The Supporting Structures Design Space in Java

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5 The Supporting Structures Design Space
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51 INTRODUCTION 52 FORCES 53 CHOOSING THE PATTERNS 54 THE SPMD PATTERN 55 THE MASTER/WORKER PATTERN 56 THE LOOP P ARALLELISM P ATTERN 57 THE FORK/JOIN PATTERN 58 THE SHARED DATA PATTERN 59 THE SHARED QUEUE PATTERN 510 THE DISTRIBUTED ARRAY PATTERN 511 OTHER SUPPORTING STRUCTURES
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51 INTRODUCTION
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The Finding Concurrency and Algorithm Structure design spaces focus on algorithm expression At some point, however, algorithms must be translated into programs The patterns in the Supporting Structures design space address that phase of the parallel program design process, representing an intermediate stage between the problem oriented patterns of the Algorithm Structure design space and the specific programming mechanisms described in the Implementation Mechanisms design space We call these patterns Supporting Structures because they describe software constructions or "structures" that support the expression of parallel algorithms An overview of this design space and its place in the pattern language is shown in Fig 51
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Figure 51 Overview of the Supporting Structures design space and its place in the pattern language
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The two groups of patterns in this space are those that represent program structuring approaches and those that represent commonly used shared data structures These patterns are briefly described in the next section In some programming environments, some of these patterns are so well supported that there is little work for the programmer We nevertheless document them as patterns for two reasons: First, understanding the low level details behind these structures is important for effectively using them Second, describing these structures as patterns provides guidance for programmers who might need to implement them from scratch The final section of this chapter describes structures that were not deemed important enough, for various reasons, to warrant a dedicated pattern, but which deserve mention for completeness 511 Program Structuring Patterns Patterns in this first group describe approaches for structuring source code These patterns include the following
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SPMD In an SPMD (Single Program, Multiple Data) program, all UEs execute the same
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program (Single Program) in parallel, but each has its own set of data (Multiple Data) Different UEs can follow different paths through the program In the source code, the logic to control this is expressed using a parameter that uniquely labels each UE (for example a process ID)
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Master/Worker A master process or thread sets up a pool of worker processes or threads and a bag of tasks The workers execute concurrently, with each worker repeatedly removing a task from the bag of tasks and processing it, until all tasks have been processed or some other termination condition has been reached In some implementations, no explicit master is present Loop Parallelism This pattern addresses the problem of transforming a serial program whose runtime is dominated by a set of compute intensive loops into a parallel program where the different iterations of the loop are executed in parallel Fork/Join A main UE forks off some number of other UEs that then continue in parallel to accomplish some portion of the overall work Often the forking UE waits until the child UEs terminate and join
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While we define each of these program structures as a distinct pattern, this is somewhat artificial It is possible, for example, to implement the Master/Worker pattern using the Fork/Join pattern or the SPMD pattern These patterns do not represent exclusive, unique ways to structure a parallel program Rather, they define the major idioms used by experienced parallel programmers These patterns also inevitably express a bias rooted in the subset of parallel programming environments we consider in this pattern language To an MPI programmer, for example, all program structure patterns are essentially a variation on the SPMD pattern To an OpenMP programmer, however, there is a huge difference between programs that utilize thread IDs (that is, the SPMD pattern) versus programs that express all concurrency in terms of loop level worksharing constructs (that is, the Loop Parallelism pattern) Therefore, in using these patterns, don't think of them too rigidly These patterns express important techniques and are worthy of consideration in isolation, but do not hesitate to combine them in different ways to meet the needs of a particular problem For example, in the SPMD pattern, we will discuss parallel algorithms based on parallelizing loops but expressed with the SPMD pattern It might seem that this indicates that the SPMD and Loop Parallelism patterns are not really distinct patterns, but in fact it shows how flexible the SPMD pattern is 512 Patterns Representing Data Structures Patterns in this second group have to do with managing data dependencies The Shared Data pattern deals with the general case The others describe specific frequently used data structures
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Shared Data This pattern addresses the general problem of handling data that is shared by more than one UE, discussing both correctness and performance issues Shared Queue This pattern represents a "thread safe" implementation of the familiar queue abstract data type (ADT), that is, an implementation of the queue ADT that maintains the correct semantics even when used by concurrently executing UEs
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Distributed Array This pattern represents a class of data structures often found in parallel scientific computing, namely arrays of one or more dimensions that are decomposed into subarrays and distributed among processes or threads
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