Speculative Multithreaded Processors
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1 Guri Sohi and Amir Roth Computer Sciences Department University of Wisconsin-Madison
2 utline Trends and their implications Workloads for future processors Program parallelization and speculative threads speculative control-driven threads speculative data-driven threads Sample applications and research issues Summary 2
3 Driving Factors Match upcoming technology trends Match upcoming software trends Match upcoming technology constraints Match upcoming design constraints Learn, and exploit, new program behaviors 3
4 Hardware/Design Trends Increasing wire delays Increasing memory latencies Deeper pipelines Design complexity Verification complexity Power issues 4
5 Implications of Trends Distributed microarchitectures Clustered superscalar, with multithreading Chip multiprocessor Question: what to run on underlying microarchitecture? 5
6 Work for Distributed/Multithreaded Processor Independent programs increase overall processing throughput works well in server environment Independent threads of multithreaded application increase overall throughput compatible with software trends? Related threads e.g., for reliability But what about speeding up single program execution? single program speed will continue to be important how to parallelize or multithread single program? 6
7 Program Parallelization What does it mean to parallelize? how to divide program into multiple portions What constrains parallelization? dependences (especially ambiguous) How to overcome constraints? use speculation 7
8 Program Parallelization -- Theme I Traditional view: control-driven threads divide work into multiple groups of instructions - conservative assumptions about dependences constrain parallelization each group is specified using traditional control-driven (von Neumann) semantics A newer view: multiscalar use dependence speculation to overcome constraints commercial example: Sun MAJC 8
9 Multiscalar: Speculative Control-Driven Threads PRGRAM A A predict B predict C B C PRC UNIT 1 PRC UNIT 2 PRC UNIT 3 9
10 Program Parallelization -- Theme II Traditional view: data-driven threads divide work into (dependent) computations each computation is represented in a data-driven manner A newer view: speculative data-driven threads use speculation to facilitate thread creation create threads only for important events 10
11 Motivation for Data-driven Threads Program execution: processing of low-latency instructions, with pauses for high-latency events Parallelizing low-latency instructions isn t crucial verlapping high-latency events is what matters! Threads should create high-latency events early 11
12 Speculative Data-Driven Threads Use dependence relationships to isolate thread(s) of code from main program thread use speculation to facilitate creation Execute threads (speculatively) in parallel with main program assist main thread via side-effects don t impact architectural correctness 12
13 Application: Cache Misses and Branch Mispredicts Memory Control Spec2000 Benchmark # inst % dyn. memops % miss # inst % dyn. branch %misp bzip crafty eon Insufficient misses gap gcc gzip mcf parser perl twolf vortex vpr (route)
14 Performance Leverage IPC oracle-all oracle-probs baseline bzip2 crafty eon gap gcc gzip mcf parser perl twolf vortex vpr Perfecting a small set of instructions provides significant performance much of that of a perfect branch predictor and data cache 14
15 Using Speculative Data-driven Threads RETIREMENT STREAM Pre-execution FRK BRANCH sub program branch mispredict TIME BRANCH BRANCH UTCME BRANCH TIME cache miss AVID MISPREDICTIN LAD 15
16 Sample Performance Results VPR (RUTE) 200M instruction sample (starting at 14.1B on 20B run) 100M instruction warm-up for caches/predictors 32% SPEEDUP: 16% FRM PRE-FETCHING, 16% FRM BRANCHES Cache Misses (primary L1) Branch Mispredictions number rate /1000 inst number rate /1000 inst base 2,850, % ,400, % 7.0 w/slices 1,340, % , %
17 Sample Applications Cache prefetching/management Computing branch outcomes TLB prefetching/management I/ prefetching Multiprocessor communication management ther applications where high-latency events need to be created early 17
18 Some Research Issues How to divide control-driven program into datadriven threads? When to divide program? How to represent data-driven threads? Managing mixed thread workloads 18
19 Summary Hardware and design trends will lead to distributed/multithreaded processors Many options for running different thread types on underlying microarchitecture vercome constraints to parallelization techniques with speculation speculative control-driven threads speculative data-driven threads Most of the research still needs to be done 19
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