Design quality in Java

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Keeping these characteristics of the target platform in mind, we can evaluate the design along the three dimensions of flexibility, efficiency, and simplicity Flexibility It is desirable for the high level design to be adaptable to a variety of different implementation requirements, and certainly all the important ones The rest of this section provides a partial checklist of factors that affect flexibility
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Is the decomposition flexible in the number of tasks generated Such flexibility allows the design to be adapted to a wide range of parallel computers Is the definition of tasks implied by the task decomposition independent of how they are scheduled for execution Such independence makes the load balancing problem easier to solve Can the size and number of chunks in the data decomposition be parameterized Such parameterization makes a design easier to scale for varying numbers of PEs Does the algorithm handle the problem's boundary cases A good design will handle all relevant cases, even unusual ones For example, a common operation is to transpose a matrix so that a distribution in terms of blocks of matrix columns becomes a distribution in terms of
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blocks of matrix rows It is easy to write down the algorithm and code it for square matrices where the matrix order is evenly divided by the number of PEs But what if the matrix is not square, or what if the number of rows is much greater than the number of columns and neither number is evenly divided by the number of PEs This requires significant changes to the transpose algorithm For a rectangular matrix, for example, the buffer that will hold the matrix block will need to be large enough to hold the larger of the two blocks If either the row or column dimension of the matrix is not evenly divisible by the number of PEs, then the blocks will not be the same size on each PE Can the algorithm deal with the uneven load that will result from having different block sizes on each PE Efficiency The program should effectively utilize the available computing resources The rest of this section gives a partial list of important factors to check Note that typically it is not possible to simultaneously optimize all of these factors; design tradeoffs are inevitable
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Can the computational load be evenly balanced among the PEs This is easier if the tasks are independent, or if they are roughly the same size Is the overhead minimized Overhead can come from several sources, including creation and scheduling of the UEs, communication, and synchronization Creation and scheduling of UEs involves overhead, so each UE needs to have enough work to do to justify this overhead On the other hand, more UEs allow for better load balance Communication can also be a source of significant overhead, particularly on distributed memory platforms that depend on message passing As we discussed in Section 26, the time to transfer a message has two components: latency cost arising from operating system overhead and message start up costs on the network, and a cost that scales with the length of the message To minimize the latency costs, the number of messages to be sent should be kept to a minimum In other words, a small number of large messages is better than a large number of small ones The second term is related to the bandwidth of the network These costs can sometimes be hidden by overlapping communication with computation On shared memory machines, synchronization is a major source of overhead When data is shared between UEs, dependencies arise requiring one task to wait for another to avoid race conditions The synchronization mechanisms used to control this waiting are expensive compared to many operations carried out by a UE Furthermore, some synchronization constructs generate significant memory traffic as they flush caches, buffers, and other system resources to make sure UEs see a consistent view of memory This extra memory traffic can interfere with the explicit data movement within a computation Synchronization overhead can be reduced by keeping data well localized to a task, thereby minimizing the frequency of synchronization operations
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Simplicity To paraphrase Einstein: Make it as simple as possible, but not simpler Keep in mind that practically all programs will eventually need to be debugged, maintained, and often enhanced and ported A design even a generally superior design is not valuable if it is too hard to debug, maintain, and verify the correctness of the final program The medical imaging example initially described in Sec 313 and then discussed further in the Task Decomposition and Data Decomposition patterns is an excellent case in point in support of the value
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of simplicity In this problem, a large database could be decomposed, but this decomposition would force the parallel algorithm to include complex operations for passing trajectories between UEs and to distribute chunks of the database This complexity makes the resulting program much more difficult to understand and greatly complicates debugging The other approach, replicating the database, leads to a vastly simpler parallel program in which completely independent tasks can be passed out to multiple workers as they are read All complex communication thus goes away, and the parallel part of the program is trivial to debug and reason about
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