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Dependence-Based Value Prediction

Dependence-Based Value Prediction. Yiannakis Sazeides University of Cyprus yanos@ucy.ac.cy UPC-Barcelona 17/5/2001. Motivation. Improve Performance (reduce complexity, save power...) What limits performance? Dependences Break dependences: Prediction

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Dependence-Based Value Prediction

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  1. Dependence-Based Value Prediction Yiannakis Sazeides University of Cyprus yanos@ucy.ac.cy UPC-Barcelona 17/5/2001

  2. Motivation • Improve Performance (reduce complexity, save power...) • What limits performance? Dependences • Break dependences: Prediction • Exploits regularity and/or non-uniformity in predicted information UPC 17/5/01

  3. Predicted Information • Architectural • dependences: control, address, value, memory • Non-Architectural • structural constrains: cache misses, bank conflicts • reduce hardware complexity: cache sets, shared patterns UPC 17/5/01

  4. Use of Predicted Information • Speculative/Non-Speculative • Depends on whether predicted information can directly/indirectly cause an incorrect update of architected state • non-speculative: branch prediction for instruction prefetching • speculative: branch prediction for instruction execution • Hardware/Software UPC 17/5/01

  5. Trends • Identify and eliminate predictability at computational levels below programming • Predictors in almost all high performance processors and many compilers • Technological evolution and limitations will increase latency and make predictive techniques more important • deeper pipelines • fast processor/slow memory • distributed microarchitectures UPC 17/5/01

  6. Our motivation • Can value prediction help? • How to use value predictability? • Wholistic approach: all types not just vp and coarser grain • Absolutely curious to discover, understand and use the esoteric program properties causing the predictability and non-uniformity observed in program information UPC 17/5/01

  7. This talk... • Hypothesis: dependence information influences the predictability of program values • Propose and evaluate various dependence-based value predictors • Approach: theoretical and practical • Work in progress... UPC 17/5/01

  8. Outline • Introduction • Background on value prediction • Dependence Info and Predictability • Dependence-based Value Predictors • Results • Future work UPC 17/5/01

  9. Value Sequences Produced by Instructions • Basic Sequences • Constant: 0 0 0 0 0 • Stride: 4 3 2 1 0 • Non-Stride: -1 23 10 94 • Repeating Sequences (composition of basic sequences): • Repeated Stride: 4 3 2 1 0 4 3 2 1 0 • Repeated Non-Stride: -1 23 10 94 -1 23 10 94 UPC 17/5/01

  10. Local History Value Predictors • Computational-Based • compute next value by performing a computation on previous value(s) • Context-Based • learn the value that follows a finite number of previous values (context) and predict that value when context repeats • need to observe a context-value before predicting correctly • Hybrid: Delta-Predictor UPC 17/5/01

  11. Last Value Predictor UPC 17/5/01

  12. Stride Predictor UPC 17/5/01

  13. Context-Based Predictor UPC 17/5/01

  14. Hybrid Predictor UPC 17/5/01

  15. Causes of Predictability • Model Based on Dependence Graph • Robustness: predictability due to program control structure and immediate values not input data • Predictability generation-propagation termination • need to consider info beyond local scope • dependence information holds potential to increase accuracy of value prediction UPC 17/5/01

  16. Hypothesis/Fact • The predictability of an instruction is determined by the information on the dependence path that leads to it • Predict the value produced by an instruction based on the values and/or information of its predecessors UPC 17/5/01

  17. Outline • Introduction • Background on value prediction • Dependence Info and Predictability • Dependence-based Value Predictors • Results • Future work UPC 17/5/01

  18. Example

  19. Observations • All instructions can trace dependence back to $6 • $6 used as an induction variable • Use information about $6 to get predictions UPC 17/5/01

  20. Example • Use value in $6 to predict instruction 7: Value of $6 Output of Instr. 7 0,..,13, 15, 31..63 1 14, 16..30 0 • Local history with <47 previous values mispredicts UPC 17/5/01

  21. Dependence-Based Prediction • Prediction based on information from the component of the predicted instruction i Output = PF(Componenti) • Component: • backward dynamic data dependence slice • node info: pcs, optypes, immed., outcomes • livein values (values not produced by component, register and memory) UPC 17/5/01

  22. Dependence-Based Prediction • In general, use information from components of previous instructions Output = PF(Ci-n..Ci-1,Ci) UPC 17/5/01

  23. Perspective • Virtually all predictors are functions of componentapproximations to values • “Practical Limitation”: predictors can rely on a subset of components and information UPC 17/5/01

  24. Component Approximation • Approximation accuracy depends on information used from component • What limits approximation accuracy: • finite resources and aliasing • amount of information to be stored • how soon a prediction can be made UPC 17/5/01

  25. Existing Predictors • “Interesting” with how little information predictors work well • Global History Branch Predictors • use information from multiple previous branch components Output=PF(Ci-n..Ci-1,Ci) • Note components may be unrelated (longer learning and destructive aliasing) UPC 17/5/01

  26. Dependence Information • A lot of information to choose from • Which predecessors: • recent, recurrent, earliest, all • Information • values • register, memory names • pc, optypes, immediates • dependence distance • Propagation through registers or memory UPC 17/5/01

  27. Ideas… predict based on: • Values of recurrent predecessors • values indicating location in program UPC 17/5/01

  28. Based on Recurrent Predecessors

  29. Ideas… predict based on: • Values of recurrent predecessors • values indicating location in program • no sp • sp with last SP value and PC selection of stride UPC 17/5/01

  30. Dependence-Based Stack Pointer Predictor UPC 17/5/01

  31. Ideas… predict based on: • Values of recurrent predecessors • values indicating location in program • no sp • sp with last SP value and PC selection of stride • Distance from predecessors • distance indicates coordinates in program • predict values not based on values • no sp UPC 17/5/01

  32. Based on Dependence Distance

  33. Ideas… predict based on: • Values of recurrent predecessors • values indicating location in program • no sp • sp with last SP value and PC selection of stride • Distance from predecessors • distance indicates coordinates in program • predict values not based on values • no sp • Most recently known processor state • isolate non-determinism UPC 17/5/01

  34. Based on Recent Predecessors

  35. Outline • Introduction • Background on value prediction • Dependence Info and Predictability • Dependence-based Value Predictors • Results • Future work UPC 17/5/01

  36. Prediction Process • To predict an instruction • Construction of Dependence Record (DR) • approximation of the information on the component of the instruction • Use DR to obtain a prediction (directly or indirectly) UPC 17/5/01

  37. DBVP Predictor with CT UPC 17/5/01

  38. DBVP Register (DBVP-R) • Component subset: instructions those fetched but have not updated yet the architectural state • isolates the non-determinism in execution • Information used: • Generate Registers(GR):livein registers (most recently known architected state) • Dependence Path Id(DPI):pcs, optypes, immediates (info about unknown state) UPC 17/5/01

  39. Construction of DR for an instruction (GR) UPC 17/5/01

  40. Construction of DR for an instruction - Many instructions same GRs - Differentiate with DPI UPC 17/5/01

  41. DR Construction • Information encoded in instructions • May be done incrementally off-line and stored in a table where it can be retrieved (imprecise?) • No investigation of implementation specifics of DR construction • Memory instructions propagate the dependence info through address operands (can be problematic - later) UPC 17/5/01

  42. DBVP-R Predictor History Table indexed with register names contains architected state UPC 17/5/01

  43. DBVP Memory (DBVP-M) • DBVP-R ignores memory dependences • often no correlation between load address and value • DBVP-M: same as DBVP-R with additional functionality and information: • propagation of DR through memory • convert def-store-load-use dependence in a component to def-use • livein memory locations(generate locations GLs): • converts values from memory to liveins UPC 17/5/01

  44. DR Propagation Through Memory (spilled variable) UPC 17/5/01

  45. Livein Memory Locations UPC 17/5/01

  46. Generate Locations (GL) and Memory History Table(MHT) • GL is an index into a table (MHT) with values written to memory • Store-load dependent pairs are assigned MHT location • Memory Dependence Prediction mechanisms can provide the extra functionality required by DBVP-M UPC 17/5/01

  47. DBVP-M Predictor UPC 17/5/01

  48. Differences DBVP and CB • Information used to access tables and table sizes • History table is register indexed (vs PC indexed) +smaller +may be easier to manage speculative updates -possibly provide predictions later • Smaller context is required to capture repeated behavior UPC 17/5/01

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