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52 FORCES
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All of the program structuring patterns address the same basic problem: how to structure source code to best support algorithm structures of interest Unique forces are applicable to each pattern, but in designing a program around these structures, there are some common forces to consider in most cases:
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Clarity of abstraction Is the parallel algorithm clearly apparent from the source code In a well structured program, the algorithm leaps from the page The reader can see the details of the algorithm with little mental effort We refer to this quality as clarity of abstraction Good clarity of abstraction is always important for writing correct code, but is particularly essential for parallel programs: Parallel programmers must deal with multiple simultaneous tasks that interact in subtle ways Getting this right can be very difficult, especially if it is hard to figure out what the algorithm is doing by looking at the source code
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Scalability How many processors can the parallel program effectively utilize The scalability of a program is restricted by three factors First, there is the amount of concurrency available in the algorithm If an algorithm only has ten concurrent tasks, then running with more than ten PEs will provide no benefit Second, the fraction of the runtime spent doing inherently serial work limits how many processors can be used This is described quantitatively by Amdahl's law as discussed in 2 Finally, the parallel overhead of the algorithm contributes to the serial fraction mentioned in Amdahl's law and limits scalability
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Efficiency How close does the program come to fully utilizing the resources of the parallel computer Recall the quantitative definition of efficiency given in 2: Equation 51
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Equation 52
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P is the number of PEs, T(1) is some sequential reference time, and T(P) is the parallel time with P PEs S(P) is the speedup The most rigorous definition of efficiency sets T(1) to the execution time of the best sequential algorithm corresponding to the parallel algorithm under study When analyzing parallel programs, "best" sequential algorithms are not always available, and it is common to use the runtime for the parallel program on a single PE as the reference time This can inflate the efficiency because managing the parallel computation always incurs some overhead, even
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when executing on a single PE Efficiency is closely related to scalability because every highly scalable algorithm is also highly efficient Even when the scalability is limited by the available number of tasks or the parallel hardware, however, algorithms can differ in their efficiency
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Maintainability Is the program easy to debug, verify, and modify Casting an algorithm into source code is almost never a one time proposition Programs need to be debugged, new features added, performance tuned, etc These changes to the source code are referred to as maintenance Programs are more or less maintainable depending on how hard it is to make these changes and to do them correctly
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Environmental affinity Is the program well aligned with the programming environment and hardware of choice If the hardware, for example, lacks support for shared memory, an algorithm structure based on shared memory would be a poor choice This issue also comes up when considering programming environments When creating a programming environment, the creators usually have a particular style of programming in mind For example, OpenMP is designed specifically for programs consisting of a series of loops, the iterations of which will be split between multiple threads (loop based parallelism) It is much easier to write software when the program structure employed is well aligned with the programming environment
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Sequential equivalence Where appropriate, does a program produce equivalent results when run with many UEs as with one If not equivalent, is the relationship between them clear It is highly desirable that the results of an execution of a parallel program be the same regardless of the number of PEs used This is not always possible, especially if bitwise equivalence is desired, because floating point operations performed in a different order can produce small (or, for ill conditioned algorithms, not so small) changes in the resulting values However, if we know that the parallel program gives equivalent results when executed on one processor as many, then we can reason about correctness and do most of the testing on the single processor version This is much easier, and thus, when possible, sequential equivalence is a highly desirable goal
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