Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism
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1 Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism Raj Parihar Advisor: Prof. Michael C. Huang March 22, 2013 Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism
2 Despite the proliferation of multi-core, multi-threaded systems Single-thread performance is still an important design goal Modern programs do not lack instruction level parallelism Real challenge: exploit implicit parallelism without undue costs One effective approach: Decoupled look-ahead K K 107 IPC 10 1 bzip2 crafty eon gap gcc gzip mcf pbmk twolf vortex vpr Gmean Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 2
3 Despite the proliferation of multi-core, multi-threaded systems Single-thread performance is still an important design goal Modern programs do not lack instruction level parallelism Real challenge: exploit implicit parallelism without undue costs One effective approach: Decoupled look-ahead K K 107 IPC 10 1 bzip2 crafty eon gap gcc gzip mcf pbmk twolf vortex vpr Gmean Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 3
4 : Decoupled Look-ahead Decoupled look-ahead architecture targets Performance hurdles: branch mispredictions, cache misses, etc. Exploration of parallelization opportunities, dependence information Microarchitectural complexity, energy inefficiency through decoupling Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 4
5 : Decoupled Look-ahead Decoupled look-ahead architecture targets Performance hurdles: branch mispredictions, cache misses, etc. Exploration of parallelization opportunities, dependence information Microarchitectural complexity, energy inefficiency through decoupling The look-ahead thread can often become a new bottleneck Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 4
6 : Decoupled Look-ahead Decoupled look-ahead architecture targets Performance hurdles: branch mispredictions, cache misses, etc. Exploration of parallelization opportunities, dependence information Microarchitectural complexity, energy inefficiency through decoupling The look-ahead thread can often become a new bottleneck We explore techniques to accelerate the look-ahead thread Speculative parallelization: aptly suited due to increased parallelism in the look-ahead binary Weak dependence: lack of correctness constraint allows weak instruction removal w/o affecting the quality of look-ahead Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 4
7 Outline Speculative parallelization in look-ahead Weak dependence removal in look-ahead Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 5
8 Baseline Decoupled Look-ahead Binary parser is used to generate skeleton from original program The skeleton runs on a separate core and Maintains its memory image in local L1, no writeback to shared L2 Sends branch outcomes through FIFO queue; also helps prefetching A. Garg and M. Huang, A Performance-Correctness Explicitly Decoupled Architecture, MICRO-08 Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 6
9 Practical Advantages of Decoupled Look-ahead Look-ahead thread is a self-reliant agent, completely independent of main thread No need for quick spawning and register communication support Low management overhead on main thread Easier for run-time control to disable Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 7
10 Practical Advantages of Decoupled Look-ahead Look-ahead thread is a self-reliant agent, completely independent of main thread No need for quick spawning and register communication support Low management overhead on main thread Easier for run-time control to disable Natural throttling mechanism to prevent Run-away prefetching, cache pollution Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 7
11 Practical Advantages of Decoupled Look-ahead Look-ahead thread is a self-reliant agent, completely independent of main thread No need for quick spawning and register communication support Low management overhead on main thread Easier for run-time control to disable Natural throttling mechanism to prevent Run-away prefetching, cache pollution Look-ahead thread size comparable to aggregation of short helper threads Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 7
12 Look-ahead: A New Bottleneck Comparing four systems to discover new bottlenecks Single-thread, decoupled look-ahead, ideal, and look-ahead alone Application categories: Bottleneck removed or speed of look-ahead is not an issue Look-ahead thread is the new bottleneck (right half) 4 Single thread Decoupled look ahead Ideal(Cache,Br) Look ahead only 3 IPC apl msa wup mgr six swim fac gal gcc gap eon fm3d gzip crft vor apsi vpr bzp2 eqk amp luc art pbmkmcf twlf Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 8
13 Look-ahead: A New Bottleneck Comparing four systems to discover new bottlenecks Single-thread, decoupled look-ahead, ideal, and look-ahead alone Application categories: Bottleneck removed or speed of look-ahead is not an issue Look-ahead thread is the new bottleneck (right half) 4 Single thread Decoupled look ahead Ideal(Cache,Br) Look ahead only 3 IPC apl msa wup mgr six swim fac gal gcc gap eon fm3d gzip crft vor apsi vpr bzp2 eqk amp luc art pbmkmcf twlf Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 9
14 Look-ahead: A New Bottleneck Comparing four systems to discover new bottlenecks Single-thread, decoupled look-ahead, ideal, and look-ahead alone Application categories: Bottleneck removed or speed of look-ahead is not an issue Look-ahead thread is the new bottleneck (right half) 4 3 Single thread Decoupled look ahead Ideal(Cache,Br) Look ahead only 11% 16% 11% 47% 35% 27% 47% 68% 53% 70% 81% 117% 189% 154% 262% 262% IPC apl msa wup mgr six swim fac gal gcc gap eon fm3d gzip crft vor apsi vpr bzp2 eqk amp luc art pbmkmcf twlf Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 10
15 Look-ahead: A New Bottleneck Comparing four systems to discover new bottlenecks Single-thread, decoupled look-ahead, ideal, and look-ahead alone Application categories: Bottleneck removed or speed of look-ahead is not an issue Look-ahead thread is the new bottleneck (right half) 4 3 Single thread Decoupled look ahead Ideal(Cache,Br) Look ahead only 11% 16% 11% 47% 35% 27% 47% 68% 53% 70% 81% 117% 189% 154% 262% 262% IPC apl msa wup mgr six swim fac gal gcc gap eon fm3d gzip crft vor apsi vpr bzp2 eqk amp luc art pbmkmcf twlf Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 11
16 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Unique Opportunities for Speculative Parallelization Skeleton code offers more parallelism Certain dependencies removed during slicing for skeleton Short-distance dependence chains become long-distance chains, suitable for TLP exploitation Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 12
17 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Unique Opportunities for Speculative Parallelization Skeleton code offers more parallelism Certain dependencies removed during slicing for skeleton Short-distance dependence chains become long-distance chains, suitable for TLP exploitation Look-ahead is inherently error-tolerant Can ignore dependence violations Little to no support needed, unlike in conventional TLS Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 12
18 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Software Support Dependence analysis Profile guided, coarse-grain at basic block level Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 13
19 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Software Support Dependence analysis Profile guided, coarse-grain at basic block level Spawn and Target points Basic blocks with consistent dependence distance of more than threshold of D MIN Spawned thread executes from target point Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 13
20 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Software Support Dependence analysis Profile guided, coarse-grain at basic block level Spawn and Target points Basic blocks with consistent dependence distance of more than threshold of D MIN Spawned thread executes from target point Loop level parallelism is also exploited Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 13
21 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Parallelism Potential in Look-ahead Binary Available parallelism for 2 core/contexts system; D MIN = 15BB Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 14
22 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Parallelism Potential in Look-ahead Binary Available parallelism for 2 core/contexts system; D MIN = 15BB Skeleton exhibits significant more BB level parallelism (17%) Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 14
23 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Parallelism Potential in Look-ahead Binary Available parallelism for 2 core/contexts system; D MIN = 15BB Skeleton exhibits significant more BB level parallelism (17%) Loop based FP applications exhibit more BB level parallelism Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 14
24 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Hardware and Runtime Support Thread spawning and merging are very similar to regular thread spawning except Spawned thread shares the same register and memory state Spawning thread terminates at the target PC Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 15
25 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Hardware and Runtime Support Thread spawning and merging are very similar to regular thread spawning except Spawned thread shares the same register and memory state Spawning thread terminates at the target PC Value communication Register-based naturally through shared registers in SMT Memory-based communication can be supported at different levels Partial versioning in cache at line level Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 15
26 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speedup of Speculative Parallelization 14 applications in which the look-ahead thread is bottleneck Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 16
27 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speedup of Speculative Parallelization 14 applications in which the look-ahead thread is bottleneck Speedup of look-ahead systems over single-thread Decoupled look-ahead over single-thread baseline: 1.53x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 16
28 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speedup of Speculative Parallelization 14 applications in which the look-ahead thread is bottleneck Speedup of look-ahead systems over single-thread Decoupled look-ahead over single-thread baseline: 1.53x Speculative parallel look-ahead over single-thread: 1.73x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 16
29 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speedup of Speculative Parallelization 14 applications in which the look-ahead thread is bottleneck Speedup of look-ahead systems over single-thread Decoupled look-ahead over single-thread baseline: 1.53x Speculative parallel look-ahead over single-thread: 1.73x Speculative look-ahead over decoupled look-ahead: 1.13x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 16
30 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speculative Look-ahead vs Conventional TLS Skeleton provides more opportunities for parallelization Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 17
31 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speculative Look-ahead vs Conventional TLS Skeleton provides more opportunities for parallelization Speculative look-ahead over decoupled LA baseline: 1.13x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 17
32 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Speculative Look-ahead vs Conventional TLS Skeleton provides more opportunities for parallelization Speculative look-ahead over decoupled LA baseline: 1.13x Speculative main thread over single thread baseline: 1.07x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 17
33 Speculative parallelization in look-ahead Weak dependence removal in look-ahead for Exploiting Weak Dependences Not all instructions are equally important and critical Example of weak instructions: Inconsequential adjustments Load and store instructions that are (mostly) silent Dynamic NOP instructions Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 18
34 Speculative parallelization in look-ahead Weak dependence removal in look-ahead for Exploiting Weak Dependences Not all instructions are equally important and critical Example of weak instructions: Inconsequential adjustments Load and store instructions that are (mostly) silent Dynamic NOP instructions Plenty of weak instructions are present in programs Challenges involved: Context-based, hard to identify and combine much like Jenga Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 18
35 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Comparison of Weak and Strong Instructions Static attributes of weak and strong insts are remarkably same Static attributes: opcode, number of inputs The correlation coefficient of the two distributions is 0.96 Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 19
36 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Comparison of Weak and Strong Instructions Static attributes of weak and strong insts are remarkably same Static attributes: opcode, number of inputs The correlation coefficient of the two distributions is 0.96 Weakness has very poor correlation with static attributes Hard to identify the weak insts through static heuristics Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 19
37 Genetic Algorithm based Framework Speculative parallelization in look-ahead Weak dependence removal in look-ahead Genetic algorithm based framework to identify and eliminate weak instructions from the look-ahead skeleton Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 20
38 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Genetic Algorithm based Framework Genetic algorithm based framework to identify and eliminate weak instructions from the look-ahead skeleton Genetic evolution: procreation and natural selection Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 20
39 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Genetic Algorithm based Framework Genetic algorithm based framework to identify and eliminate weak instructions from the look-ahead skeleton Genetic evolution: procreation and natural selection Chromosomes creation and hybridization Baseline look-ahead skeleton construction Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 20
40 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Heuristic Based Solutions Heuristic based solutions are helpful to jump start the evolution Superposition based chromosomes Orthogonal subroutine based chromosomes Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 21
41 Speculative parallelization in look-ahead Weak dependence removal in look-ahead Progress of Genetic Evolution Process Per generation progress compared to the final best solution After 2 generations, more than half of the benefits are achieved After 5 generations, at least 90% of benefits are achieved Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 22
42 Experimental Setup Program/binary analysis tool: based on ALTO Simulator: based on heavily modified SimpleScalar SMT, look-ahead and speculative parallelization support True execution-driven simulation (faithfully value modeling) Genetic algorithm framework Modeled as offline and online extension to the simulator Microarchitectural config: Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 23
43 Speedup of Self-tuned Look-ahead Applications in which the look-ahead thread is a bottleneck Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 24
44 Speedup of Self-tuned Look-ahead Applications in which the look-ahead thread is a bottleneck Self-tuned, genetic algorithm based decoupled look-ahead Speedup over baseline decoupled look-ahead: 1.16x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 24
45 Speedup of Self-tuned Look-ahead Applications in which the look-ahead thread is a bottleneck Self-tuned, genetic algorithm based decoupled look-ahead Speedup over baseline decoupled look-ahead: 1.16x Speedup over single-thread baseline: 1.78x Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 24
46 Comparison with Speculative Parallel Look-ahead Self-tuned skeleton is used in the speculative parallel look-ahead In some cases, self-tuned and speculative parallel look-ahead techniques are synergistic (ammp, art) Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 25
47 Ongoing and Future Explorations Load balancing through skipping non-critical branches Weak instruction classification and identification Single-core version of decoupled look-ahead Static heuristics to construct adaptive skeletons Role of look-ahead to promote parallelization and accelerate the execution of interpreted programs Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 26
48 Decoupled look-ahead can uncover significant implicit parallelism However, look-ahead thread often becomes a new bottleneck Fortunately, look-ahead lends itself to various optimizations: Speculative parallelization is more beneficial in look-ahead thread Weak instructions can be removed w/o affecting look-ahead quality Intelligent look-ahead technique is a promising solution in the era of flat frequency and modest microarchitecture scaling Idle cores in multicore environment will further strengthen the case of decoupled look-ahead adoption in mainstream system Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 27
49 Backup Slides Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism Raj Parihar Advisor: Prof. Michael C. Huang March 22, 2013 Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 28
50 Microthreads vs Decoupled Look-ahead Lightweight Microthreads: Decoupled Look-ahead: Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 29
51 Look-ahead Skeleton Construction Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 30
52 Performance Benefits of Decoupled Look-ahead Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 31
53 IPC of Speculative Parallelization Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 32
54 Speculative Parallelization: Cortex-A9 vs POWER5 Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 33
55 Flexibility in Look-ahead Hardware Design Comparison of regular (partial versioning) cache support with two other alternatives No cache versioning support Dependence violation detection and squash Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 34
56 Partial Recoveries and Spawns Partial recoveries: Breakdown of all the spawns: Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 35
57 Genetic Algorithm Evolution Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 36
58 Multi-instruction Gene Examples Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 37
59 Optimizations to Implementation Fitness test optimizations Sampling based fitness Multi-instruction genes Early termination of tests GA framework optimizations Hybridization of solutions Adaptive mutation rate Unique chromosomes Fusion crossover operator Elitism policy Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 38
60 Superposition based Chromosomes Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 39
61 Recovery based Early Termination of Fitness Test Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 40
62 Weak Dependence: IPC and Instructions Removed IPC comparison: Instructions removed from baseline skeleton: Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 41
63 Sampling based Fitness Test Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 42
64 L2 Cache Sensitivity Study Speedup for various L2 caches is quite stable 1.161x (1 MB), 1.154x (2 MB), and 1.152x (4 MB) L2 caches Avg. speedups, shown in the figure, are relative to single-threaded execution with a 1 MB L2 cache Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 43
65 Single-Gene vs Heuristic based Chromosomes Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 44
66 Genetic Algorithm based Heuristics Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 45
67 Other Details Energy reduction: 11% over baseline decoupled look-ahead Reduced cache accesses, less stalling of main thread On an average, 10% of the dynamic instructions are removed from the baseline skeleton Offline profiling and control software overhead Offline profiling time: 2 to 20 seconds on the target machine Online control software: 17 million instructions for whole evolution Average extra recoveries: 3-4 per 100,000 instructions Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 46
68 Load Balancing in Look-ahead Non-critical branches can be transformed to accelerate the look-ahead thread and achieve better load balancing Non-critical branches are skipped in the look-ahead thread Main thread executes such branches by itself w/o any help We call these branches Do-It-Yourself or DIY branches Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 47
69 Preliminary Speedup of Load Balancing Load balancing through skipping of the non-critical branches Max. speedup over decoupled look-ahead: 1.76x (art) Avg. speedup over decoupled look-ahead: 1.12x (Gmean) Raj Parihar Accelerating Decoupled Look-ahead to Exploit Implicit Parallelism 48
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