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This problem was described in Sec 313, and we discussed its decomposition in the Task Decomposition and Data Decomposition patterns In the Group Tasks pattern, we described how to organize the tasks for this problem in the following groups:
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A group of tasks to find the "bonded forces" (vibrational forces and rotational forces) on each atom A group of tasks to find the nonbonded forces on each atom A group of tasks to update the position and velocity of each atom A task to update the neighbor list for all the atoms (which trivially constitutes a task group)
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Now we are ready to consider ordering constraints between the groups Clearly, the update of the atomic positions cannot occur until the force computation is complete Also, the nonbonded forces cannot be computed until the neighbor list is updated So in each time step, the groups must be ordered as shown in Fig 34
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Figure 34 Ordering of tasks in molecular dynamics problem
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While it is too early in the design to consider in detail how these ordering constraints will be enforced, eventually we will need to provide some sort of synchronization to ensure that they are strictly followed
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36 THE DATA SHARING PATTERN
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Problem Given a data and task decomposition for a problem, how is data shared among the tasks Context At a high level, every parallel algorithm consists of
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A collection of tasks that can execute concurrently (see the Task Decomposition pattern) A data decomposition corresponding to the collection of concurrent tasks (see the Data Decomposition pattern) Dependencies among the tasks that must be managed to permit safe concurrent execution
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As addressed in the Group Tasks and Order Tasks patterns, the starting point in a dependency analysis is to group tasks based on constraints among them and then determine what ordering constraints apply to groups of tasks The next step, discussed here, is to analyze how data is shared among groups of tasks, so that access to shared data can be managed correctly Although the analysis that led to the grouping of tasks and the ordering constraints among them focuses primarily on the task decomposition, at this stage of the dependency analysis, the focus shifts to the data decomposition, that is, the division of the problem's data into chunks that can be updated independently, each associated with one or more tasks that handle the update of that chunk This chunk of data is sometimes called task local data (or just local data), because it is tightly coupled to the task(s) responsible for its update It is rare, however, that each task can operate using only its own local data; data may need to be shared among tasks in many ways Two of the most common situations
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are the following
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In addition to task local data, the problem's data decomposition might define some data that must be shared among tasks; for example, the tasks might need to cooperatively update a large shared data structure Such data cannot be identified with any given task; it is inherently global to the problem This shared data is modified by multiple tasks and therefore serves as a source of dependencies among the tasks Data dependencies can also occur when one task needs access to some portion of another task's local data The classic example of this type of data dependency occurs in finite difference methods parallelized using a data decomposition, where each point in the problem space is updated using values from nearby points and therefore updates for one chunk of the decomposition require values from the boundaries of neighboring chunks
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This pattern discusses data sharing in parallel algorithms and how to deal with typical forms of shared data Forces The goal of this pattern is to identify what data is shared among groups of tasks and determine how to manage access to shared data in a way that is both correct and efficient Data sharing can have major implications for both correctness and efficiency
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If the sharing is done incorrectly, a task may get invalid data due to a race condition; this happens often in shared address space environments, where a task can read from a memory location before the write of the expected data has completed Guaranteeing that shared data is ready for use can lead to excessive synchronization overhead For example, an ordering constraint can be enforced by putting barrier operations[1] before reads of shared data This can be unacceptably inefficient, however, especially in cases where only a small subset of the UEs are actually sharing the data A much better strategy is to use a combination of copying into local data or restructuring tasks to minimize the number of times shared data must be read
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A barrier is a synchronization construct that defines a point in a program that a group of UEs must all reach before any of them are allowed to proceed
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Another source of data sharing overhead is communication In some parallel systems, any access to shared data implies the passing of a message between UEs This problem can sometimes be mitigated by overlapping communication and computation, but this isn't always possible Frequently, a better choice is to structure the algorithm and tasks so that the amount of shared data to communicate is minimized Another approach is to give each UE its own copy of the shared data; this requires some care to be sure that the copies are kept consistent in value but can be more efficient The goal, therefore, is to manage shared data enough to ensure correctness but not so much as to interfere with efficiency
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Solution The first step is to identify data that is shared among tasks This is most obvious when the decomposition is predominantly a data based decomposition For example, in a finite difference problem, the basic data is decomposed into blocks The nature of the decomposition dictates that the data at the edges of the blocks is shared between neighboring blocks In essence, the data sharing was worked out when the basic decomposition was done In a decomposition that is predominantly task based, the situation is more complex At some point in the definition of tasks, it was determined how data is passed into or out of the task and whether any data is updated in the body of the task These are the sources of potential data sharing After the shared data has been identified, it needs to be analyzed to see how it is used Shared data falls into one of the following three categories
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Read only The data is read but not written Because it is not modified, access to these values does not need to be protected On some distributed memory systems, it is worthwhile to replicate the read only data so each unit of execution has its own copy Effectively local The data is partitioned into subsets, each of which is accessed (for read or write) by only one of the tasks (An example of this would be an array shared among tasks in such a way that its elements are effectively partitioned into sets of task local data) This case provides some options for handling the dependencies If the subsets can be accessed independently (as would normally be the case with, say, array elements, but not necessarily with list elements), then it is not necessary to worry about protecting access to this data On distributed memory systems, such data would usually be distributed among UEs, with each UE having only the data needed by its tasks If necessary, the data can be recombined into a single data structure at the end of the computation Read write The data is both read and written and is accessed by more than one task This is the general case, and includes arbitrarily complicated situations in which data is read from and written to by any number of tasks It is the most difficult to deal with, because any access to the data (read or write) must be protected with some type of exclusive access mechanism (locks, semaphores, etc), which can be very expensive Accumulate The data is being used to accumulate a result (for example, when computing a reduction) For each location in the shared data, the values are updated by multiple tasks, with the update taking place through some sort of associative accumulation operation The most common accumulation operations are sum, minimum, and maximum, but any associative operation on pairs of operands can be used For such data, each task (or, usually, each UE) has a separate copy; the accumulations occur into these local copies, which are then accumulated into a single global copy as a final step at the end of the accumulation Multiple read/single write The data is read by multiple tasks (all of which need its initial value), but modified by only one task (which can read and write its value arbitrarily often) Such variables occur frequently in algorithms based on data decompositions For data of this type, at least two copies are needed, one to preserve the initial value and one to be used by the
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Two special cases of read write data are common enough to deserve special mention:
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modifying task; the copy containing the initial value can be discarded when no longer needed On distributed memory systems, typically a copy is created for each task needing access (read or write) to the data Examples
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