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How to Turn the Technological Constraint Problem into a Control Policy Problem Using Slack Brian FieldsRastislav BodíkMark D. Hill University of Wisconsin-Madison

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The Problem: Managing constraints Technological constraints dominate memory design Non-uniformity: Load latencies Cache hierarchy Design: Memory latency Constraint: Policy: What to replace?

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The Problem: Managing constraints In the future, technological constraints will also dominate microprocessor design Policy Goal: Minimize effect of lower-quality resources Clusters Fast/Slow ALUs Grid, ILDP Design: Wires Power Complexity Constraint: Non-uniformity: Bypasses Exe. Latencies L1 latencies Policy: ? ? ?

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Key Insight: Control policy crucial With non-uniform machines, the technological constraint problem becomes a control policy problem

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Key Insight: Control policy crucial The best possible policy: Delays are imposed only on instructions so that execution time is not increased Achieved through slack: The amount an instruction can be delayed without increasing execution time

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Contributions/Outline Understanding (measure slack in a simulator?) determining slack: resource constraints important reporting slack: apportion to individual instructions analysis: suggest nonuniform machines to build Predicting (how to predict slack in hardware?) simple, delay and observe approach works well Case study (how to design a control policy?) on power-efficient machine, up to 20% speedup

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Determining slack: Why hard? “Probe the processor” approach: Delay and observe 1. Delay dynamic instruction by n cycles 2. See if execution time increased a) No, increase n; restart; go to step 1 Srinivasan and Lebeck approximation, for loads (MICRO ’98) heuristics to predict execution time increase Microprocessors are complex: Sometimes slack is determined by resources (e.g. ROB)

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Determining slack Alternative approach: Dependence-graph analysis 1. Build resource-sensitive dependence graph 2. Analyze to find slack Casmira and Grunwald’s solution (Kool Chips Workshop ’00) Graphs only with instructions in issue window But, how to build resource-sensitive graph?

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Data-Dependence Graph Slack = 0 cycles

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Our Dependence Graph Model (ISCA ‘01) EEEEE FFFFF CCCCC Slack = 0 cycles

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Our Dependence Graph Model (ISCA ‘01) E 1 EEEE FFFFF CCCCC Slack = 6 cycles Modeling resources increases observable slack

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Reporting slack Global slack: # cycles a dynamic operation can be delayed without increasing execution time Apportioned slack: Distribute global slack among operations using an apportioning strategy GS = AS = 10 AS = 5

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Slack measurements (Perl) 6-wide out-of-order superscalar 128-entry issue window 12-stage pipeline

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Slack measurements (Perl) global

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Slack measurements (Perl) apportioned global

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Analysis via apportioning strategy What non-uniform designs can slack tolerate? Design Non-uniformity App. Strategy Fast/slow ALU Exe. latency Double latency Good news: 80% of dynamic instructions can have latency doubled

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Contributions/Outline Understanding (measure slack in a simulator?) determining slack: resource constraints important reporting slack: apportion to individual instructions analysis: suggest nonuniform machines to build Predicting (how to predict slack in hardware?) simple, delay and observe approach works well Case study (how to design a control policy?) on power-efficient machine, up to 20% speedup

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Measuring slack in hardware Goal: Determine whether static instruction has n cycles of slack 1. Delay a dynamic instance by n cycles 2. Check if critical (via critical-path analyzer): a) No, instruction has n cycles of slack b) Yes, instruction does not have n cycles of slack delay and observe ISCA ‘01

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Two predictor designs 2. Implicit slack predictor delay and observe with natural non-uniform delays “Bin” instructions to match non-uniform hardware 1. Explicit slack predictor Retry delay and observe with different values of slack Problem: obtaining unperturbed measurements

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Contributions/Outline Understanding (measure slack in a simulator?) determining slack: resource constraints important reporting slack: apportion to individual instructions analysis: suggest nonuniform machines to build Predicting (how to predict slack in hardware?) simple, delay and observe approach works well Case study (how to design a control policy?) on power-efficient machine, up to 20% speedup

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Fast/slow pipeline microarchitecture Data Cache WIN Reg WIN Reg Fast, 3-wide pipeline Slow, 3-wide pipeline ALUs Fetch + Rename Design has three nonuniformities: Higher execution latencies Increased (cross-domain) bypass latency Decreased effective issue bandwidth Steer Bypass Bus P F 2 save ~37% core power

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Selecting bins for implicit slack predictor Use implicit slack predictor with four (2 2 ) bins: Two decisions 1.Steer to fast/slow pipeline, then 2.Schedule with high/low priority within a pipeline High Low Fast Slow 1 Steer Schedule 2 3 4

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Putting it all together Slack prediction table 4 KB Fast/slow pipeline core Slack bin # Training Path PC Prediction Path Criticality Analyzer ~1 KB 4-bin slack state machine

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Fast/slow pipeline performance 2 fast, high-power pipelines slack-based policy reg-dep steering

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Slack used up Average global slack per dynamic instruction 2 fast, high-power pipelines slack-based policy

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Slack used up Average global slack per dynamic instruction 2 fast, high-power pipelines slack-based policy reg-dep steering

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Conclusion: Future processor design flow Future processors will be non-uniform. A slack-based policy can control them. 1. Measure slack in a simulator decide early on what designs to build 2. Predict slack in hardware simple implementation 3. Design a control policy policy decisions slack bins

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Backup slides

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Define local slack Define Local Slack: # cycles edge latency can be increased without delaying subsequent instructions 2 cycles 1 cycle In real programs, ~20% insts have local slack of at least 5 cycles

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Compute local slack cycles 1 cycle Define Local Slack: # cycles edge latency can be increased without delaying subsequent instructions In real programs, ~20% insts have local slack of at least 5 cycles Arrival Time

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Define global slack Global Slack: # cycles edge latency can be increased without delaying the last instruction in the program cycles 1 cycle In real programs, >90% insts have global slack of at least 5 cycles

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Compute global slack Calculate global slack: backward propagate, accumulating local slacks LS 5 =2 LS 3 =1 LS 1 =1 LS 2 =0 GS 3 =GS 6 +LS 3 =1 GS 1 =MIN(GS 3,GS 5 )+LS 1 =2 GS 6 =LS 6 =0 GS 5 =LS 5 =2 In real programs, >90% insts have global slack of at least 5 cycles

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Apportioned slack Goal: Distribute slack to instructions that need it Thus, apportioning strategy depends upon nature of non-uniformities in machine e.g.: non-uniformity: 2 speed bypass busses (1 cycle, 2 cycle) strategy: give 1 cycle slack to as many edges as possible

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Define apportioned slack Apportioned slack: Distribute global slack among edges For example: GS 3 =1, AS 3 =0 GS 2 =1, AS 2 =1 GS 1 =2, AS 1 =1GS 5 =2, AS 5 =1 In real programs, >75% insts can be apportioned slack of at least 5 cycles

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Slack measurements local apportioned global

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Multi-speed ALUs Can we tolerate ALUs running at half frequency? Yes, but: 1. For all types of operations? (needed for multi-speed clusters) 2. Can we make all integer ops double latency?

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Load slack Can we tolerate a long-latency L1 hit? design: wire-constrained machine, e.g. Grid non-uniformity: multi-latency L1 apportioning strategy: apportion ALL slack to load instructions

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Apportion all slack to loads Most loads can tolerate an L2 cache hit

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Multi-speed ALUs Can we tolerate ALUs running at half frequency? design: fast/slow ALUs non-uniformity: multi-latency execution latency, bypass apportioning strategy: give slack equal to original latency + 1

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Latency+1 apportioning Most instructions can tolerate doubling their latency

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Breakdown by operation (Latency+1 apportioning)

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Validation Two steps: 1. Increase latencies of insts. by their apportioned slack for three apportioning strategies: 1) latency+1, 2) 5-cycles to as many instructions as possible, 3) 12-cycles to as many loads as possible 2. Compare to baseline (no delays inserted)

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Validation Worst case: Inaccuracy of 0.6%

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Predicting slack Two steps to PC-indexed, history-based prediction: 1. Measure slack of a dynamic instruction 2. Store in array indexed by PC of static instruction Need: Locality of slack can capture 80% of potential exploitable slack Need: Ability to measure slack of a dynamic instruction

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Locality of slack experiment For each static instruction: 1. Measure % slackful dynamic instances 2. Multiply by # of dynamic instances 3. Sum across all static instructions 4. Compare to total slackful dynamic instructions (ideal case) slackful = has enough apportioned slack to double latency

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Locality of slack

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PC-indexed, history-based predictor can capture most of the available slack

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Predicting slack Two steps to PC-indexed, history-based prediction: 1. Measure slack of a dynamic instruction 2. Store in array indexed by PC of static instruction Need: Locality of slack can capture 80% of potential exploitable slack Need: Ability to measure slack of a dynamic instruction

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Measuring slack in hardware Goal: Determine whether static instruction has n cycles of slack 1. Delay a dynamic instance by n cycles 2. Check if critical (via critical-path analyzer): a) No, instruction has n cycles of slack b) Yes, instruction does not have n cycles of slack delay and observe

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Review: Critical-path analyzer (ISCA ’01)

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Don’t need to measure latencies

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Review: Critical-path analyzer (ISCA ’01) Just observe last-arriving edges

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Review: Critical-path analyzer (ISCA ’01) Plant token and propagate forward If token survives, node is critical If token dies, node is noncritical

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Baseline policies (existing, not based on slack) 1.Simple reg dep steering (reg dep) Send to fast cluster until: 2.Window half full (fast-first win) 3.Too many ready insts (fast-first rdy)

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Baseline policies (existing, not based on slack) 2 fast clusters register dependence fast-first window fast-first ready

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Slack-based policies 2 fast clusters token-passing slack ALOLD slack reg-dep steering 10% better performance from hiding non-uniformities

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Extra slow cluster (still save ~25% core power) 2 fast clusters token-passing slack ALOLD slack best-existing policy

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