Topics on Compilers Spring Semester Christine Wagner 2011/04/13

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1 Topics on Compilers Spring Semester 2011 Christine Wagner 2011/04/13

2 Availability of multicore processors Parallelization of sequential programs for performance improvement Manual code parallelization: time-consuming and error-prone process Compiler based automatic parallelization techniques Speculative parallelization 2011/04/13 2

3 But: parallelization difficult due to cross-iteration dependences Different solutions: DOACROSS parallelism techniques Thread level speculation (TLS) Speculative parallelization 2011/04/13 3

4 Speculative parallelization of loops in sequential programs containing dependences Use of: State separation Multiple value prediction Speculative parallelization 2011/04/13 4

5 TLS: Optimistic assumption: no occurring dependence Parallel execution of statements Possible misspeculation worse performance Speculative parallelization 2011/04/13 5

6 Multiple Value Predictions: Distinct versions of following iteration Parallel computation Speculative parallelization 2011/04/13 6

7 One main thread: first iteration Multiple parallel threads: speculative versions of following iteration Executed in separate memory states simultaneously Speculative parallelization 2011/04/13 7

8 Separation of non-speculative and speculative states (main thread parallel threads) Logical division of entire shared memory space: D: non-speculative state; updates only by main thread P: speculative state; communication of results to main thread C: coordinating state; synchronization and tracking Speculative parallelization 2011/04/13 8

9 D and P memories must support stack, global and heap sections since they are used by all threads Each of these sections must provide state separation Stack Separation: Allocation of local variables, accessed through stack pointer Each thread has own stack and stack-pointer Speculative parallelization 2011/04/13 9

10 Global Section Separation: Stored variables only used by main thread Local copies of global variables used by parallel threads Heap Separation: Dynamic memory allocation on one shared heap Logical separation in D, P and C space heaps when heap chunk is allocated by a thread or for coordination Speculative parallelization 2011/04/13 10

11 Speculative parallelization 2011/04/13 11

12 A loop is a good candidate for parallelization if: 1. Loop has frequent loop-carried dependences 2. Values carried by loop dependences are predictable 1. condition: profiling based dependence analysis 2. condition: variable is predictable if its value can be computed through backward data slicing Speculative parallelization 2011/04/13 12

13 Finding parts of a program which are relevant to the value of a variable at a chosen point (slicing criterion) Deleting irrelevant parts Backward slice: statements with some effect on the criterion Forward slice: statements affected by the criterion Speculative parallelization 2011/04/13 13

14 Generate multiple versions: Compute backward data slices of a variable for each different control flow path Create multiple versions of the second iteration Data slice and path information are computed based on profiling trace Speculative parallelization 2011/04/13 14

15 Version confidence: probability of a version being correct VC = path execution frequency prediction confidence Prediction confidence always 1 if data slice is used as prediction code Multiple variables on same path: merge data slices Speculative parallelization 2011/04/13 15

16 But: performance can be limited due to large data slices Computing only partial slice: values of a variable can be computed based on predictions of other variables in original slice Search point where all variables in the slice can either be computed or predicted with high confidence using simple value predictors Speculative parallelization 2011/04/13 16

17 Speculative parallelization 2011/04/13 17

18 If partial slice is used instead of complete data slice: VC = path execution frequency OCBQ OCBQ = overall confidence of boundary queue Speculative parallelization 2011/04/13 18

19 Compiler performs code transformation Speculative parallelization 2011/04/13 19

20 Speculative parallelization 2011/04/13 20

21 Two problems with basic scheme: Small number of versions cover all popular execution paths: VCs of these versions very high Executing versions with small VCs wastes cores Speculative parallelization 2011/04/13 21

22 VC computation relies on path frequency information: Real run: different frequencies due to different inputs which leads to change of VC Some versions less likely to be correct than expected Wastes cores Speculative parallelization 2011/04/13 22

23 Adaptive technique for better use of available cores: Key idea: consider versions with higher VC as candidates for executing additional iterations beyond second iteration Avoid using cores to execute versions with small VCs (threshold) Speculative parallelization 2011/04/13 23

24 Benchmark: ten tested programs Performance analysis: Basic scheme: highest speedups from 1.12x to 1.51x (3 or 4 versions used) Adaptive scheme: highest speedups from 1.18x to 2.33x (7 versions used) Average utilization of cores: 4 Speculative parallelization 2011/04/13 24

25 Overhead analysis: Time overhead: less than 25 % obtained speedups outweigh cost of overhead Speculative parallelization 2011/04/13 25

26 Speculative parallelization technique: Implemented purely in software Using multiple value predictions and State separation Exploits parallelism between consecutive loop iterations Experimental results: average speedup of 1.7x Speculative parallelization 2011/04/13 26

27 Thank you for listening! Please feel free to ask questions! Speculative parallelization 2011/04/13 27

28 Tian, C., Feng, M. and Gupta, R.: Speculative parallelization using state separation and multiple value prediction. Proceedings of ISSM 10, June 5 6, 2010, Toronto, Ontario, Canada. pp Speculative parallelization 2011/04/13 28

Speculative Parallelization Using State Separation and Multiple Value Prediction

Speculative Parallelization Using State Separation and Multiple Value Prediction Chen Tian, Min Feng, Rajiv Gupta University of California, CSE Department, Riverside, CA, 92521 {tianc, mfeng, gupta}@cs.ucr.edu