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Spatial Computation Thesis committee: Seth Goldstein Peter Lee Todd Mowry Babak Falsafi Nevin Heintze Ph.D. Thesis defense, December 8, 2003 SCS Mihai Budiu CMU CS

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2 Spatial Computation Thesis committee: Seth Goldstein Peter Lee Todd Mowry Babak Falsafi Nevin Heintze Ph.D. Thesis defense, December 8, 2003 SCS A model of general-purpose computation based on Application-Specific Hardware.

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3 Thesis Statement Application-Specific Hardware (ASH): can be synthesized by adapting software compilation for predicated architectures, provides high-performance for programs with high ILP, with very low power consumption, is a more scalable and efficient computation substrate than monolithic processors.

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4 Outline Introduction Compiling for ASH Media processing on ASH ASH vs. superscalar processors Conclusions

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5 CPU Problems Complexity Power Global Signals Limited ILP

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6 Design Complexity from Michael Flynns FCRC 2003 talk

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7 Communication vs. Computation 5ps20ps gate wire Power consumption on wires is also dominant

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8 Our Approach: ASH Application-Specific Hardware

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9 1. 2. 1. 2. Programs Resource Binding Time CPUASH

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10 Hardware Interface CPUASH ISA software hardware software hardware gates virtual ISA

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11 Application-Specific Hardware C program Compiler Dataflow IR Reconfigurable/custom hw

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12 Contributions Compilation Computer architecture Reconfigurable computing Embedded systems Asynchronous circuits High-level synthesis Dataflow machines Nanotechnology theory systems

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13 Outline Introduction CASH: Compiling for ASH Media processing on ASH ASH vs. superscalar processors Conclusions

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14 Computation = Dataflow Operations ) functional units Variables ) wires No interpretation x = a & 7;... y = x >> 2; Programs & a 7 >> 2 x Circuits

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15 Basic Operation + data valid ack latch

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16 + Asynchronous Computation data valid ack 1 + 2 + 3 + 4 + 8 + 7 + 6 + 5 latch

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17 Distributed Control Logic +- ack rdy FSM asynchronous control short, local wires

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18 Forward Branches if (x > 0) y = -x; else y = b*x; * xb0 y ! -> Conditionals ) Speculation critical path

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19 Control Flow ) Data Flow data predicate Merge (label) Gateway data Split (branch) p !

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20 i +1 < 100 0 * + sum 0 Loops int sum=0, i; for (i=0; i < 100; i++) sum += i*i; return sum; ! ret

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21 no speculation sequencing of side-effects Predication and Side-Effects Load addr data pred token to memory

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22 Thesis Statement Application-Specific Hardware: can be synthesized by adapting software compilation for predicated architectures, provides high-performance for programs with high ILP, with very low power consumption, is a more scalable and efficient computation substrate than monolithic processors.

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23 Outline Introduction CASH: Compiling for ASH –An optimization on the SIDE Media processing on ASH ASH vs. superscalar processors Conclusions skip to

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24 Availability Dataflow Analysis y y = a*b;... if (x) {...... = a*b; }

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25 Dataflow Analysis Is Conservative if (x) {... y = a*b; }...... = a*b; y?y?

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26 Static Instantiation, Dynamic Evaluation flag = false; if (x) {... y = a*b; flag = true; }...... = flag ? y : a*b;

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27 SIDE Register Promotion Impact Loads Stores % reduction

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28 Outline Introduction CASH: Compiling for ASH Media processing on ASH ASH vs. superscalar processors Conclusions

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29 Performance Evaluation ASH LSQ limited BW L1 8K L2 1/4M Mem CPU: 4-way OOO Assumption: all operations have the same latency.

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30 Media Kernels, vs 4-way OOO

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31 Media Kernels, IPC

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32 Speed-up IPC Correlation

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33 Low-Level Evaluation C CASH core Verilog back-end Synopsys, Cadence P/R Results shown so far. All results in thesis. Results in the next two slides. ASIC 180nm std. cell library, 2V ~1999 technology

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34 Area Reference: P4 in 180nm has 217mm 2

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35 Power vs 4-way OOO superscalar, 600 Mhz, with clock gating (Wattch), ~ 6W

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36 Thesis Statement Application-Specific Hardware: can be synthesized by adapting software compilation for predicated architectures, provides high-performance for programs with high ILP, with very low power consumption, is a more scalable and efficient computation substrate than monolithic processors.

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37 Outline Introduction CASH: Compiling for ASH Media processing on ASH –dataflow pipelining ASH vs. superscalar processors Conclusions skip to

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38 Pipelining i + <= 100 1 * + sum pipelined multiplier (8 stages) int sum=0, i; for (i=0; i < 100; i++) sum += i*i; return sum; cycle=1

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39 Pipelining i + <= 100 1 * + sum cycle=2

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40 Pipelining i + <= 100 1 * + sum cycle=3

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41 Pipelining i + <= 100 1 * + sum cycle=4

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42 Pipelining i + <= 100 1 i=1 i=0 + sum cycle=5 pipeline balancing

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43 Outline Introduction CASH: Compiling for ASH Media processing on ASH ASH vs. superscalar processors Conclusions

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44 This Is Obvious! ASH runs at full dataflow speed, so CPU cannot do any better (if compilers equally good).

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45 SpecInt95, ASH vs 4-way OOO

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46 Predicted not taken Effectively a noop for CPU! Predicted taken. Branch Prediction for (i=0; i < N; i++) {... if (exception) break; } i + < 1 & ! exception result available before inputs ASH crit path CPU crit path

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47 SpecInt95, perfect prediction

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48 ASH Problems Both branch and join not free Static dataflow (no re-issue of same instr)(no re-issue of same instr) Memory is far Fully static – No branch prediction – No dynamic unrolling – No register renaming Calls/returns not lenient...

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49 Thesis Statement Application-Specific Hardware: can be synthesized by adapting software compilation for predicated architectures, provides high-performance for programs with high ILP, with very low power consumption, is a more scalable and efficient computation substrate than monolithic processors.

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50 Outline Introduction +CASH: Compiling for ASH +Media processing on ASH +ASH vs. superscalar processors =Conclusions

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51 low power simple verification? specialized to app. unlimited ILP simple hardware no fixed window economies of scale highly optimized branch prediction control speculation full-dataflow global signals/decision Strengths

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52 Conclusions Compiling around the ISA is a fruitful research approach. Distributed computation structures require more synchronization overhead. Spatial Computation efficiently implements high-ILP computation with very low power.

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53 Backup Slides Control logic Pipeline balancing Lenient execution Dynamic Critical Path Memory PRE Critical path analysis CPU + ASH

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54 Control Logic C C Reg rdy in ack in rdy out ack out data in data out backback to talk

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55 Last-Arrival Events + data valid ack Event enabling the generation of a result May be an ack Critical path=collection of last-arrival edges

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56 Dynamic Critical Path 3. Some edges may repeat 2. Trace back along last-arrival edges 1. Start from last node backback to analysis

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57 Critical Paths if (x > 0) y = -x; else y = b*x; * xb0 y ! ->

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58 Lenient Operations if (x > 0) y = -x; else y = b*x; * xb0 y ! -> Solve the problem of unbalanced paths backback to talk

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59 Pipelining i + <= 100 1 * i=1 i=0 + sum cycle=6

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60 Pipelining i + <= 100 1 * + sum is loop sums loop Long latency pipe predicate cycle=7

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61 Predicate ack edge is on the critical path. Pipelining i + <= 100 1 * + sum critical path is loop sums loop

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62 Pipelinine balancing i + <= 100 1 * + sum is loop sums loop decoupling FIFO cycle=7

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63 Pipelinine balancing i + <= 100 1 * + sum is loop sums loop critical path decoupling FIFO backback to presentation

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64 Register Promotion …=*p (p2) *p=… (p1) …=*p *p=… (p1) (p2 Æ : p1) Load is executed only if store is not

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65 Register Promotion (2) …=*p (p2) *p=… (p1) …=*p (false) *p=… (p1) When p2 ) p1 the load becomes dead......i.e., when store dominates load in CFG back

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66 ¼ PRE...=*p (p1)...=*p (p2)...=*p (p1 Ç p2) This corresponds in the CFG to lifting the load to a basic block dominating the original loads

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67 Store-store (1) *p=... (p2) *p=… (p1) *p=... (p2) *p=… (p1 Æ : p2) When p1 ) p2 the first store becomes dead......i.e., when second store post-dominates first in CFG

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68 Store-store (2) *p=... (p2) *p=… (p1) *p=... (p2) *p=… (p1 Æ : p2) Token edge eliminated, but......transitive closure of tokens preserved back

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69 A Code Fragment for(i = 0; i < 64; i++) { for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break; Y[i] = X[j].q; } SpecINT95:124.m88ksim:init_processor, stylized

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70 Dynamic Critical Path for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break; load predicate loop predicate sizeof(X[j]) definition

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71 MIPS gcc Code LOOP: L1: beq $v0,$a1,EXIT ; X[j].r == i L2: addiu $v1,$v1,20 ; &X[j+1].r L3: lw $v0,0($v1) ; X[j+1].r L4: addiu $a0,$a0,1 ; j++ L5: bne $v0,$a3,LOOP ; X[j+1].r == 0xF EXIT: L1 ! L2 ! L3 ! L5 ! L1 4-instructions loop-carried dependence for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break;

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72 If Branch Prediction Correct L1 ! L2 ! L3 ! L5 ! L1 Superscalar is issue-limited! 2 cycles/iteration sustained for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break; LOOP: L1: beq $v0,$a1,EXIT ; X[j].r == i L2: addiu $v1,$v1,20 ; &X[j+1].r L3: lw $v0,0($v1) ; X[j+1].r L4: addiu $a0,$a0,1 ; j++ L5: bne $v0,$a3,LOOP ; X[j+1].r == 0xF EXIT:

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73 Critical Path with Prediction Loads are not speculative for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break;

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74 Prediction + Load Speculation ~4 cycles! Load not pipelined (self-anti-dependence) ack edge for (j = 0; X[j].r != 0xF; j++) if (X[j].r == i) break;

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75 OOO Pipe Snapshot IFDAEXWBCT L5 L1 L2 L1 L2 L3 L4 L1 L3 L5 L3 L2 L1 L3 register renaming LOOP: L1: beq $v0,$a1,EXIT ; X[j].r == i L2: addiu $v1,$v1,20 ; &X[j+1].r L3: lw $v0,0($v1) ; X[j+1].r L4: addiu $a0,$a0,1 ; j++ L5: bne $v0,$a3,LOOP ; X[j+1].r == 0xF EXIT:

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76 Unrolling? for(i = 0; i < 64; i++) { for (j = 0; X[j].r != 0xF; j+=2) { if (X[j].r == i) break; if (X[j+1].r == 0xF) break; if (X[j+1].r == i) break; } Y[i] = X[j].q; } when 1 iteration backback to talk

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77 Ideal Architecture High-ILP computation Low ILP computation + OS + VM CPUASH Memory back

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Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.

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