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This is a private supporting method used by some of the other methods to move the le position pointer to the rst byte of the record at the given index position We begin by checking to see whether the given index is in range We do this by seeking to the end of the le (byte offset of 0 from the end), and using the tell() method to retrieve the byte position we have seeked to If the record s offset (index position real record size) is at or after the end then the index is out of range and we raise a suitable exception Otherwise, we seek to the offset position ready for the next read or write

def __delitem__(self, index): self__seek_to_index(index) state = self__fhread(1) if state != _OKAY: return self__fhseek(index * self__record_size) self__fhwrite(_DELETED) if selfauto_flush: self__fhflush()

First we move the le position pointer to the right place If the index is in range (ie, if no IndexError exception has occurred), and providing the record isn t blank or already deleted, we delete the record by overwriting its state byte with _DELETED

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Problem The interactions between the various UEs cause most of the problems when writing correct and efficient parallel programs How can programmers structure their parallel programs to make these interactions more manageable and easier to integrate with the core computations Context A parallel program takes complexity to a new level There are all the normal challenges of writing any program On top of those challenges, the programmer must manage multiple tasks running on multiple UEs In addition, these tasks and UEs interact, either through exchange of messages or by sharing memory In spite of these complexities, the program must be correct, and the interactions must be well orchestrated if excess overhead is to be avoided Fortunately, for most parallel algorithms, the operations carried out on each UE are similar The data might be different between UEs, or slightly different computations might be needed on a subset of UEs (for example, handling boundary conditions in partial differential equation solvers), but for the most part each UE will carry out similar computations Hence, in many cases the tasks and their interactions can be made more manageable by bringing them all together into one source tree This way, the logic for the tasks is side by side with the logic for the interactions between tasks, thereby making it much easier to get them right This is the so called "Single Program, Multiple Data" (SPMD) approach It emerged as the dominant way to structure parallel programs early in the evolution of scalable computing, and programming environments, notably MPI, have been designed to support this approach[1]

Data format This includes any requirements that a given type of data have a specific length, type of character, and specific order of characters Examples include date and time formats, user name and password fields, and alphanumeric value constraints (eg, no letters in a Social Security Number field) Data relationships and integrity Relationships that require the association of data from one entity with another are business rules in a data model For example, all orders must be associated with a customer, or all outgoing shipments must have shipping details Another example is the requirement that multiple records be updated if a single piece of information is changed for example, updating the ship date of a shipment automatically updates similar fields in order summary tables

def undelete(self, index): self__seek_to_index(index) state = self__fhread(1) if state == _DELETED: self__fhseek(index * self__record_size) self__fhwrite(_OKAY) if selfauto_flush: self__fhflush() return True return False

This method begins by nding the record and reading its state byte If the record is deleted we overwrite the state byte with _OKAY and return True to the caller to indicate success; otherwise (for blank or nondeleted records), we return False

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