • Zilog from Z80 to Z80000 (terminated)
  • National Semiconductor NS320xx (terminated)

26
Scheduling
  • Scheduling a process which determines when to
    start a particular instruction, when to read its
    operands, and when to write its result,
  • Target of scheduling rearrange instructions to
    reduce stalls when data or control dependences
    are present
  • Static scheduling the compiler does it
  • Dynamic scheduling the hardware does it
  • Key idea Allow instructions behind stall to
    proceed
  • DIVD F0,F2,F4
  • ADDD F10,F0,F8
  • SUBD F12,F8,F14
  • SUBD is not data dependent on anything in the
    pipeline
  • Enables out-of-order execution ? out-of-order
    completion
  • ID stage checks for structural and data
    dependencies

27
Dynamic Scheduling
  • Dynamic scheduling works also when stalls arise
    that are unknown at compile-time, e.g. cache
    misses
  • Dynamic scheduling can be either
  • Control flow scheduling, when performed
    centrally at the time of decode e.g.
    scoreboarding in CDC 6600
  • Dataflow scheduling, if performed in a
    distributed manner by the FUs themselves at
    execute time. Instructions are decoded and
    issued to reservation stations awaiting their
    operands. Tomasulo scheme in the IBM System/360
    Model 91 processor

28
Scoreboarding
  • Introduced in 1963 by Thornton in the CDC6600
    processor.
  • Goal of scoreboarding is to maintain an execution
    rate of one instruction per clock cycle by
    executing an instruction as early as possible.
  • Instructions execute out-of-order when there are
    sufficient resources and no data dependences.
  • A scoreboard is a hardware unit that keeps track
    of
  • the instructions that are in the process of
    being executed,
  • the functional units that are doing the
    executing,
  • and the registers that will hold the results of
    those units.
  • A scoreboard centrally performs all hazard
    detection and resolution and thus controls the
    instruction progression from one step to the next.

29
Scoreboard Pipeline
30
Scoreboarding
  • The ID stage of the standard pipeline is split
    into two stages,
  • the issue (IS) stage decode instructions, check
    for structural and WAW hazards and
  • the read operands (RO) stage wait until no data
    hazards, then read operands from registers.
  • EX and WB stages are augmented with additional
    bookkeeping tasks.

31
Scoreboarding (in more detail)
  • IS stage if there is no structural hazard and no
    WAW hazard,
  • the scoreboard issues the instruction to the FU
    and updates its internal data structure
  • otherwise, the instruction issue stalls, and no
    further instruction is issued until the hazard is
    cleared (gt single issue and in-order issue!).
  • RO stage the scoreboard monitors the
    availability of the input operands, and when they
    are all available, tells the FU to read them from
    the register file (gt no forwarding!!) and to
    proceed to EX stage. RAW hazards are dynamically
    resolved (gt instructions may be dispatched
    into EX stage out of order)
  • EX stage the FU begins execution (which may take
    multiple cycles) and notifies the scoreboard when
    the result is ready (result ready flag set!).
  • WB stage once the scoreboard is aware that the
    FU has completed execution, the scoreboard checks
    for WAR hazards and stalls the completing
    instruction, if necessary. Otherwise, the
    scoreboard tells the FU to write its result to
    the destination register.

32
Scoreboard Implementation
  • Register result status table (R) indicates which
    FU will produce a result in each register (if
    any). The number of entries in R is equal to the
    number m of registers.
  • Functional unit status table (F) indicates the
    phase of execution each instruction is in. Phase
    flags Busy, RO, EX, and WB for each FU.
  • Instruction status table (also F) one entry per
    FU, telling
  • what operation the FU is scheduled to do
    (opcode),
  • where its result goes (destination register),
  • where its operands come from (source registers),
  • and if those results are available (validity of
    sources).
  • If an operand is not available, the table tells
    which FU will produce it (FU that produces a
    source value).
  • On power up, the scoreboard is initialized by
    setting all its entries to zero.

33
Scoreboarding Example
34
Scoreboarding Example
35
Scoreboarding Example
36
Scoreboarding Example
37
Scoreboarding Example
38
Scoreboarding Example
39
Scoreboarding Example
40
Scoreboarding Example
41
Scoreboarding Example
42
Scoreboarding Example
43
Scoreboarding Example
44
Scoreboarding Example
45
Scoreboarding Example
46
Scoreboarding Example
47
CDC 6600 Processor
  • delivered in 1964 by Control Data Corporation
  • pipelining
  • register-register instruction set (load/store
    architecture)
  • 3-address instruction format gt several
    characteristics of a RISC processor
  • first processor to make extensive use of multiple
    functional units
  • 10 FUs able to operate simultaneously on 24
    registers
  • 4 FUs for 60-bit floating-point operations among
    eight 60-bit operand registers,
  • 6 FUs for logic, indexing, and program control on
    the eight 18-bit address registers and eight
    18-bit increment/index registers.
  • scoreboarding scheme!

48
CDC 6600 Processor
49
Scoreboard Summary
  • Main advantage
  • managing multiple FUs
  • out-of-order execution of multicycle operations
  • maintaining all data dependences (RAW, WAW, WAR)
  • Scoreboard limitations
  • single issue scheme, however scheme is
    extendable to multiple-issue
  • in-order issue
  • no renaming ? antidependences and output
    dependences may lead to WAR and WAW stalls,
  • no forwarding hardware ? all results go through
    the registers
  • General limitations (not only valid for
    scoreboarding)
  • number and types of FUs since contention for FUs
    leads to structural hazards
  • the amount of parallelism available in code
    (dependences lead to stalls)
  • Tomasulo scheme removes some of the scoreboard
    limitations by forwarding and renaming hardware,
    but is still single and in-order issue

50
Next Lecture
  • Tomasulo in detail
  • Tomasulo animation
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