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disks as those on which that relation is stored Suppose that we choose processors P0 , P1 , , Pm , where m < n to sort the relation There are two steps involved in this operation: 1 Redistribute the tuples in the relation, using a range-partition strategy, so that all tuples that lie within the ith range are sent to processor Pi , which stores the relation temporarily on disk Di To implement range partitioning, in parallel every processor reads the tuples from its disk and sends the tuples to their destination processor Each processor P0 , P1 , , Pm also receives tuples belonging to its partition, and stores them locally This step requires disk I/O and communication overhead 2 Each of the processors sorts its partition of the relation locally, without interaction with the other processors Each processor executes the same operation namely, sorting on a different data set (Execution of the same operation in parallel on different sets of data is called data parallelism) The nal merge operation is trivial, because the range partitioning in the rst phase ensures that, for 1 i < j m, the key values in processor Pi are all less than the key values in Pj We must do range partitioning with a good range-partition vector, so that each partition will have approximately the same number of tuples Virtual processor partitioning can also be used to reduce skew

Parallel external sort merge is an alternative to range partitioning Suppose that a relation has already been partitioned among disks D0 , D1 , , Dn 1 (it does not matter how the relation has been partitioned) Parallel external sort merge then works this way: 1 Each processor Pi locally sorts the data on disk Di 2 The system then merges the sorted runs on each processor to get the nal sorted output The merging of the sorted runs in step 2 can be parallelized by this sequence of actions: 1 The system range-partitions the sorted partitions at each processor Pi (all by the same partition vector) across the processors P0 , P1 , , Pm 1 It sends the tuples in sorted order, so that each processor receives the tuples in sorted streams 2 Each processor Pi performs a merge on the streams as they are received, to get a single sorted run 3 The system concatenates the sorted runs on processors P0 , P1 , , Pm 1 to get the nal result

The McGraw Hill Companies, 2001

Despite this, we will see that the op amp will result in voltage gains at the output terminal c How does this work Two voltages are input to terminals a and b Their difference is then ampli ed and output at c, which is taken with referenc to ground Although we won t worry about the internal construction of an op amp, note that it consists of a set of resistors and dependent voltage

20

You can create titles that roll up from the bottom of the screen towards the top or that slide from the right side of the screen to the left (similar to a news ticker) Figure 7-7 shows a rolling title as it is being played in the viewing window the title scrolls up as it is slowly displayed

As described, this sequence of actions results in an interesting form of execution skew, since at rst every processor sends all blocks of partition 0 to P0 , then every processor sends all blocks of partition 1 to P1 , and so on Thus, while sending happens in parallel, receiving tuples becomes sequential: rst only P0 receives tuples, then only P1 receives tuples, and so on To avoid this problem, each processor repeatedly sends a block of data to each partition In other words, each processor sends the rst block of every partition, then sends the second block of every partition, and so on As a result, all processors receive data in parallel Some machines, such as the Teradata DBC series machines, use specialized hardware to perform merging The Y-net interconnection network in the Teradata DBC machines can merge output from multiple processors to give a single sorted output

The McGraw Hill Companies, 2001

source The internal voltage source is related to the input voltages by A(V+ V ) (93)

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