1. "DDMT", Amir Roth, HPCA-71 Speculative Data-Driven Multithreading (an implementation of pre-execution) Amir Roth and Gurindar S. Sohi HPCA-7 Jan. 22, 2001

2. "DDMT", Amir Roth, HPCA-72 Pre-Execution Goal: high single-thread performance Problem: μarchitectural latencies of “problem” instructions (PIs) Memory: cache misses, Pipeline: mispredicted branches Solution: decouple μarchitectural latencies from main thread Execute copies of PI computations in parallel with whole program Copies execute PIs faster than main thread  “pre-execute” Why? Fewer instructions Initiate Cache misses earlier Pre-computed branch outcomes, relay to main thread DDMT: an implementation of pre-execution

3. "DDMT", Amir Roth, HPCA-73 Pre-Execution is a Supplement Fundamentally: tolerating non-execution latencies (pipeline, memory) requires values faster than execution can provide them Ways of providing values faster than execution Old: Behavioral prediction: table-lookup + small effort per value, - less than perfect (“problems”) New: Pre-execution: executes fewer instructions + perfect accuracy, - more effort per value Solution: supplement behavioral prediction with pre-execution Key: behavioral prediction must handle majority of cases Good news: it already does

4. "DDMT", Amir Roth, HPCA-74 Data-Driven Multithreading (DDMT) DDMT: an implementation of pre-execution Data-Driven Thread (DDT): pre-executed computation of PI Implementation: extension to simultaneous multithreading (SMT) SMT is a reality (21464) Low static cost: minimal additional hardware Pre-execution siphons execution resources Low dynamic cost: fine-grain, flexible bandwidth partitioning Take only as much as you need Minimize contention, overhead Paper: Metrics, algorithms and mechanics Talk: Mostly mechanics

5. "DDMT", Amir Roth, HPCA-75 Talk Outline Working example in 3 parts Some details Numbers, numbers, numbers

6. "DDMT", Amir Roth, HPCA-76 Example.1 Identify PIs Use profiling to find PIs for (node=list; node; node = node->next) node->val -= node->neighbor->val * node->coeff; if (node->neighbor != NULL) STATIC CODE ldt f1, 16(r2)I5 ldt f0, 16(r1)I4 ldt f2, 24(r1)I6 ldt f1, 16(r2)I5 ldt f0, 16(r1)I4 ldq r1, 0(r1)I10 stt f0, 16(r1)I9 subt f0, f3, f0I8 mult f1, f2, f3I7 beq r2, I10I3 ldq r2, 8(r1)I2 beq r1, I12I1 br I1I11 beq r2, I10I3 ldq r2, 8(r1)I2 beq r1, I12I1 br I1I5 ldq r1, 0(r1)I3 INSTPC DYNAMIC INSN STREAM Running example: same as the paper Simplified loop from EM3D Few static PIs cause most dynamic “problems” Good coverage with few static DDTs

7. "DDMT", Amir Roth, HPCA-77 ldt f1, 16(r2)I5 ldt f0, 16(r1)I4 ldt f2, 24(r1)I6 ldt f1, 16(r2)I5 ldt f0, 16(r1)I4 ldq r1, 0(r1)I10 stt f0, 16(r1)I9 subt f0, f3, f0I8 mult f1, f2, f3I7 beq r2, I10I3 ldq r2, 8(r1)I2 beq r1, I12I1 br I1I11 beq r2, I10I3 ldq r2, 8(r1)I2 beq r1, I12I1 br I1I11 ldq r1, 0(r1)I10 INSTPC Example.2 Extract DDTs Use first instruction as DDT trigger Dynamic trigger instances signal DDT fork ldt f1, 16(r2)I5 beq r2, I10I3 ldq r2, 8(r1)I2ldq r1, 0(r1)I10ldq r1, 0(r1)I10 ldt f1, 16(r2)I5 beq r2, I10I3 ldq r2, 8(r1)I2 ldq r1, 0(r1)I10 INSTPC DDTC (static) I10: Examine program traces Start with PIs Work backwards, gather backward-slices Eventually stop. When? (see paper) Pack last N-1 slice instructions into DDT Load DDT into DDTC (DDT$)

8. "DDMT", Amir Roth, HPCA-78 Example.3 Pre-Execute DDTs Main thread (MT) ldt f0, 16(r1)I4 beq r1, I12I1 br I1I11 ldq r1, 0(r1)I10 ldq r2, 8(r1)I2beq r2, I10I3ldt f1, 16(r2)I5 stt f0, 16(r1)I9 subt f0, f3, f0I8 ldt f1, 16(r2)I5 mult f1, f2, f3I7beq r2, I10I3ldt f2, 24(r1)I6ldq r2, 8(r1)I2 INSTPC ldt f1, 16(r2)I5 ldq r1, 0(r1)I10 DDT ldt f0, 16(r1)I4 beq r2, I10I3 ldq r2, 8(r1)I2 beq r1, I12I1 br I1I11 ldq r1, 0(r1)I10 INSTPC … ldq r1, 0(r1)I10 INSTPC DDTC (static) I10: MT MT integrates DDT results Instr’s not re-executed  reduces contention Shortens MT critical path Pre-computed branch avoids mis-prediction MT, DDT execute in parallel Executed a trigger instr? Fork DDT (μarch) DDT initiates cache miss “Absorbs” latency

9. "DDMT", Amir Roth, HPCA-79 Details.1 More About DDTs Composed of instr’s from original program Required by integration Should look like normal instr’s to processor Data-driven: instructions are not “sequential” No explicit control-flow How are they sequenced? Pack into “traces” (in DDTC) Execute all instructions (branches too) Save results for integration No runaway threads, better overhead control “Contain” any control-flow (e.g. unrolled loops) ldt f1, 16(r2)I5 beq r2, I10I3 ldq r2, 8(r1)I2 ldq r1, 0(r1)I10 INSTPC I10: ldt f1, 16(r2)I5 beq r2, I10I3 ldq r2, 8(r1)I2 ldq r1, 0(r1)I10 INSTPC I10: ldq r1, 0(r1)I10 ldq r1, 0(r1)I10 ldq r1, 0(r1)I10

10. "DDMT", Amir Roth, HPCA-710 Details.2 More About Integration Centralized physical register file Use for DDT-MT communication beq r1, I12 br I1 ldq r1, 0(r1) ldq r2, 8(r1) ldq r1, 0(r1) stt f0, 16(r1) subt f0, f3, f0 mult f1, f2, f3 ldt f2, 24(r1) ldt f1, 16(r2) ldt f0, 16(r1) beq r2, I10 ldq r2, 8(r1) beq r1, I12 br I1 ldq r1, 0(r1) ldq r2, 8(r1) ldq r1, 0(r1) r1 ldq r1, 0(r1) ldq r2, 8(r1) More on integration: implementation of squash-reuse [MICRO-33] Fork: copy MT rename map to DDT DDT locates MT values via lookups “Roots” integration Integration: match PC/physical registers to establish DDT-MT instruction correspondence MT “claims” physical registers allocated by DDT Modify register-renaming to do this

11. "DDMT", Amir Roth, HPCA-711 Details.3 More About DDT Selection Very important problem Fundamental aspects Metrics, algorithms Promising start See paper Practical aspects Who implements algorithm? How do DDTs get into DDTC? Paper: profile-driven, offline, executable annotations Open question

12. "DDMT", Amir Roth, HPCA-712 Performance Evaluation SPEC2K, Olden, Alpha EV6, –O3 –fast Chose programs with problems SimpleScalar-based simulation environment DDT selection phase: functional simulation on small input DDT measurement phase: timing simulation on larger input 8-wide, superscalar, out-of-order core 128 ROB, 64 LDQ, 32 STQ, 80 RS (shared) Pipe: 3 fetch, 2 rename/integrate, 2 schedule, 2 reg read, 2 load 32KB I$/64KB D$ (2-way), 1MB L2$ (4-way), mem b/w: 8 b/cyc. DDTC: 16 DDTs, 32 instructions (max) per DDT

13. "DDMT", Amir Roth, HPCA-713 Numbers.1 The Bottom Line Cache misses Speedups vary, 10-15% DDT “unrolling”: increases latency tolerance (paper) Branch mispredictions Speedups lower, 5-10% More PIs, lower coverage Branch integration != perfect branch prediction Effects mix

14. "DDMT", Amir Roth, HPCA-714 Numbers.2 A Closer Look DDT overhead: fetch utilization ~5% (reasonable) Fewer MT fetches (always) Contention Fewer total fetches Early branch resolution DDT utility: integration rates Vary, mostly ~30% (low) Completed: well done Not completed: a little late Input-set differences Greedy DDTs, no early exits

15. "DDMT", Amir Roth, HPCA-715 Numbers.3 Is Full DDMT necessary? How important is integration? Important for branch resolution, less so for prefetching How important is decoupling? What if we just priority-scheduled? Extremely. No data-driven sequencing  no speedup

16. "DDMT", Amir Roth, HPCA-716 Summary Pre-execution: supplements behavioral prediction Decouple/absorb μarchitectural latencies of “problem” instructions DDMT: an implementation of pre-execution An extension to SMT Few hardware changes Bandwidth allocation flexibility reduces overhead Future: DDT selection Fundamental: better metrics/algorithms to increase utility/coverage Practical: an easy implementation Coming to a university/research-lab near you