1 Lecture 5: Pipeline Wrap-up, Static ILP • Topics: multi-cycle ops, precise interrupts, compiler scheduling, loop unrolling, software pipelining (Sections C.5, 3.2) • Please hand in Assignment 1 now2 Multicycle Instructions Functional unit Latency Initiation interval Integer ALU 1 1 Data memory 2 1 FP add 4 1 FP multiply 7 1 FP divide 25 253 Effects of Multicycle Instructions • Structural hazards if the unit is not fully pipelined (divider) • Frequent RAW hazard stalls • Potentially multiple writes to the register file in a cycle • WAW hazards because of out-of-order instr completion • Imprecise exceptions because of o-o-o instr completion Note: Can also increase the “width” of the processor: handle multiple instructions at the same time: for example, fetch two instructions, read registers for both, execute both, etc.4 Precise Exceptions • On an exception: must save PC of instruction where program must resume all instructions after that PC that might be in the pipeline must be converted to NOPs (other instructions continue to execute and may raise exceptions of their own) temporary program state not in memory (in other words, registers) has to be stored in memory potential problems if a later instruction has already modified memory or registers • A processor that fulfils all the above conditions is said to provide precise exceptions (useful for debugging and of course, correctness)5 Dealing with these Effects • Multiple writes to the register file: increase the number of ports, stall one of the writers during ID, stall one of the writers during WB (the stall will propagate) • WAW hazards: detect the hazard during ID and stall the later instruction • Imprecise exceptions: buffer the results if they complete early or save more pipeline state so that you can return to exactly the same state that you left at6 ILP • Instruction-level parallelism: overlap among instructions: pipelining or multiple instruction execution • What determines the degree of ILP? dependences: property of the program hazards: property of the pipeline7 Static vs Dynamic Scheduling • Arguments against dynamic scheduling: requires complex structures to identify independent instructions (scoreboards, issue queue) high power consumption low clock speed high design and verification effort the compiler can “easily” compute instruction latencies and dependences – complex software is always preferred to complex hardware (?)8 Loop Scheduling • Revert back to the 5-stage in-order pipeline • The compiler’s job is to minimize stalls • Focus on loops: account for most cycles, relatively easy to analyze and optimize • Recall: a load has a two-cycle latency (1 stall cycle for the consumer that immediately follows), FP ALU feeding another 3 stall cycles, FP ALU feeding a store 2 stall cycles, int ALU feeding a branch 1 stall cycle, one delay slot after a branch9 Loop Example for (i=1000; i>0; i--) x[i] = x[i] + s; Loop: L.D F0, 0(R1) ; F0 = array element ADD.D F4, F0, F2 ; add scalar S.D F4, 0(R1) ; store result DADDUI R1, R1,# -8 ; decrement address pointer BNE R1, R2, Loop ; branch if R1 != R2 NOP Source code Assembly code10 Loop Example for (i=1000; i>0; i--) x[i] = x[i] + s; Loop: L.D F0, 0(R1) ; F0 = array element ADD.D F4, F0, F2 ; add scalar S.D F4, 0(R1) ; store result DADDUI R1, R1,# -8 ; decrement address pointer BNE R1, R2, Loop ; branch if R1 != R2 NOP Source code Assembly code Loop: L.D F0, 0(R1) ; F0 = array element stall ADD.D F4, F0, F2 ; add scalar stall stall S.D F4, 0(R1) ; store result DADDUI R1, R1,# -8 ; decrement address pointer stall BNE R1, R2, Loop ; branch if R1 != R2 stall 10-cycle schedule11 Smart Schedule • By re-ordering instructions, it takes 6 cycles per iteration instead of 10 • We were able to violate an anti-dependence easily because an immediate was involved • Loop overhead (instrs that do book-keeping for the loop): 2 Actual work (the ld, add.d, and s.d): 3 instrs Can we somehow get execution time to be 3 cycles per iteration? Loop: L.D F0, 0(R1) stall ADD.D F4, F0, F2 stall stall S.D F4, 0(R1) DADDUI R1, R1,# -8 stall BNE R1, R2, Loop stall Loop: L.D F0, 0(R1) DADDUI R1, R1,# -8 ADD.D F4, F0, F2 stall BNE R1, R2, Loop S.D F4, 8(R1)12 Loop Unrolling Loop: L.D F0, 0(R1) ADD.D F4, F0, F2 S.D F4, 0(R1) L.D F6, -8(R1) ADD.D F8, F6, F2 S.D F8, -8(R1) L.D F10,-16(R1) ADD.D F12, F10, F2 S.D F12, -16(R1) L.D F14, -24(R1) ADD.D F16, F14, F2 S.D F16, -24(R1) DADDUI R1, R1, #-32 BNE R1,R2, Loop • Loop overhead: 2 instrs; Work: 12 instrs • How long will the above schedule take to complete?13 Scheduled and Unrolled Loop Loop: L.D F0, 0(R1) L.D F6, -8(R1) L.D F10,-16(R1) L.D F14, -24(R1) ADD.D F4, F0, F2 ADD.D F8, F6, F2 ADD.D F12, F10, F2 ADD.D F16, F14, F2 S.D F4, 0(R1) S.D F8, -8(R1) DADDUI R1, R1, # -32 S.D F12, 16(R1) BNE R1,R2, Loop S.D F16, 8(R1) • Execution time: 14 cycles or 3.5 cycles per original iteration14 Loop Unrolling • Increases program size • Requires more registers • To unroll an n-iteration loop by degree k, we will need (n/k) iterations of the larger loop, followed by (n mod k) iterations of the original loop15 Automating Loop Unrolling • Determine the dependences across iterations: in the example, we knew that loads and stores in different iterations did not conflict and could be re-ordered • Determine if unrolling will help – possible only if iterations are independent • Determine address offsets for different loads/stores • Dependency analysis to schedule code without introducing hazards; eliminate name dependences by using additional registers16 Superscalar
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