EE382N (20): Computer Architecture - Parallelism and Locality Spring 2015 Lecture 14 Parallelism in Software I

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1 EE382 (20): Computer Architecture - Parallelism and Locality Spring 2015 Lecture 14 Parallelism in Software I Mattan Erez The University of Texas at Austin EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez 1

2 Credits 2 Most of the slides courtesy Dr. Rodric Rabbah (IBM) Taken from IAP taught at MIT in 2007 Parallel Scan slides courtesy David Kirk (VIDIA) and Wen-Mei Hwu (UIUC) Taken from EE493-AI taught at UIUC in Sprig 2007 EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

3 Parallel programming from scratch 3 Start with an algorithm Formal representation of problem solution Sequence of steps Make sure there is parallelism In each algorithm step Minimize synchronization points Don t forget locality Communication is costly Performance, Energy, System cost More often start with existing sequential code EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

4 Reengineering for Parallelism 4 Define a testing protocol Identify program hot spots: where is most of the time spent? Look at code Use profiling tools Parallelization Start with hot spots first Make sequences of small changes, each followed by testing Patterns provide guidance EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

5 4 Common Steps to Creating a Parallel Program 5 Partitioning d e c o m p o s i t i o n a s s i g n m e n t UE 0 UE 1 UE 2 UE 3 o r c h e s t r a t i o n UE 0 UE 1 UE 2 UE 3 m a p p i n g UE 0 UE 1 UE 2 UE 3 Sequential Computation Tasks Units of Execution Parallel program Processors EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

6 Decomposition 6 Identify concurrency and decide at what level to exploit it Break up computation into tasks to be divided among processes Tasks may become available dynamically umber of tasks may vary with time Enough tasks to keep processors busy umber of tasks available at a time is upper bound on achievable speedup Main consideration: coverage and Amdahl s Law EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

7 Coverage 7 Amdahl's Law: The performance improvement to be gained from using some faster mode of execution is limited by the fraction of the time the faster mode can be used. Demonstration of the law of diminishing returns EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

8 Amdahl s Law 8 Potential program speedup is defined by the fraction of code that can be parallelized time Use 5 processors for parallel work 25 seconds + sequential 25 seconds + sequential 50 seconds + parallel 10 seconds + 25 seconds sequential 25 seconds sequential 100 seconds 60 seconds EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

9 Amdahl s Law 9 time Use 5 processors for parallel work 25 seconds + sequential 25 seconds + sequential 50 seconds + parallel 10 seconds + 25 seconds sequential 25 seconds sequential 100 seconds 60 seconds Speedup= old running time / new running time = 100 seconds / 60 seconds = 1.67 (parallel version is 1.67 times faster) EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

10 Amdahl s Law 10 p = fraction of work that can be parallelized n = the number of processor speedup old running new running time time (1 fraction of time to complete sequential work 1 p) p n EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez fraction of time to complete parallel work

11 speedup Implications of Amdahl s Law 11 Speedup tends to tends to infinity as 1 number of processors 1 p Super linear speedups are possible due to registers and caches Typical speedup is less than linear Parallelism number of only processors worthwhile when it dominates execution EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

12 Assignment 12 Specify mechanism to divide work among PEs Balance work and reduce communication Structured approaches usually work well Code inspection or understanding of application Well-known design patterns As programmers, we worry about partitioning first Independent of architecture or programming model? Complexity often affects decisions Architectural model affects decisions Main considerations: granularity and locality EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

13 Fine vs. Coarse Granularity 13 Fine-grain Parallelism Low computation to communication ratio Small amounts of computational work between communication stages High communication overhead Potential HW assist Coarse-grain Parallelism High computation to communication ratio Large amounts of computational work between communication events Harder to load balance efficiently EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

14 Load Balancing vs. Synchronization 14 Fine Coarse PE 0 PE 1 PE 0 PE 1 EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

15 Load Balancing vs. Synchronization 15 Fine Coarse PE 0 PE 1 PE 0 PE 1 Expensive sync coarse granularity Few units of exec + time disparity fine granularity EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

16 Orchestration and Mapping 16 Computation and communication concurrency Preserve locality of data Schedule tasks to satisfy dependences early Survey available mechanisms on target system Main considerations: locality, parallelism, mechanisms (efficiency and dangers) EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

17 Parallel Programming by Pattern 17 Provides a cookbook to systematically guide programmers Decompose, Assign, Orchestrate, Map Can lead to high quality solutions in some domains Provide common vocabulary to the programming community Each pattern has a name, providing a vocabulary for discussing solutions Helps with software reusability, malleability, and modularity Written in prescribed format to allow the reader to quickly understand the solution and its context Otherwise, too difficult for programmers, and software will not fully exploit parallel hardware EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

18 History 18 Berkeley architecture professor Christopher Alexander In 1977, patterns for city planning, landscaping, and architecture in an attempt to capture principles for living design EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

19 Example 167 (p. 783): 6ft Balcony 19 EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

Patterns in Object-Oriented Programming 20 Design Patterns: Elements of Reusable Object- Oriented Software (1995) Gang of Four (GOF): Gamma, Helm, Johnson,

20 Patterns in Object-Oriented Programming 20 Design Patterns: Elements of Reusable Object- Oriented Software (1995) Gang of Four (GOF): Gamma, Helm, Johnson, Vlissides Catalogue of patterns Creation, structural, behavioral EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

Patterns for Parallelizing Programs 21 4 Design Spaces Algorithm Expression Finding Concurrency Expose concurrent tasks Algorithm Structure Map tasks to processes to exploit parallel architecture

21 Patterns for Parallelizing Programs 21 4 Design Spaces Algorithm Expression Finding Concurrency Expose concurrent tasks Algorithm Structure Map tasks to processes to exploit parallel architecture Software Construction Supporting Structures Code and data structuring patterns Implementation Mechanisms Low level mechanisms used to write parallel programs EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez Patterns for Parallel Programming. Mattson, Sanders, and Massingill (2005).

22 Here s my algorithm. Where s the concurrency? 22 MPEG Decoder frequency encoded macroblocks ZigZag IQuantization MPEG bit stream VLD macroblocks, motion vectors split differentially coded motion vectors Motion Vector Decode IDCT Repeat Saturation spatially encoded macroblocks join motion vectors Motion Compensation Picture Reorder recovered picture Color Conversion Display EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

23 Here s my algorithm. Where s the concurrency? 23 MPEG Decoder frequency encoded macroblocks ZigZag IQuantization MPEG bit stream VLD macroblocks, motion vectors split differentially coded motion vectors Motion Vector Decode Task decomposition Independent coarse-grained computation Inherent to algorithm IDCT Saturation spatially encoded macroblocks join Motion Compensation Picture Reorder recovered picture Repeat motion vectors Sequence of statements (instructions) that operate together as a group Corresponds to some logical part of program Usually follows from the way programmer thinks about a problem Color Conversion Display EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

24 Here s my algorithm. Where s the concurrency? 24 MPEG Decoder frequency encoded macroblocks ZigZag MPEG bit stream VLD macroblocks, motion vectors split differentially coded motion vectors Task decomposition Parallelism in the application IQuantization IDCT Saturation Motion Vector Decode Repeat Pipeline task decomposition Data assembly lines spatially encoded macroblocks join motion vectors Producer-consumer chains Motion Compensation recovered picture Picture Reorder Color Conversion Display EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

25 Here s my algorithm. Where s the concurrency? 25 MPEG Decoder frequency encoded macroblocks ZigZag MPEG bit stream VLD macroblocks, motion vectors split differentially coded motion vectors Task decomposition Parallelism in the application IQuantization IDCT Saturation Motion Vector Decode Repeat Pipeline task decomposition Data assembly lines spatially encoded macroblocks join motion vectors Producer-consumer chains Motion Compensation Picture Reorder Color Conversion recovered picture Data decomposition Same computation is applied to small data chunks derived from large data set Display EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

26 Guidelines for Task Decomposition 26 Algorithms start with a good understanding of the problem being solved Programs often naturally decompose into tasks Two common decompositions are Function calls and Distinct loop iterations Easier to start with many tasks and later fuse them, rather than too few tasks and later try to split them EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

27 Guidelines for Task Decomposition 27 Flexibility Program design should afford flexibility in the number and size of tasks generated Tasks should not tied to a specific architecture Fixed tasks vs. Parameterized tasks Efficiency Tasks should have enough work to amortize the cost of creating and managing them Tasks should be sufficiently independent so that managing dependencies doesn t become the bottleneck Simplicity The code has to remain readable and easy to understand, and debug EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

28 Case for Pipeline Decomposition 28 Data is flowing through a sequence of stages Assembly line is a good analogy ZigZag IQuantization IDCT What s a prime example of pipeline decomposition in computer architecture? Instruction pipeline in modern CPUs Saturation What s an example pipeline you may use in your UIX shell? Pipes in UIX: cat foobar.c grep bar wc Other examples Signal processing Graphics EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

29 Guidelines for Data Decomposition 29 Data decomposition is often implied by task decomposition Programmers need to address task and data decomposition to create a parallel program Which decomposition to start with? Data decomposition is a good starting point when Main computation is organized around manipulation of a large data structure Similar operations are applied to different parts of the data structure EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

30 Common Data Decompositions 30 Geometric data structures Decomposition of arrays along rows, columns, blocks Decomposition of meshes into domains EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

Common Data Decompositions 31 Geometric data structures Decomposition of arrays along rows, columns, blocks Decomposition of meshes into domains Recursive data structures Example: decomposition of

31 Common Data Decompositions 31 Geometric data structures Decomposition of arrays along rows, columns, blocks Decomposition of meshes into domains Recursive data structures Example: decomposition of trees into sub-trees problem subproblem split split subproblem split compute subproblem compute subproblem compute subproblem compute subproblem merge merge subproblem subproblem merge EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez solution

32 Guidelines for Data Decomposition 32 Flexibility Size and number of data chunks should support a wide range of executions Efficiency Data chunks should generate comparable amounts of work (for load balancing) Simplicity Complex data compositions can get difficult to manage and debug EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

33 Data Decomposition Examples 33 Molecular dynamics Compute forces Update accelerations and velocities Update positions Decomposition Baseline algorithm is 2 All-to-all communication Best decomposition is to treat mols. as a set Some advantages to geometric discussed in future lecture EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez

34 Data Decomposition Examples 34 Molecular dynamics Geometric decomposition Merge sort Recursive decomposition subproblem split problem split subproblem split compute subproblem compute subproblem compute subproblem compute subproblem merge merge subproblem subproblem merge EE382: Parallelilsm and Locality, Spring Lecture 14 (c) Rodric Rabbah, Mattan Erez solution

EE382N (20): Computer Architecture - Parallelism and Locality Fall 2011 Lecture 11 Parallelism in Software II

EE382N (20): Computer Architecture - Parallelism and Locality Fall 2011 Lecture 11 Parallelism in Software II EE382 (20): Computer Architecture - Parallelism and Locality Fall 2011 Lecture 11 Parallelism in Software II Mattan Erez The University of Texas at Austin EE382: Parallelilsm and Locality, Fall 2011 --